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39 Information Systems Dissertation Topics Ideas

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As the name depicts, information systems dissertation topics revolve around the information technology sphere of organizations and industries. Information systems research topics include both primary as well as secondary levels of research studies and their complexities differ in accordance with the academic and degree levels at hand.

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Best Information Systems Dissertation Topics Ideas for College Students

Given below is an extensive and enriched list of information systems thesis topics for our clients so that they go through the list and find something as per their interest and priority:

  • A historical analysis of information systems management: focus on the past three decades.
  • The role played by leadership, alignment, and planning in the domain of information systems management.
  • Research in information systems management: focus on post-COVID time period.
  • International information systems management: potential challenges and risks involved.
  • Information policy and international information systems management: a systematic analysis.
  • Information systems management and global operations: a review of the literature.
  • Importance of case studies and integrated projects in teaching information systems management.
  • A comparative analysis of practitioners and academicians in the field of information systems management.
  • How information technology supports businesses: the role played by information systems management.
  • Information systems management practices: a descriptive analysis.
  • Information systems management and the public sector: focus on the key issues.
  • Utilization of consumer internet data: ethics in information systems management.
  • Software development: groupware and problem-solving in a correlational analysis.
  • Research in the field of information systems management: focus on new innovations and ideas.
  • Cognition digital twins for personalized information systems of smart cities: Proof of concept
  • Information management systems: comparing private and public organizations in country X.
  • Machine learning-based diagnosis of diseases using the unfolded EEG spectra: toward an intelligent software sensor.
  • Relationship between information systems management and risk management systems: a comparative analysis.
  • Judging the IT department performance in an organization through information systems management.
  • Information systems management graduate school curriculums: a descriptive study.
  • Relationship between organizational learning and information systems management: a systematic analysis.
  • Quality management in the domain of information systems: a descriptive analysis.
  • Management of big data in developing countries of the world: a review of the literature.
  • Strategic information systems management: focus on the role of a balanced scorecard.
  • Delivery of information system: formation of a hypothetical framework.
  • Information quality management framework: a review of the literature.
  • Information systems hierarchy: a systematic analysis.
  • Importance of big data and business intelligence for the sustainable development in organizations: a UK-based approach.
  • Correlation between information systems management and risk management infrastructure to attain business risk resilience.
  • Effects of COVID-19 pandemic on the information systems management of X country.
  • Role of structured versus unstructured data in the domain of information systems management.
  • Business intelligence and information systems management: a review of the literature.
  • Effects of information systems on organizational performance: pre and post COVID analysis.
  • The Determinants of management information systems effectiveness in small-and medium-sized enterprises.
  • IT governance implementation and information systems management.
  • IS strategic planning and management services: a descriptive review.
  • Information system security at international levels: a review of the literature.
  • Developing a hypothetical model for measuring quality in information systems management.
  • The effects of information systems compatibility on firm performance following mergers and acquisitions
  • Implications of Knowledge Organization Systems for Health Information Exchange and Communication during the COVID-19 Pandemic

Above is the best list of  Information Systems Dissertation Topics, If you are still looking for some unique information systems dissertation topics fill out the form below and get the topic mini proposal on your requirements.

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GW School of Business

Ph.D. in Business with an Information Systems and Technology Management Area of Focus

The Department of Information Systems and Technology Management at the GW School of Business offers a Ph.D. in Business with an Information Systems and Technology Management (ISTM) Area of Focus. The ISTM doctoral program focuses on the research areas in information systems and technology management. The program is designed to explore diverse research topics in information systems, including digital transformation, IS economics, digital strategy, e-commerce, artificial intelligence (AI), and other emerging digital phenomena in today’s pervasively digitized businesses, and exploits them for business values. The proposed ISTM Ph.D. degree is intended to prepare students for successful research careers in the IS fields.  

At the same time, the ISTM field itself is highly interdisciplinary encompassing various research areas and topics studying IS and IT, including economics, sociology, psychology, organization theory and behavior, strategic management and computer science. ISTM faculty members have been actively doing interdisciplinary research in IS economics, digital marketing, digital business strategy, and IS theories in psychology and organizations, and doing professional service as associative editors at top-tier journals such as MIS Quarterly and Information Systems Research. The ISTM Ph.D. concentration is intended to prepare students for successful research careers in the IS specialization as well as encouraging cross-disciplinary research inquiries.  

The research phase begins as early as the first year, when students serve as research assistants, and continues throughout the doctoral program. Students should gradually become more involved in the design and execution of research and, by the end of the second year, have typically produced at least one paper suitable for publication, typically coauthored with a faculty member. The later years of the program are dedicated to original research and the creation of the dissertation.

ISTM faculty are dedicated to providing our students with leading edge information systems management, artificial intelligence, technology innovation courses, and research designed to make an impact in the business world.  

Our top ranked research faculty’s broad, multidisciplinary approach to the study and research of information systems core concepts, trends and forecasts leverages information technology and exploits them for business values. 

The ISTM department is ranked #24 world-wide in research productivity measured by the number of articles published in the top information systems journals such as Information Systems Research and MIS Quarterly in the past three years, 2020-2022. The research and expertise of the faculty of the Information Systems and Technology Management Department spans a broad theoretical base, including information systems, engineering, computer science and healthcare. 

Our Faculty

Our faculty earned their doctoral degrees from renowned schools, including the Massachusetts Institute of Technology (MIT), University of Texas at Austin, University of Southern California (USC), University of Washington, Erasmus University, Korea Institute of Science and Technology (KAIST), and the City University of New York (CUNY).  

ISTM department faculty have published in top tier journals, and work closely with faculty and doctoral students all over the world, leading to co-authored published articles. Our faculty are active in the discipline with four faculty members serving as Associate Editors for two top information systems journals , Information Systems Research and MIS Quarterly . They also serve as officers for academic divisions and track chairs for major conferences including the International Conference on Information Systems (ICIS), Conference on Information Systems and Technology (CIST), INFORMS, and the Americas Conference on Information Systems (AMCIS).  

YoungKi Park

Dr. YoungKi Park is the lead professor for the ISTM doctoral program.

  • Elias Carayannis
  • Sunghun Chung Ph.D. in Information Systems, KAIST Areas of Expertise: FinTech, Digital Inclusion/Innovation, Social Media Analytics, IT Business Value 
  • Subhasish Dasgupta Ph.D. in Information Systems, City University of New York (CUNY)  Areas of expertise: Information Technology Adoption and Diffusion, Behavioral Issues in Information Systems, Global Information Systems
  • Richard Donnelly Ph.D., Massachusetts Institute of Technology 
  • Wenjing Duan Ph.D. in Management Science and Information Systems, University of Texas at Austin Areas of Expertise: Electronic Commerce; Social Media; Digital Marketing; Healthcare Analytics 
  • Yi-Chun (Chad) Ho Ph.D. in Information Systems, University of Washington Areas of Expertise: Platform/Sharing Economy, User-Generated Content, E-/M-Commerce, Pricing in Digital Markets Topical Areas of Interest: Live Streaming Commerce, Influencer Marketing, Crowdfunding 
  • Yixin Lu PhD: Rotterdam School of Management, Erasmus University Research Interests: Artificial Intelligence, Healthcare Analytics, Market Design, Programmatic Advertising, Platform Strategies, Sustainable Operations 
  • YoungKi Park Ph.D. in Information Systems, Marshall School of Business, University of Southern California Areas of Expertise: Digital Business Strategy, Business Intelligence & Analytics, Enterprise Information Systems & Database, Set-Theoretic Configuration Methods, Fuzzy-Set Qualitative Comparative Analysis (fsQCA)

Program & Curriculum

The program is designed to provide doctoral students with solid theoretical and methodological foundations to pursue the key topics of information and digital systems, virtual environments, digitization, and emerging digital technologies. The ISTM Ph.D. degree consists of fifteen courses (equivalent of forty-five credit hours) as part of the student's plan of study. These include:  

  • Four required courses in research methods and quantitative analysis (12 credits)  
  • Four required ISTM field core doctoral courses (12 credits)  
  • Five doctoral level elective courses (15 credits)  
  • An additional doctoral level seminar from outside the student's area of interest (3 credits). 
  • A summer research paper during the first or first and second summer (3 credits).  

Throughout the program, students become familiar with research methods and the literature of their major fields through research projects and directed readings. The completion of course work and qualifying examinations requires no more than two years.  

Early student participation in research is strongly encouraged. The ISTM department hosts regular seminar series, which introduce students to cutting-edge research and provide a forum for faculty-student interaction. Students are encouraged to work with their counterparts at other schools and departments including the Marketing, Management, and Strategic Management & Public Policy Departments, as well as the university's Computer Sciences Department and the Milken School of Public Health, among others. 

Students should take at least 15 (three-credits) courses to meet the required 45 credit hours for the PhD degree. The ISTM Ph.D. Program requires students to take a minimum of 45 credits during their program, including 27 credits in core courses, a 3-credit summer paper, and 15 credits in elective courses. Students should consult their Faculty Advisory Group about the required courses and electives for which they should register. All classes are required to be taken for letter grades. The program course requirements are provided below followed by an overview of the program structure.

Common Methodology Courses (4) 

  • ECON 8301 Microeconomic Theory I
  • ECON 8375: Econometrics I
  • ECON 8376: Econometrics II
  • MGT 8385: Research Methods and Design

Core Courses (4) 

Students choose four out of the five following ISTM seminar courses with the student’s faculty advisory group:

  • ISTM 8391: Contemporary Research Topics in Information Systems
  • ISTM 8392: Empirical Research in Information Systems – A Cross-Disciplinary Overview
  • ISTM 8393: Behavioral Theories in Information Systems Research
  • ISTM 8394: Advanced Empirical Methods in Information Systems
  • ISTM 8395: Research in Digital Business Strategy

Students must also complete:

  • An additional doctoral level seminar from outside the student's area of interest
  • A summer research paper during the first or first and second summer, as required by the student's area of interest

Elective Courses (5) 

Students choose five of the following courses with the student’s faculty advisory group:

  • DNSC 8328: Bayesian Statistics  
  • DNSC 8393: Applied Stochastic Models for Business 
  • ECON 8302: Microeconomic Theory II  
  • ECON 8303: Microeconomic Theory III  
  • ECON 8375: Econometrics III  
  • ECON 8379: Applied Econometrics  
  • EDUC 8122: Qualitative Research Methods 
  • MGT 8382: Foundations of Organizational Behavior and Development 
  • MGT 8397: Doctoral Seminar 
  • PSYC 8202: Psychological Research Methods and Procedures 
  • PSYC 8205: Multiple Regression Analysis in Psychological and Social Sciences 
  • PSYC 8206: Multivariate Analysis and Modeling 
  • PSYC 8291: Theories of Organizational Behavior 
  • SMPP 8321: Seminar in Strategic Management 
  • SMPP 8331: Organizational Theory  
  • STAT 8273: Stochastic Processes

In addition to the above electives, students can — with ISTM doctoral program approval — register for relevant doctoral courses offered by the other GWU Departments or at the other universities which are part of the Consortium of Universities of the Washington Metropolitan Area.   

  • Fall Semester: ECON 8301, ECON 8375, ISTM 8391, ISTM 8392 
  • Spring Semester: ECON 8376, MGT 8385, ISTM 8393, 1 elective 
  • Research Activities: Seeking research topics and advisors; regularly attending departmental and school seminars; start summer paper research
  • Milestone/Objective: Summer paper (due by the start of fall second year) 
  • Fall Semester: ISTM 8394, ISTM 8398, 1 elective 
  • Spring Semester: ISTM 8395, 2 electives  
  • Research Activities: Identification of major research topics; continuation of work with advisors; completion of second-year paper; presentation at conferences
  • Milestone/Objective: Comprehensive exam 
  • Fall Semester: Dissertation
  • Spring Semester: Dissertation
  • Research Activities: Conference presentations; premier journal submissions; start of dissertation proposal
  • Milestone/Objective: Teaching, Work on dissertation
  • Research Activities: Conference presentations; premier journal submissions
  • Milestone/Objective: Dissertation proposal defense
  • Research Activities: Premier journal submissions and publications; job market papers ready
  • Milestone/Objective: Dissertation defense, Research-oriented faculty position

Note: Please refer to the course requirements section for core courses and elective courses.

Admissions, Financial Aid & Next Steps

Admission to the program is competitive. All applicants must have a bachelor’s degree or the equivalent, representing a four-year course of study in an accredited college or university. Superior academic performance is expected, and a strong background and ability in mathematics are essential for successfully completing the program. The program is expected to be completed in four to five years.  

Learn about admissions and the costs of pursuing a Ph.D. in ISTM, and explore various resources available to help finance your degree.

Learn More About Doctoral Program Admissions, Financial Aid, and How to Apply

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Department of Technology, Operations, and Statistics | Doctoral Program in Information Systems

Doctoral program in information systems.

Authoritative curriculum information can be found exclusively in the University Bulletin. All other content, including this page, is for informational purpose only. You can find the curriculum for this program on this page of the Bulletin.

  • Overview of the Doctoral Program in Information Systems

Program Requirements

Doctoral Courses

  • Doctoral Students and their Research
  • Information Systems Faculty

Overview of the IS Doctoral Program

Mission: To educate and train scholars who will produce first-rate IS research and who will succeed as faculty members in first-rate universities. We offer tracks in technical perspectives on IS, economic perspectives on IS, and organizational/management perspectives on IS. Admissions and performance: We enroll an average of three students each year out of more than 100 highly qualified applicants. Students enrolling typically have GMATS over 700 or GREs over 1400. International students typically have TOEFLs higher than 640. Our students are highly competitive within Stern and nationally. Recently our students have received school-wide awards as "outstanding doctoral students." They have won acceptance at doctoral consortia sponsored by the Academy of Management and the International Conference on Information Systems. And they have won national dissertation research competitions.

Advising and evaluation: The IS doctoral program faculty director advises all first-year doctoral students. During the first year students have many opportunities to get to know the research interests of all departmental faculty. By the beginning of the second year, students have selected a concentration advisor who will guide them through the comprehensive exam process and up to the thesis stage. By the middle of the third year students will have selected a thesis advisor. Each year every student submits a statement of intellectual progress to his/her advisor. All faculty meet to review the progress of all students in a day-long meeting each year. At this time, the student's intellectual progress is reviewed and plans for the following year are considered. The results of this review include a formal letter to the student assessing the previous year's work and offering guidance for the following year's work. All students take a comprehensive written and oral exam at the end of the second year. Students defend their thesis proposal by March of their fourth year and defend their completed dissertation at the end of the fourth year or during the fifth year.

Research and interaction with faculty: The heart of the IS doctoral program is immersion in a community of researchers. Every student has a formal research apprenticeship with one or more faculty members each year. Every student participates in formal and informal research seminars each week with departmental faculty and visitors. Every student presents research in progress and works toward producing publishable papers, usually with a faculty co-author. Students learn to be researchers by doing research. They learn to be research colleagues by working with others and critiquing their research.

Placement record: In the past ten years, our graduates have accepted faculty positions at such schools as University of California at Berkeley, Hong Kong University of Science & Technology, University of Maryland, University of Minnesota, University of Texas at Austin, the University of British Columbia, National University of Singapore, The Wharton School and the University of Cambridge, UK.   Please click on the links on the right to learn how to apply, to attend an information session, and to contact the Stern School Doctoral Office. 

Natalia Levina Coordinator, Information Systems Doctoral Program IOMS Department

Back-to-Top

All students take a common core of courses during their first year which provides an overview of the major research areas in IS and the fundamental knowledge necessary for specialized course work in the second year. In the second year students take specialized course work in one of three concentrations: technical perspectives, economic perspectives, behavioral/managerial perspectives.  

Mandatory Breadth Courses (3)

  • Behavioral Research Methods
  • Micro-economics
  • Technical Foundations
  • Each student is required to take 1 Probability and 1 Statistics course, from a list of approved courses.
  • Technical Research in IS
  • Economics Research in IS
  • Behavioral/Managerial Research in IS
  • Research Apprenticeship

YEAR TWO - Each student chooses one concentration track

Technical Track:

  • A programming requirement, may be satisfied in a variety of ways
  • Honors Analysis of Algorithms
  • Artificial Intelligence
  • Optimization
  • Database Systems
  • Machine Learning/Data Mining
  • Other courses based on student's interest
  • Research apprenticeship

Economics Track:

  • Mathematical Methods for Economists
  • Econometrics
  • Game Theory
  • Students will take elective courses in the Stern Economics Department, at the Graduate School of Arts and Sciences, in Operations Management, Statistics, or at Courant as specified in consultation with the advisor

Behavioral/Managerial Track:

  • Any two of the following four Stern Management Department Courses
  • Organizational Behavior
  • Managerial Cognition
  • Organizational Theory
  • At least one research methods or statistics course beyond the first year courses.
  • Students may take doctoral level courses in Psychology, Sociology, Political Science, Public Policy, History, Education, or Law.
  • Electives in the area of interest
  • Thesis research
  • Teaching apprenticeship (in year 3 or 4)
  • Teaching one course (in year 3 or 4)
  • INFO-GB.3345 (B20.3345)  Doctoral Seminar in Digital Economics  (offered in Spr 2012) This course introduces students to scientific paradigms and research perspectives related to the economics of information technologies. Topics in 2012 include information goods, piracy, digital rights management, network economics, sponsored search auctions, user-generated content, contagion in networks, technological innovation, IT productivity, the digital commons and online privacy.  
  • INFO-GB.3382 (B20.3382)  Research Seminar on IT and Organizations: Social Perspectives (offered in Spr 2012) The course introduces students to sociological and organizational literature on the role of Information Technology in organizations and society.  
  • INFO-GB.3383 (B20.3383)  Networks, Crowds & Markets   
  • INFO-GB.3386 (B20.3386)  Technical Foundations of IS  
  • INFO-GB.3355 (B20.3355)  Behavioral Research Methods  
  • INFO-GB.3391 (B20.3391)  Research Seminar in Data Science   (offered in Spr 2012) In this course we will take a deep dive into selected topics in data science. The focus will be two-fold. First, we will read textbook segments, classic papers, and new research, with the goal of understanding research in data science. Second, we will study the actual practical application of data science methods to extract knowledge from large-scale data. We will cover topics such as machine learning, data mining, information retrieval, text classification, sentiment analysis, similarity analysis, network analysis, graphical models, Bayesian models, topic models, model evaluation, crowd-sourcing and micro-outsourcing, massive-scale data processing, reducing data for analytic purposes, and more. The selection of which topics are covered in a particular semester will be based on: (i) the current research and business environments, (ii) the research interests of the IS faculty, and (iii) the interests of the students in that semester. We also will discuss applications that are of current interest, such as recommender systems, social-network marketing, online advertising, Mechanical Turking, and more.

IS PhD

Questions about the PhD Program in Information Systems?

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Information Systems Research Topics for MS PhD

Information systems research topic ideas for ms, or ph.d. degree.

I am sharing with you some of the research topics regarding Information Systems that you can choose for your research proposal for the thesis work of MS, or Ph.D. Degree.

  • A survey on blockchain for information systems management and security
  • The perils and promises of big data research in information systems
  • A novel Dual-Blockchained structure for contract-theoretic LoRa-based information systems
  • A novel decision-making approach based on three-way decisions in fuzzy information systems
  • Review and critique of the information systems development project failure literature: An argument for exploring information systems development project distress
  • Product decision-making information systems, real-time big data analytics, and deep learning-enabled smart process planning in sustainable industry 4.0
  • Blockchain-based privacy-preserving remote data integrity checking scheme for IoT information systems
  • Human resource information systems
  • The role of three-dimensional geographic information systems in subsurface characterization for hydrogeological applications
  • Cognition digital twins for personalized information systems of smart cities: Proof of concept
  • Trust in Management Information Systems (MIS) A Theoretical Model
  • Exploring the characteristics and utilisation of Farm Management Information Systems (FMIS) in Germany
  • Managing risk in information systems
  • Data science: developing theoretical contributions in information systems via text analytics
  • The Determinants of management information systems effectiveness in small-and medium-sized enterprises
  • Accounting information systems: controls and processes
  • Weaponizing information systems for political disruption: The actor, lever, effects, and response taxonomy (ALERT)
  • A systematic review of social media acceptance from the perspective of educational and information systems theories and models
  • End-user participation in health information systems (HIS) development: Physicians’ and nurses’ experiences
  • Financial Management Information Systems and accounting policies retention in Brazil
  • A common attribute reduction form for information systems
  • … quality factors matter in enhancing the perceived benefits of online health information sites? Application of the updated DeLone and McLean Information Systems …
  • Geographic information systems (GIS) for disaster management
  • Enabling supply chain analytics for enterprise information systems: a topic modelling literature review and future research agenda
  • A Systematic Review of Empirical Affordance studies: Recommendations for Affordance Research in Information Systems.
  • Applying Team-Based Learning in Online Introductory Information Systems Courses
  • Complexity and Information Systems Rsearch in the Emerging Digital World
  • Public health informatics and information systems
  • Analyzing the location of city logistics centers in Istanbul by integrating Geographic Information Systems with Binary Particle Swarm Optimization algorithm
  • Understanding the challenges associated with the use of data from routine health information systems in low-and middle-income countries: A systematic review
  • Integration of the Dimensions of Computerized Health Information Systems and Their Role in Improving Administrative Performance in Al-Shifa Medical Complex
  • Accounting information systems in the blockchain era
  • Continuous transition from model-driven prototype to full-size real-world enterprise information systems
  • On the Use of Qualitative Comparative Analysis in Information Systems Research-A Critical Review
  • Implementation of business intelligence considering the role of information systems integration and enterprise resource planning
  • Utilizing chemometrics and geographical information systems to evaluate spatial and temporal variations of coastal water quality
  • Optimizing data quality of pharmaceutical information systems in public health care in resource limited settings
  • The impact of Public Sector Scorecard adoption on the effectiveness of accounting information systems towards the sustainable performance in Public Sector
  • The role of information systems in decision-making and public policy making
  • Analysis of barriers to the deployment of health information systems: A stakeholder perspective
  • Reengineering of Information Systems toward Classical-Quantum Systems.
  • Safe use of hospital information systems: an evaluation model based on a sociotechnical perspective
  • Organizational information security management for sustainable information systems: An unethical employee information security behavior perspective
  • Organization of a virtual enterprise in information systems
  • Twenty‐five years of the Information Systems Journal: A bibliometric and ontological overview
  • Green Information Systems Refraction for Corporate Ecological Responsibility Reflection in ICT Based Firms: Explicating Technology Organization Environment …
  • A Data Analytics Framework for Smart Asthma Management Based on Remote Health Information Systems with Bluetooth-Enabled Personal Inhalers.
  • Utilisation of hospital information systems for medical research in Saudi Arabia: A mixed-method exploration of the views of healthcare and IT professionals involved in …
  • Unconstrained design: Improving multitasking with in-vehicle information systems through enhanced situation awareness
  • The architecture of computer hardware, systems software, and networking: An information technology approach
  • Covid-19 pandemic and suicide in France: An opportunity to improve information systems
  • Information Technology and Systems: Proceedings of ICITS 2020
  • Risk Management in Information Technology
  • The effects of information systems compatibility on firm performance following mergers and acquisitions
  • Information Systems Students’ Impressions on Learning Modeling Enterprise Architectures
  • Drivers of intentions to use healthcare information systems among health and care professionals
  • Vulnerability and protection of business management systems: threats and challenges
  • Development of algorithm for analysis of sound fragments in medical information systems
  • A framework for validating information systems research based on a pluralist account of truth and correctness
  • Use of ontology learning in information system integration: a literature survey
  • Conceptmap: A conceptual approach for formulating user preferences in large information spaces
  • An Analysis of Point of Sales (POS) Information Systems in SMEswith The Black Box Testing and PIECES Method
  • Design principles for the General Data Protection Regulation (GDPR): A formal concept analysis and its evaluation
  • Virtually in this together–how web-conferencing systems enabled a new virtual togetherness during the COVID-19 crisis
  • Roadmap to strengthen global mental health systems to tackle the impact of the COVID-19 pandemic
  • Information and Communication Infrastructures in Modern Wide-Area Systems
  • Cyber-Physical Systems: a multi-criteria assessment for Internet-of-Things (IoT) systems
  • On the declarative paradigm in hybrid business process representations: A conceptual framework and a systematic literature study
  • Compact and high-performance vortex mode sorter for multi-dimensional multiplexed fiber communication systems
  • A modeling method for systematic architecture reconstruction of microservice-based software systems
  • Decision support systems for agriculture 4.0: Survey and challenges
  • Blockchain adoption from an interorganizational systems perspective–a mixed-methods approach
  • Exploiting chemistry and molecular systems for quantum information science
  • Information Technology Governance: Reflections on the Past and Future Directions
  • The price of fairness-A framework to explore trade-offs in algorithmic fairness
  • Shared Ledger Accounting—Implementing the Economic Exchange Pattern
  • Implications of Knowledge Organization Systems for Health Information Exchange and Communication during the COVID-19 Pandemic
  • Using secondary data to tell a new story: A cautionary tale in health information technology research
  • Combining symbiotic simulation systems with enterprise data storage systems for real-time decision-making
  • Integration of new information in memory: new insights from a complementary learning systems perspective
  • What drives unverified information sharing and cyberchondria during the COVID-19 pandemic?
  • The rise of human machines: How cognitive computing systems challenge assumptions of user-system interaction
  • Community-diversified influence maximization in social networks
  • Electronic religious programs on islamic subjects on the example of the sanctuary of Al-Hakim Al-Termizi
  • An incremental attribute reduction approach based on knowledge granularity for incomplete decision systems
  • Unpacking the difference between digital transformation and IT-enabled organizational transformation
  • Information scrambling at finite temperature in local quantum systems
  • Auditing cloud-based blockchain accounting systems
  • Eye-tracking-based classification of information search behavior using machine learning: evidence from experiments in physical shops and virtual reality shopping …
  • Uncertain information and linear systems
  • Maritime reporting systems
  • Evaluating E-learning systems success: An empirical study
  • Information freshness in cache updating systems
  • Business models shifts: Impact of Covid-19
  • A trustworthiness-based vehicular recruitment scheme for information collections in distributed networked systems
  • Medical information retrieval systems for e-Health care records using fuzzy based machine learning model
  • On the reliability of test collections for evaluating systems of different types
  • Global health crises are also information crises: A call to action
  • Review of compact computational spectral information acquisition systems
  • A pre-filtering approach for incorporating contextual information into deep learning based recommender systems
  • Mapping county-level mobility pattern changes in the United States in response to COVID-19
  • Banana Classification Using Deep Learning
  • Information technology elements for optical systems of identification of autonomous underwater vehicles
  • Time-efficient target tags information collection in large-scale RFID systems
  • Blockchain and the united nations sustainable development goals: towards an agenda for is research
  • Infrastructural sovereignty over agreement and transaction data (‘metadata’) in an open network-model for multilateral sharing of sensitive data
  • Are high-performing health systems resilient against the COVID-19 epidemic?
  • The search for smartness in working, living and organising: beyond the ‘Technomagic’
  • Covert communications without channel state information at receiver in IoT systems
  • Hypertext: from text to expertext
  • JSON: Data model and query languages
  • Introduction to ultra-wideband radar systems
  • Machine learning based diagnosis of diseases using the unfolded EEG spectra: towards an intelligent software sensor
  • A review of research relevant to the emerging industry trends: Industry 4.0, IoT, blockchain, and business analytics
  • Principles of construction of systems for diagnosing the energy equipment
  • Editorial reflections: Lockdowns, slow downs, and some introductions
  • Containing COVID-19 through physical distancing: the impact of real-time crowding information
  • Evaluating content novelty in recommender systems
  • Smart city model based on systems theory
  • Type of Grapefruit Classification Using Deep Learning
  • Information technology audit quality: an investigation of the impact of individual and organizational factors
  • Directions for professional social matching systems
  • Conceptual approach to building a digital twin of the production system
  • New information technologies in the estimation of stationary modes of the third type systems
  • RF systems design for simultaneous wireless information and power transfer (SWIPT) in automation and transportation
  • Capturing the complexity of gamification elements: a holistic approach for analysing existing and deriving novel gamification designs
  • Optimal site selection for solar photovoltaic (PV) power plants using GIS and AHP: A case study of Malatya Province, Turkey
  • Mining association rules for anomaly detection in dynamic process runtime behavior and explaining the root cause to users
  • Eight grand challenges in socio-environmental systems modeling
  • Future integrated mobility-energy systems: A modeling perspective
  • A novel framework to evaluate innovation value proposition for smart product–service systems
  • Diagnostic Systems For Energy Equipments
  • Utilising neutrosophic theory to solve transition difficulties of IoT-based enterprises
  • Towards smart farming: Systems, frameworks and exploitation of multiple sources
  • Measuring Resilience of Human–Spatial Systems to Disasters: Framework Combining Spatial-Network Analysis and Fisher Information
  • Age of information for multicast transmission with fixed and random deadlines in IoT systems
  • The role of personality and linguistic patterns in discriminating between fake news spreaders and fact checkers
  • Mapping the incidence of the COVID-19 hotspot in Iran–Implications for Travellers
  • How farmers shape cultural landscapes. Dealing with information in farm systems (Vallès County, Catalonia, 1860)
  • An overview of clinical decision support systems: benefits, risks, and strategies for success
  • Will the COVID-19 pandemic change waste generation and composition?: The need for more real-time waste management data and systems thinking
  • Digital Systems and New Challenges of Financial Management–FinTech, XBRL, Blockchain and Cryptocurrencies
  • Identity asymmetries: An experimental investigation of social identity and information exchange in multiteam systems
  • How enterprises adopt agile forms of organizational design: a multiple-case study
  • Acceptance of text-mining systems: The signaling role of information quality
  • Improving health care management in hospitals through a productivity dashboard
  • The construction of smart city information system based on the Internet of Things and cloud computing
  • Foundations of cryptoeconomic systems
  • Information distribution in multi-robot systems: Utility-based evaluation model
  • Dew computing architecture for cyber-physical systems and IoT
  • The port as a set of socio-technical systems: A multi-organisational view
  • IOS drivers of manufacturer-supplier flexibility and manufacturer agility
  • A secure authenticated and key exchange scheme for fog computing
  • Information and communication technologies in tourism
  • The effect of customer lifestyle patterns on the use of mobile banking applications in Jordan
  • Human identification for activities of daily living: A deep transfer learning approach
  • The geography of transport systems
  • Virtual reality
  • Technical provision of diagnostic systems
  • The contribution of systems science to Industry 4.0
  • Peers matter: The moderating role of social influence on information security policy compliance
  • On the age of information in internet of things systems with correlated devices
  • A survey on knowledge graph-based recommender systems
  • How corporate social responsibility activities influence employer reputation: The role of social media capability
  • A dual systems model of online impulse buying
  • Explanatory and predictive model of the adoption of P2P payment systems
  • Public Health Informatics: An Introduction
  • Principles of ties of internal control and management accounting systems at the enterprises of black metallurgy
  • Pivot-based approximate k-NN similarity joins for big high-dimensional data
  • Classifying nuts types using convolutional neural network
  • Enabling the analysis of personality aspects in recommender systems
  • Transparency and accountability in AI decision support: Explaining and visualizing convolutional neural networks for text information
  • Cornac: A Comparative Framework for Multimodal Recommender Systems
  • Trust information network in social Internet of things using trust-aware recommender systems
  • The Role of KM in Enhancing AI Algorithms and Systems
  • Towards a characterisation of smart systems: A systematic literature review
  • Impact of COVID-19 pandemic on information management research and practice: Transforming education, work and life
  • Output and regulated output synchronization of heterogeneous multi-agent systems: A scale-free protocol design using no information about communication network …
  • Secure lightweight password authenticated key exchange for heterogeneous wireless sensor networks
  • How voluntary information sharing systems form: Evidence from a us commercial credit bureau
  • Designing multistage search systems to support the information seeking process
  • Recent Advances in Flexible and Stretchable Sensing Systems: From the Perspective of System Integration
  • Integrating geospatial technologies and unmanned aircraft systems into the grower’s disease management toolbox
  • A development framework for decision support systems in high-performance sport
  • Information resource orchestration during the COVID-19 pandemic: A study of community lockdowns in China
  • IoT Data Management—Security Aspects of Information Linkage in IoT Systems
  • Task recommendation in crowdsourcing systems: A bibliometric analysis
  • Survey on various conversational systems
  • The Role Of Blockchain As A Security Support For Student Profiles In Technology Education Systems
  • Reliability bounds for multi-state systems by fusing multiple sources of imprecise information
  • On the design of output information-based sliding mode controllers for switched descriptor systems: Linear sliding variable approach
  • The pinar del río geography and connected photovoltaic systems to grid
  • Challenges and future directions of computational advertising measurement systems
  • Fisher information and Shannon entropy calculations for two-electron systems
  • TAMING COMPLEXITY IN SEARCH MATCHING: TWO-SIDED RECOMMENDER SYSTEMS ON DIGITAL PLATFORMS.
  • An extensive study on the evolution of context-aware personalized travel recommender systems
  • Clustering and self-organization in small-scale natural and artificial systems
  • Establishing smart service systems is a challenge: a case study on pitfalls and implications
  • Designing, developing, and deploying artificial intelligence systems: Lessons from and for the public sector
  • The negative skycube
  • Towards digital engineering: the advent of digital systems engineering
  • An Integrated model of continuous intention to use of google classroom
  • Port Community Systems: A structured literature review
  • Working towards a multimedia learning environment: experiences in the classroom
  • Large-scale question tagging via joint question-topic embedding learning
  • Skills, Certifications, or Degrees: What Companies Demand for Entry-Level Cybersecurity Jobs.
  • Geographic objects with indeterminate boundaries
  • Recent advances and challenges in task-oriented dialog systems
  • Industry 4.0 integration with socio-technical systems theory: A systematic review and proposed theoretical model
  • Diagnosis of arthritis using adaptive hierarchical Mamdani fuzzy type-1 expert system
  • Securing of Unmanned Aerial Systems (UAS) against security threats using human immune system
  • Cybernetic Approach to Developing Resilient Systems: Concept, Models and Application
  • Factors that affect accounting information system success and its implication on accounting information quality
  • Reconciliation of privacy with preventive cybersecurity: The bright internet approach
  • How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research
  • A survey on conversational recommender systems
  • A human-in-the-loop manufacturing control architecture for the next generation of production systems
  • Dynamic representations in networked neural systems
  • Cognitive twins for supporting decision-makings of Internet of Things systems
  • Challenges in building intelligent open-domain dialog systems
  • Organizational and environmental influences in the adoption of computer-assisted audit tools and techniques (CAATTs) by audit firms in Malaysia
  • Mutual clustering on comparative texts via heterogeneous information networks
  • Smart production systems drivers for business process management improvement
  • Real time dataset generation framework for intrusion detection systems in IoT
  • COVID-19 pandemic: Shifting digital transformation to a high-speed gear
  • Design Theory Indeterminacy: What is it, how can it be reduced, and why did the polar bear drown?
  • A cloud-based platform for the non-invasive management of coronary artery disease
  • A practical GIS-based hazard assessment framework for water quality in stormwater systems
  • An affordance perspective of team collaboration and enforced working from home during COVID-19
  • Health Information Systems, 2008
  • Applications and Datasets for Superpixel Techniques: A Survey
  • An ECDSA Approach to Access Control in Knowledge Management Systems Using Blockchain
  • What makes a review a reliable rating in recommender systems?
  • Revocation Mechanisms for Academic Certificates Stored on a Blockchain
  • Context-Aware Recommendations Based on Deep Learning Frameworks
  • Towards automating the synthesis of chatbots for conversational model query
  • A hierarchical model to evaluate the quality of web-based e-learning systems
  • Blockchain technology-enabled supply chain systems and supply chain performance: a resource-based view
  • Analyzing Cryptocurrencies
  • Estimation-action-reflection: Towards deep interaction between conversational and recommender systems
  • Recommender systems and their ethical challenges
  • Advanced Database systems
  • Towards predictive maintenance for flexible manufacturing using FIWARE
  • Automated continuous noninvasive ward monitoring: validation of measurement systems is the real challenge
  • Digital nomads
  • An Algorithm to Select an Energy-Efficient Sever for an Application Process in a Cluster of Servers
  • Evolution and revolution: Personality research for the coming world of robots, artificial intelligence, and autonomous systems
  • Considering random factors in modeling complex microeconomic systems
  • Discrete event-driven model predictive control for real-time work-in-process optimization in serial production systems
  • Challenges to transforming unconventional social media data into actionable knowledge for public health systems during disasters
  • An analysis of learners’ intentions toward virtual reality online learning systems: a case study in Taiwan
  • Techno-unreliability: a pilot study in the field
  • Learning relational fractals for deep knowledge graph embedding in online social networks
  • A novel approach towards using big data and IoT for improving the efficiency of m-health systems
  • Big data analytics for manufacturing internet of things: opportunities, challenges and enabling technologies
  • State machine based human-bot conversation model and services
  • Contributions of scale: what we stand to gain from Indigenous and local inclusion in climate and health monitoring and surveillance systems
  • Intelligent knowledge lakes: The age of artificial intelligence and big data
  • Learning management systems: a review of the research methodology literature in Australia and China
  • Performance Analysis of Machine Learning Algorithms for Cervical Cancer Detection
  • Privacy in Dynamical Systems
  • Prominence and engagement: Different mechanisms regulating continuance and contribution in online communities
  • Evaluation of views regarding pharmacy information management systems implementation and systemic issues in community pharmacies
  • A critical look at theories in design science research
  • What affects usage satisfaction in mobile payments? Modelling user generated content to develop the “digital service usage satisfaction model”
  • A survey on empathetic dialogue systems
  • Method of constructing explanations for recommender systems based on the temporal dynamics of user preferences
  • How Foreign and Domestic Firms Differ in Leveraging IT-enabled Supply Chain Information Integration in BOP Markets: The Role of Supplier and Client …
  • … trial of an information technology enhanced peer-integrated collaborative care intervention versus enhanced usual care for us trauma care systems: clinical …
  • From responsive to adaptive and interactive materials and materials systems: A roadmap
  • A multi-dimensional model of Enterprise Resource Planning critical success factors
  • A minimum centre distance rule activation method for extended belief rule-based classification systems
  • A fuzzy-based system for assessment of available edge computing resources in a cloud-fog-edge SDN-VANETs architecture
  • Advances in smart environment monitoring systems using iot and sensors
  • Understanding the apparent superiority of over-sampling through an analysis of local information for class-imbalanced data
  • About trust in the information systems on the basis of internet-based technologies
  • Cyber-physical production systems retrofitting in context of industry 4.0
  • Distributed maximum correntropy filtering for stochastic nonlinear systems under deception attacks
  • Adaptive rule adaptation in unstructured and dynamic environments
  • Systems thinking: A review and bibliometric analysis
  • Introduction to unmanned aircraft systems
  • Recent advances and opportunities for improving critical realism-based case study research in IS
  • Data analytics in higher education: an integrated view
  • Governance by Other Means: Rankings as Regulatory Systems
  • Codifying Interdisciplinary Design Knowledge through Patterns–The Case of Smart Personal Assistants
  • Critical factors in information technology capability for enhancing firm’s environmental performance: case of Indonesian ICT sector
  • A Model Management Platform for Industry 4.0–Enabling Management of Machine Learning Models in Manufacturing Environments
  • Compact polarizers for satellite information systems
  • Combining multicriteria decision analysis and GIS to assess vulnerability within a protected area: An objective methodology for managing complex and fragile systems
  • Contextualizing the effective use of social media network for collaborative learning: An affordance perspective
  • Explaining the link between technostress and technology addiction for social networking sites: A study of distraction as a coping behavior
  • Design of an Inclusive Financial Privacy Index (INF-PIE): A Financial Privacy and Digital Financial Inclusion Perspective
  • Applying a systematic literature review and content analysis method to analyse open source developers’ forking motivation interpretation, categories and …
  • Satellite communications systems: systems, techniques and technology
  • TOPSIS method for developing supplier selection with probabilistic linguistic information
  • The role of information technology in organization and management in tourism
  • Big data analytics in healthcare: a systematic literature review
  • Configuration Optimization and Channel Estimation in Hybrid Beamforming mmWave Systems With Channel Support Side Information
  • High-Capacity Robust Image Steganography via Adversarial Network.
  • Does Tailoring Gamified Educational Systems Matter? The Impact on Students’ Flow Experience
  • Soft systems methodology
  • Computer Tools for Energy Systems
  • Customer loyalty improves the effectiveness of recommender systems based on complex network
  • Assessment of workforce systems preferences/skills based on Employment domain
  • Efficient NTRU lattice-based certificateless signature scheme for medical cyber-physical systems
  • Market drivers of sustainability and sustainability learning capabilities: The moderating role of sustainability control systems
  • Fedfast: Going beyond average for faster training of federated recommender systems
  • Sustainability management control systems in higher education institutions from measurement to management
  • Understanding user trust in artificial intelligence‐based educational systems: Evidence from China
  • Feature selection using genetic algorithms for the generation of a recognition and classification of children activities model using environmental sound
  • Attributes reductions of bipolar fuzzy relation decision systems
  • Basic classes in conceptual modeling: theory and practical guidelines
  • Dynamic-sos: An approach for the simulation of systems-of-systems dynamic architectures
  • A novel software engineering approach toward using machine learning for improving the efficiency of health systems
  • Emergent properties of foveated perceptual systems
  • Fuzzy model estimation of the risk factors impact on the target of promotion of the software product
  • A real-time data-driven collaborative mechanism in fixed-position assembly systems for smart manufacturing
  • Ontologies as nested facet systems for human–data interaction
  • Incorporating rainwater harvesting systems in Iran’s potable water-saving scheme by using a GIS-simulation based decision support system
  • Digital storytelling and blockchain as pedagogy and technology to support the development of an inclusive smart learning ecosystem
  • … of everyday life–How COVID-19 pandemic transformed the basic education of the young generation and why information management research should care?
  • Teaching programming to the post-millennial generation: Pedagogic considerations for an IS course
  • Uncertainty in information system development: Causes, effects, and coping mechanisms
  • An effective training scheme for deep neural network in edge computing enabled Internet of medical things (IoMT) systems
  • Bureaucracy as a lens for analyzing and designing algorithmic systems
  • Strictly linear light cones in long-range interacting systems of arbitrary dimensions
  • Self-sovereign identity in a globalized world: Credentials-based identity systems as a driver for economic inclusion
  • How Much Method-in-Use Matters? A Case Study of Agile and Waterfall Software Projects and Their Design Routine Variation
  • Underground channel model for visible light wireless communication based on neural networks
  • Enhancing the classification of social media opinions by optimizing the structural information
  • Web Scraping with HTML DOM Method for Data Collection of Scientific Articles from Google Scholar
  • Robotic process mining: vision and challenges
  • High-performance work systems, innovation and knowledge sharing
  • A systematic analysis of the optimization of computerized physician order entry and clinical decision support systems: a qualitative study in English hospitals
  • Software systems from smart city vendors
  • Information Processing, Information Networking, Cognitive Apparatuses and Sentient Software Systems
  • WAx: An integrated conceptual framework for the analysis of cyber-socio-technical systems
  • On data lake architectures and metadata management
  • Interactive planning support systems with citizens: Lessons learned from renewable energy planning in the Netherlands
  • Professional identity and the adoption of learning management systems
  • Towards Anticipatory Information Systems and Action: Notes on Early Warning and Early Action in East Africa
  • Perception and prediction of intention to use online banking systems: An empirical study using extended TAM
  • Automated verbal autopsy: from research to routine use in civil registration and vital statistics systems
  • Adaptive event-triggered control for unknown second-order nonlinear multiagent systems
  • Resource awareness in unmanned aerial vehicle-assisted mobile-edge computing systems
  • Improved covering-based collaborative filtering for new users’ personalized recommendations
  • A new model for the selection of information technology project in a neutrosophic environment
  • Experience versus expectation: Farmers’ perceptions of smart farming technologies for cropping systems across Europe
  • Agility and the role of project—Internal control systems for innovation project performance
  • Introducing systems approaches
  • Decreasing the problematic use of an information system: An empirical investigation of smartphone game players
  • Off-policy learning in two-stage recommender systems
  • Efficient neural matrix factorization without sampling for recommendation
  • Application of k-means clustering algorithm for determination of fire-prone areas utilizing hotspots in West Kalimantan Province
  • Sentiment word co-occurrence and knowledge pair feature extraction based LDA short text clustering algorithm
  • Smart contract invocation protocol (SCIP): A protocol for the uniform integration of heterogeneous blockchain smart contracts
  • Monetizing Online Content: Digital Paywall Design and Configuration
  • Adaptive systems for internet-delivered psychological treatments
  • STFT cluster analysis for DC pulsed load monitoring and fault detection on naval shipboard power systems
  • A decade of NeuroIS research: progress, challenges, and future directions
  • Examining the channel choice of experience-oriented customers in Omni-Channel retailing
  • On the ability of virtual agents to decrease cognitive load: an experimental study
  • Neighborhood multi-granulation rough sets-based attribute reduction using Lebesgue and entropy measures in incomplete neighborhood decision systems
  • Recursive coupled projection algorithms for multivariable output-error-like systems with coloured noises
  • A dynamic deep-learning-based virtual edge node placement scheme for edge cloud systems in mobile environment
  • Dbkwik: extracting and integrating knowledge from thousands of wikis
  • The determinants of digital payment systems’ acceptance under cultural orientation differences: The case of uncertainty avoidance
  • Energy systems for climate change mitigation: A systematic review
  • Helpfulness prediction for online reviews with explicit content-rating interaction
  • Do advanced information technologies produce equitable government responses in coproduction: an examination of 311 systems in 15 US cities
  • The future (s) of digital agriculture and sustainable food systems: An analysis of high-level policy documents
  • A brief history of intelligent decision support systems
  • Trainable communication systems: Concepts and prototype
  • A survey of state-of-the-art approaches for emotion recognition in text
  • Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience
  • Autonomous litter surveying and human activity monitoring for governance intelligence in coastal eco-cyber-physical systems
  • Students’ perceptions on learning management systems of Arabic learning through blended learning model
  • Information technology–based tracing strategy in response to COVID-19 in South Korea—privacy controversies
  • On privacy of dynamical systems: An optimal probabilistic mapping approach
  • Analysis of Malware Impact on Network Traffic using Behavior-based Detection Technique
  • Game-based learning and gamification to improve skills in early years education
  • Developing Design Principles for Digital Platforms: An Agent-Based Modeling Approach
  • Processes, benefits, and challenges for adoption of blockchain technologies in food supply chains: a thematic analysis
  • Neural Fuzzy Based Intelligent Systems and
  • How agile software development methods reduce work exhaustion: Insights on role perceptions and organizational skills
  • Establishment of critical success factors for implementation of product lifecycle management systems
  • The ethical, legal and social implications of using artificial intelligence systems in breast cancer care
  • The ethical balance of using smart information systems for promoting the United Nations’ sustainable development goals. Sustainability 12: 4826
  • Quantum Information
  • Assessing the effectiveness of rural credit policy on the adoption of integrated crop-livestock systems in Brazil
  • Geographical tracking and mapping of coronavirus disease COVID-19/severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic and …
  • Problem-based learning and process modeling in teaching information systems
  • Semantics for Cyber-Physical Systems: A cross-domain perspective
  • Measuring User Perspectives on Website Conference Using System Usability Scale
  • A comprehensive review on indoor air quality monitoring systems for enhanced public health
  • Business process monitoring on blockchains: Potentials and challenges
  • A systematic literature review of sparsity issues in recommender systems
  • Special Issue Editorial–Accumulation and Evolution of Design Knowledge in Design Science Research: A Journey Through Time and Space
  • Ethics in telehealth: Comparison between guidelines and practice-based experience-the case for learning health systems
  • Machine learning in business process monitoring: A comparison of deep learning and classical approaches used for outcome prediction
  • PADS Arsenal: a database of prokaryotic defense systems related genes
  • A variational autoencoder solution for road traffic forecasting systems: Missing data imputation, dimension reduction, model selection and anomaly detection
  • Feedback driven improvement of data preparation pipelines
  • IoT ecosystem: A survey on devices, gateways, operating systems, middleware and communication
  • Digital transformation and the new logics of business process management
  • Detecting fraudulent accounts on blockchain: A supervised approach
  • Quarry: a user-centered big data integration platform
  • A theoretical framework for the evaluation of massive digital participation systems in urban planning
  • Gamifying knowledge sharing in humanitarian organisations: a design science journey
  • Massive access for future wireless communication systems
  • A systematic review of Community Engagement (CE) in Disaster Early Warning Systems (EWSs)
  • Anomaly detection in cyber-physical systems using machine learning
  • RFR-DLVT: a hybrid method for real-time face recognition using deep learning and visual tracking
  • Characterizing the propagation of situational information in social media during covid-19 epidemic: A case study on weibo
  • Moving beyond the direct impact of using CRM systems on frontline employees’ service performance: The mediating role of adaptive behaviour
  • A critical interpretive synthesis of the roles of midwives in health systems
  • Smart monitoring and controlling of government policies using social media and cloud computing
  • Advances in smart antenna systems for wireless communication
  • CAD for Control Systems
  • Diagnosis support systems for rare diseases: a scoping review
  • Agents and multi-agent systems as actor-networks
  • Quaternion Markov Splicing Detection for Color Images Based on Quaternion Discrete Cosine Transform
  • The effect of perceived similarity in dominance on customer self-disclosure to chatbots in conversational commerce
  • Developing web-based support systems for predicting poor-performing students using educational data mining techniques
  • A fuzzy performance evaluation model for government websites based on language property and balanced score card
  • An affective response model for understanding the acceptance of mobile payment systems
  • The impact of control styles and control modes on individual-level outcomes: a first test of the integrated IS project control theory
  • Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?
  • Joint transmit and reflective beamforming design for IRS-assisted multiuser MISO SWIPT systems
  • Drought risk to agricultural systems in Zimbabwe: A spatial analysis of hazard, exposure, and vulnerability
  • Dynamical systems and neural networks
  • A personal data store approach for recommender systems: enhancing privacy without sacrificing accuracy
  • A participatory approach based on stochastic optimization for the spatial allocation of Sustainable Urban Drainage Systems for rainwater harvesting.
  • Keeping Community in the Loop: Understanding Wikipedia Stakeholder Values for Machine Learning-Based Systems
  • The SOTA approach to engineering collective adaptive systems
  • Assessing risks of biases in cognitive decision support systems
  • An enhanced design of sparse autoencoder for latent features extraction based on trigonometric simplexes for network intrusion detection systems
  • A survey on methods for the safety assurance of machine learning based systems
  • Cloud-based in-memory columnar database architecture for continuous audit analytics
  • (Re) considering the concept of literature review reproducibility
  • Practice Makes Perfect: Lesson Learned from Five Years of Trial and Error Building Context-Aware Systems
  • Safety assurance mechanisms of collaborative robotic systems in manufacturing
  • Geographical landslide early warning systems
  • What Does PISA Tell Us About Performance of Education Systems?
  • On Using Physical Based Intrusion Detection in SCADA Systems
  • Digital innovation dynamics influence on organisational adoption: the case of cloud computing services
  • Bitcoin investment: a mixed methods study of investment motivations
  • Understanding the role of ICT and study circles in enabling economic opportunities: Lessons learned from an educational project in Kenya
  • Value cocreation for service innovation: Examining the relationships between service innovativeness, customer participation, and mobile app performance
  • An alumni assessment of MIS related job skill importance and skill gaps
  • Leveraging semantic and lexical matching to improve the recall of document retrieval systems: A hybrid approach
  • The Cyber Threats Analysis for Web Applications Security in Industry 4.0
  • Smart contracts for blockchain-based reputation systems: A systematic literature review
  • A survey of recent methods on deriving topics from Twitter: algorithm to evaluation
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  • A survey of neural networks usage for intrusion detection systems
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  • Deep reinforcement learning for intelligent transportation systems: A survey
  • Code analysis for intelligent cyber systems: A data-driven approach
  • Smart management energy systems in industry 4.0
  • Trustworthiness in industrial IoT systems based on artificial intelligence
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  • Urban systems and the role of big data
  • Adjusting to epidemic-induced telework: empirical insights from teleworkers in France
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  • Fuzzy test model for performance evaluation matrix of service operating systems
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  • Application of intelligent multi agent based systems for E-healthcare security
  • Hidden fuzzy information: Requirement specification and measurement of project provider performance using the best worst method
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  • A cloud-edge based data security architecture for sharing and analysing cyber threat information
  • Adaptive Observer-Based Output Regulation of Multiagent Systems With Communication Constraints
  • A fault diagnosis method for power transmission networks based on spiking neural P systems with self-updating rules considering biological apoptosis …
  • Understanding massively multiplayer online role‐playing game addiction: A hedonic management perspective
  • Interoperability and integration testing methods for IoT systems: A systematic mapping study
  • Proxy tasks and subjective measures can be misleading in evaluating explainable ai systems
  • Emptransfo: A multi-head transformer architecture for creating empathetic dialog systems
  • The case of performance variability on dragonfly-based systems
  • IT reliability and its influence on the results of controlling: comparative analysis of organizations functioning in Poland and Switzerland
  • Worker stress in the age of mobile technology: The combined effects of perceived interruption overload and worker control
  • Fair Outlier Detection
  • Evaluation framework for smart disaster response systems in uncertainty environment
  • Distributed bipartite tracking consensus of nonlinear multi-agent systems with quantized communication
  • A decentralized artificial immune system for solution selection in cyber–physical systems
  • I am Me: Brain systems integrate and segregate to establish a multidimensional sense of self
  • Impacts of COVID-19 on agricultural and food systems worldwide and on progress to the sustainable development goals
  • Resource-efficient neural networks for embedded systems
  • COVID-19: challenges to GIS with big data
  • Transparency in complex computational systems
  • Practical synchronization in networks of nonlinear heterogeneous agents with application to power systems
  • A framework for sustainable contact tracing and exposure investigation for large health systems
  • Cyber-physical systems research and education in 2030: Scenarios and strategies
  • Web-based digital twin modeling and remote control of cyber-physical production systems
  • Efficiency creep and shadow innovation: enacting ambidextrous IT Governance in the public sector
  • Performance Based Planning of complex urban social-ecological systems: The quest for sustainability through the promotion of resilience
  • Anomaly detection in smart homes using bayesian networks
  • Improving recommender systems using co-appearing and semantically correlated user interests
  • Security policies and implementation issues
  • Identification of instantaneous anomalies in general aviation operations using energy metrics
  • Reinforcement learning in sustainable energy and electric systems: A survey
  • Cultural influence on e-government development
  • Thermodynamic resources in continuous-variable quantum systems
  • Physical safety and cyber security analysis of multi-agent systems: A survey of recent advances
  • Quantum vs. classical information: operator negativity as a probe of scrambling
  • Dynamical and thermodynamical approaches to open quantum systems
  • Brain-inspired systems: A transdisciplinary exploration on cognitive cybernetics, humanity, and systems science toward autonomous artificial intelligence
  • Using semantic markup to boost context awareness for assistive systems
  • Authoritarianism, outbreaks, and information politics
  • Towards dynamic dependable systems through evidence-based continuous certification
  • Technologies and systems to improve mobility of visually impaired people: a state of the art
  • Clinical managers’ identity at the crossroad of multiple institutional logics in it innovation: The case study of a health care organization in England
  • Multi-agent direct current systems using renewable energy sources and hydrogen fuel cells
  • Interpretable confidence measures for decision support systems
  • Nonstationary control for TS fuzzy Markovian switching systems with variable quantization density
  • Mitigating the intrusive effects of smart home assistants by using anthropomorphic design features: A multimethod investigation
  • AoI-optimal joint sampling and updating for wireless powered communication systems
  • Machine learning based decision making for time varying systems: parameter estimation and performance optimization
  • A case study of agile software development for safety-Critical systems projects
  • The phishing funnel model: A design artifact to predict user susceptibility to phishing websites
  • Online display advertising markets: A literature review and future directions
  • Future prospects of information warfare and particularly psychological operations
  • Attacking machine learning systems
  • What is the relationship among positive emotions, sense of presence, and ease of interaction in virtual reality systems? An on-site evaluation of a commercial virtual …
  • Quantum computer systems for scientific discovery
  • Lizards in the Street! Introducing Cybersecurity Awareness in a Digital Literacy Context.
  • From microbial communities to distributed computing systems
  • Autonomous systems in anesthesia: Where do we stand in 2020? A narrative review
  • Balancing health privacy, health information exchange, and research in the context of the COVID-19 pandemic
  • Editor’s comments: The COVID-19 pandemic: Building resilience with IS research
  • Scenario-based development of intelligent transportation systems for road freight transport in Germany
  • Simulation in the design and operation of manufacturing systems: state of the art and new trends
  • Understanding adversarial attacks on deep learning based medical image analysis systems
  • Management of distributed energy systems on the basis of optimization methods and expert approaches
  • Breaking into the curriculum: The impact of information technology on schooling
  • Hybrid quantum systems with circuit quantum electrodynamics
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  • Entanglement in indistinguishable particle systems
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  • Training or Synergizing? Complex Systems Principles Change the Understanding of Sport Processes
  • Intelligent forecasting with machine learning trading systems in chaotic intraday Bitcoin market
  • Effect of ground surface interpolation methods on the accuracy of forest attribute modelling using unmanned aerial systems-based digital aerial photogrammetry
  • The food systems in the era of the coronavirus (COVID-19) pandemic crisis
  • A qualitative content analysis of nurses’ comfort and employment of workarounds with electronic documentation systems in home care practice
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  • Orbital angular momentum holography for high-security encryption
  • Using requirement-functional-logical-physical models to support early assembly process planning for complex aircraft systems integration
  • A flood risk assessment framework for interdependent infrastructure systems in coastal environments
  • Deepmaker: A multi-objective optimization framework for deep neural networks in embedded systems
  • A Framework for Risk Assessment in Collaborative Networks to Promote Sustainable Systems in Innovation Ecosystems
  • A survey on adversarial recommender systems: from attack/defense strategies to generative adversarial networks
  • Logistics Optimization of Agricultural Products Supply to the European Union Based on Modeling by Petri Nets
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  • Multisensorial generative and descriptive self-awareness models for autonomous systems
  • Distributed fusion filter for nonlinear multi-sensor systems with correlated noises
  • Improving the security of internet of things using cryptographic algorithms: A case of smart irrigation systems
  • Self-optimizing machining systems
  • EARL—Embodied agent-based robot control systems modelling language
  • MAMBA: A multi-armed bandit framework for beam tracking in millimeter-wave systems
  • Digital transformation of business ecosystems: Evidence from the Korean pop industry
  • Health care service delivery based on the Internet of things: A systematic and comprehensive study
  • A Genetic algorithm for multi-objective reconfiguration of balanced and unbalanced distribution systems in fuzzy framework
  • Selection of intermediate routes for secure data communication systems using graph theory application and grey wolf optimisation algorithm in MANETs
  • New closed-loop insulin systems
  • Realization of AI-enhanced industrial automation systems using intelligent Digital Twins
  • Shadow systems in assessment: how supervisors make progress decisions in practice
  • TREC-COVID: rationale and structure of an information retrieval shared task for COVID-19
  • Data driven approach to risk management and decision support for dynamic positioning systems
  • Enterprise systems in transition economies: research landscape and framework for socioeconomic development
  • Tensors and compositionality in neural systems
  • Towards byzantine-resilient learning in decentralized systems
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  • Object detection with low capacity GPU systems using improved faster R-CNN
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  • Introduction to the Theory of Radiopolarimetric Navigation Systems
  • Spatial disparities in coronavirus incidence and mortality in the United States: an ecological analysis as of May 2020
  • The neural and computational systems of social learning
  • Sample complexity of kalman filtering for unknown systems
  • Modeling and assessing cyber resilience of smart grid using Bayesian network-based approach: a system of systems problem
  • Systems of neutrosophic linear equations
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  • Early warning systems in biosecurity; translating risk into action in predictive systems for invasive alien species
  • Internet of things in sustainable energy systems
  • A polynomial-membership-function approach for stability analysis of fuzzy systems
  • Designing for ambient UX: design framework for managing user experience within cyber-physical systems
  • NNV: The neural network verification tool for deep neural networks and learning-enabled cyber-physical systems
  • Big Spatiotemporal Data Analytics: A research and innovation frontier
  • Navigating the gender structure in information technology: How does this affect the experiences and behaviours of women?
  • Water electrolysers with closed and open electrochemical systems
  • A Generic Network Compression Framework for Sequential Recommender Systems
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  • Improve three-dimensional point localization accuracy in stereo vision systems using a novel camera calibration method
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  • Security in Telehealth Systems From a Software Engineering Viewpoint: A Systematic Mapping Study
  • Applying process mining and semantic reasoning for process model customisation in healthcare
  • The mediating influence of smartwatch identity on deep use and innovative individual performance
  • An infrastructure for embedded systems using task scheduling
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  • Exploring the influential factors of continuance intention to use mobile Apps: Extending the expectation confirmation model
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  • LIMITS: Lightweight machine learning for IoT systems with resource limitations
  • Network device workload prediction: A data mining challenge at knowledge pit
  • Performance optimization of IoT based biological systems using deep learning
  • Spatial-temporal data-driven service recommendation with privacy-preservation
  • Stochastic Transceiver Optimization in Multi-Tags Symbiotic Radio Systems
  • Industrial cyber-physical systems-based cloud IoT edge for federated heterogeneous distillation
  • Industry 4.0 and the human factor–A systems framework and analysis methodology for successful development
  • Intelligent traffic control for autonomous vehicle systems based on machine learning
  • Dynamic pilot allocation scheme for joint user grouping and alliance game in massive MIMO systems
  • Comparing rapid scoring systems in mortality prediction of critically ill patients with novel coronavirus disease
  • Innovative IT use and innovating with IT: A study of the motivational antecedents of two different types of innovative behaviors
  • Joint multi-innovation recursive extended least squares parameter and state estimation for a class of state-space systems
  • Achievable rate optimization for MIMO systems with reconfigurable intelligent surfaces
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  • Probing effective wetting in subsurface systems
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  • Multidisciplinary and multifidelity framework for evaluating system-of-systems capabilities of unmanned aircraft
  • CSI feedback overhead reduction for 5g massive mimo systems
  • Channel estimation for extremely large-scale massive MIMO systems
  • Learning systems: Managing uncertainty in the new normal of COVID-19
  • Smart Collaborative Balancing for Dependable Network Components in Cyber-Physical Systems
  • State Machine Fault Protection Architecture for Aerospace Vehicle Guidance, Navigation, and Control
  • Optimizing ontology alignment in vector space
  • Intelligent and connected cyber-physical systems: A perspective from connected autonomous vehicles
  • Do‐it‐yourself closed‐loop systems for people living with type 1 diabetes
  • Development of a novel tool to support engagement with continuous glucose monitoring systems and optimize outcomes
  • BLCS: Brain-like based distributed control security in cyber physical systems
  • Speckle denoising techniques in imaging systems
  • Secure transmission designs for NOMA systems against internal and external eavesdropping
  • Memory-Efficient Learning of Stable Linear Dynamical Systems for Prediction and Control
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  • Thermodynamic uncertainty relation for general open quantum systems
  • Big Data analytics and Computational Intelligence for Cyber–Physical Systems: Recent trends and state of the art applications
  • Gait-based identification for elderly users in wearable healthcare systems
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  • Prediction of users’ professional profile in moocs only by utilising learners’ written texts
  • A roadmap to integrate astrocytes into Systems Neuroscience
  • COVID-19 and dengue, co-epidemics in Ecuador and other countries in Latin America: pushing strained health care systems over the edge
  • Actual issues of electronic commerce development in the republic of Uzbekistan
  • Thoughts on Using Systems Thinking to Develop Chemistry Students’ Professional Skills
  • Omics in systems biology: Current progress and future outlook
  • Robust beamforming design for intelligent reflecting surface aided MISO communication systems
  • Automated License Plate Recognition using Existing University Infrastructure and Different Camera Angles
  • Public budgeting systems
  • Fuzzy adaptive finite-time fault-tolerant control for strict-feedback nonlinear systems
  • Volt-VAR-Pressure Optimization of Integrated Energy Systems with Hydrogen Injection
  • Tracking urban geo-topics based on dynamic topic model
  • Detection of probe flow anomalies using information entropy and random forest method
  • Exploring the rhetoric of international professional communication: An agenda for teachers and researchers
  • A synergic framework for cyber-physical production systems in the context of Industry 4.0 and beyond
  • Electronic medical record systems: Decision support examination framework for individual, security and privacy concerns using multi-perspective analysis
  • Self-assembled extended π-systems for sensing and security applications
  • River toxicity assessment using molecular biosensors: Heavy metal contamination in the Turag-Balu-Buriganga river systems, Dhaka, Bangladesh
  • A novel adaptive fuzzy control for output constrained stochastic non-strict feedback nonlinear systems
  • Assessment Modeling: Fundamental Pre-training Tasks for Interactive Educational Systems
  • Fault detection for uncertain LPV systems using probabilistic set-membership parity relation
  • Information scrambling and loschmidt echo
  • Event-triggered fuzzy bipartite tracking control for network systems based on distributed reduced-order observers (revised manuscript of TFS-2019-1049)
  • Students’ use of learning management systems and desired e-learning experiences: are they ready for next generation digital learning environments?
  • Theoretical and experimental possibilities to set up some sensors systems involved in active safety process
  • Hyperparameter optimization in CNN for learning-centered emotion recognition for intelligent tutoring systems
  • Diet: Lightweight language understanding for dialogue systems
  • Clarify the physical process for fractional dynamical systems
  • Enterprise system lifecycle-wide innovation
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  • Gradient estimation algorithms for the parameter identification of bilinear systems using the auxiliary model
  • Neurobiology of systems memory consolidation
  • Use of apps in the COVID-19 response and the loss of privacy protection
  • Using the Systems Engineering Initiative for Patient Safety (SEIPS) model to describe critical care nursing during the SARS‐CoV‐2 pandemic (2020)
  • Semantics of the Black-Box: Can knowledge graphs help make deep learning systems more interpretable and explainable?
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  • Quantifying the impacts of climate change and extreme climate events on energy systems
  • Helping university students to choose elective courses by using a hybrid multi-criteria recommendation system with genetic optimization
  • A prospect theory-based QUALIFLEX for uncertain linguistic Z-number multi-criteria decision-making with unknown weight information
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  • From smart farming towards agriculture 5.0: A review on crop data management
  • Optimal control in partially observable complex social systems
  • Determinant Factors’ Impact on Managerial Performance through Management Accounting Systems in Indonesia
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  • Photocatalytic and photoelectrochemical systems: similarities and differences
  • Modelling net-zero emissions energy systems requires a change in approach
  • Augmenting traffic signal control systems for urban road networks with connected vehicles

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RESEARCH AT THE LEADING EDGE

Doctoral Studies in Information Systems & Management

Ph.D. Studies in Information Systems & Management

The doctoral program in Information Systems & Management at Carnegie Mellon University's Heinz College prepares students with a deep understanding of the technical and organizational aspects of information systems.

At Heinz, we live and work at the critical nexus of information technology and public policy. Our Ph.D. in Information Systems & Management was created to train scholars to conduct innovative research that cuts across disciplines in order to address significant challenges in IT theory, strategy, management, and design as it relates to business and policy settings. 

Heinz College Ph.D. students enjoy close partnerships with faculty as they explore the complex and exciting interconnectedness of information systems, public policy, and management. Upon graduating, our Ph.D.s receive desirable placements at academic institutions, government agencies, and consulting firms.

KEY RESEARCH AREAS

Doctoral students take on a broad range of topics and problems, but some key areas of strength at Heinz College include:

As technology enables most content to be digitized, it is also upending business models, competition, and policy needs. From electronic health records, to streaming music and videos, to online social networks, digitization is rapidly affecting every part of the user experience, generating new jobs, and displacing old ones. Our faculty is working on a variety of projects under this broad umbrella. Some major projects are examining the role of social networks, online piracy, digital distribution, impact of mobile, the role of online education, and so on. Faculty and students use variety of methods like field experiments, analytical and structural models to study these questions.

Michael D. Smith and Rahul Telang are world recognized experts on the media industry and copyright policies who also head the IDEA research center.

Beibei Li is an expert on social media, mobile marketing, and understanding individuals’ online and offline decision making. 

Pedro Ferreira works on how people use technology in media and education, and is an expert on running randomized experiments.

Ramayya Krishnan  applies operations research tools to a variety of problems in this domain.

Our world-renowned faculty extensively works with both private firms and policy makers.  

We have multiple research centers like  IDEA ,  LARC , and  iLab  which collect large quantities of data to examine these issues.

Growth of big data has offered opportunities for development and application of novel statistical and computational methods for solving societal problems such as crime, policing, fraud detection, health care and more. To be able to use this data requires cutting edge work on developing new methods and machine learning algorithms. Heinz College has some of the top faculty who work at the intersection of machine learning and public policy.

Some key faculty members working in this space are Leman Akoglu , George Chen , Jeremy Weiss , and David Choi . Each of them is working on problems that intersect the need to use Machine Learning method to solve critical societal problems.

We also offer a joint degree in Machine Learning and Public Policy.

Data security and privacy has increasingly become a complex issue that goes beyond mere technology. Faculty at Heinz College are working on understanding users’ security and privacy decisions using economics, behavioral economics, and data analytics frameworks. This leading edge research is at the forefront of designing better tools and better regulations.

Alessandro Acquisti is an expert on economics of privacy and has done path-breaking work in this space.

Rahul Telang ’s work illustrates that firms may not do enough to protect user data, and highlights how we should design our policies.

Leman Akoglu uses large-scale data to understand our security and privacy vulnerabilities.

This group also works closely with faculty from CyLab , an interdisciplinary research center. This work is highly influential, widely cited, and extensively funded.

This group's research is motivated by information technology's important role in improving health care for patients, hospitals, and doctors. Technology is extensively used in detecting outbreaks, in providing superior quality of care at lower costs, and in prevention of medical errors.

Rema Padman studies IT adoption in hospitals and physical practices. 

Rahul Telang studies the role of electronic health records.

Martin Gaynor is a world-renowned expert on health policy and examines how technology can help improve policy outcomes.

Amelia Haviland examines the role of insurance policies and how they affect patient welfare.

As in other domains, our work on health care and IT is highly influential and has led to significant publications and extensive funding.

Ph.D. Curriculum

The pre-dissertation stage of the Ph.D. in Information Systems & Management is structured around two sets of requirements: coursework and preliminary papers.

Coursework is designed to build methodological skills, modeling competence, and substantive depth.

Preliminary papers illustrate your ability to produce effective research that exhibits your readiness to begin the dissertation.

  • A three-semester   Ph.D. Seminar Series   focusing on the research process
  • Two semesters of   Advanced Electives offering depth in specialized fields
  • Quantitative Methods Cluster   of courses in statistics, econometrics, and machine learning
  • Two semesters of coursework in   Social and Policy Sciences
  • Concentration Area Requirement , combining research and courses to support your research agenda and long-term professional objectives
  • Two semester-long  Technology Classes

Admission to candidacy means that all requirements of the Ph.D. program preliminary to the dissertation have been fulfilled. In addition to satisfying all coursework requirements, you must also meet the following research requirements:

  • First- and second-year Research Papers meeting current Ph.D. requirements
  • Dissertation focused on Information Systems topic as per judgment of Ph.D. committee

While fulfilling these requirements, you'll work closely with the faculty to develop individualized programs of study and research that meet your goals.

Department of Information Systems

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Research papers

Gain practical and in-depth research experience by working with our esteemed faculty. From the beginning of your PhD program, you’ll work on research papers and practices.

During the first two years of our research-focused doctoral program, research project courses give you an opportunity to be mentored by our faculty. Within these courses, you will complete a research paper each year and develop the skills necessary to conduct future research.

Research areas

W. P. Carey faculty and PhD students present their research at leading national and international conferences, and publishing academic journals. Research topics include:

  • Business analytics/big data
  • Healthcare IT
  • IT-enabled services and process innovation
  • Information economics and strategy
  • Security and privacy
  • Social media/e-commerce and mobile platforms

In addition to information systems, our PhD students are often published by leading journals in other disciplines, including accounting, economics, finance, health care management, marketing, managerial decision-making, operations management, strategic management.

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Research Rankings

Student publications.

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Program at a Glance

This program is designed for students who seek training in advanced theoretical and applied issues in the field of information systems. The training prepares students for conducting leading-edge research in topics ranging from the design of optimized systems to the effective use of such systems in organizations. Students undergo rigorous training in research methodologies, as well as in the design of information systems. The research conducted is often interdisciplinary in nature and is characterized by strong analytical or econometric modeling of new and emerging issues in information technology management and creation. The program prepares students mainly for academic positions in research universities; some students may be placed in research positions in industry, government or consulting organizations.

The PhD in Information Systems program is characterized by a high ratio of research faculty to students, which fosters close working relationships. Students have the opportunity to be involved in ongoing faculty research projects under the mentorship of experienced professors. The close interaction with faculty enables students to quickly learn to identify and develop research ideas and create their own research agenda. Students also develop their teaching skills under faculty mentorship by teaching organized classes.

Successful candidates must possess a strong aptitude for abstract thinking and quantitative analysis to address relevant business problems. Students admitted into the PhD in Information Systems program devote the first few years to coursework and research projects, preparing for the qualifying examinations and developing their preliminary dissertation proposal. The next one to two years are devoted to dissertation research and writing. Students must complete at least 75 semester hours of approved graduate work before a degree may be conferred. Credit may be granted for courses taken elsewhere.

Program Contact

Srinivasan Raghunathan, PhD

Srinivasan Raghunathan, PhD

Ashbel smith professor phd area coordinator, information systems.

[email protected] | (972) 883-4377 | JSOM 3.425

The Information Systems Doctoral program at the Naveen Jindal School of Management offers an outstanding opportunity for research in the Information Systems discipline. Our faculty’s research productivity usually ranks at the top in the world. More importantly, we work at the forefront of a variety of research topics and methodologies. Come join us to make this program even stronger!

Faculty research pursuits range from quantitative modeling to empirical studies, mathematical programming, applied stochastic processes, statistics, econometrics and economics.

With analytical depth and methodology, drawing from disciplines such as economics, operations research and econometrics, their research is both prevalent and employed in today’s rapidly changing technological world.

The UT Dallas Top 100 Worldwide Rankings of Business Schools based on Research Contributions in Information Systems Research, Journal on Computing, MIS Quarterly , 2017–2021.

Top 10 Business Schools Worldwide Based on Research Contributions
Rank University Articles Score Country
2 University of Maryland at College Park (Robert H. Smith School of Business) 48 20.25 USA
3 University of Minnesota at Twin Cities (Carlson School of Management) 34 16.61 USA
4 Temple University (The Fox School of Business and Management) 47 16.32 USA
5 City University of Hong Kong (College of Business) 32 12.46 China
6 University of Texas at Austin   (McCombs School of Business) 30 11.06 USA
7 University of Washington at Seattle   (Michael G. Foster School of Business) 30 10.94 USA
8 Tsinghua University (School of Economics and Management, Internet Industry) 30 10.89 China
9 Carnegie Mellon University (Tepper School of Business) 22 10.70 USA
10 Arizona State University (W.P. Carey School of Business) 24 10.54 USA

Advanced and rigorous coursework, methodology and design, and significant placement on student research are the hallmarks of the Information Systems program.

The Information Systems program is characterized by a high ratio of research faculty to students, which fosters close collaboration. Students have the opportunity to be involved in ongoing research projects under the mentorship of renowned, distinguished faculty.

The program is designed for students to develop a strong aptitude for abstract thinking and quantitative analysis to address relevant business problems for their careers in academia or industry.

As shown in the table below, our Information Systems students have obtained top academic and industry appointments.

Selected University Placements – PhD in Management Science, Information Systems Concentration
Graduation Year Name Initial Placement Title
2023 Ozdemir Yagmur Erasmus University Assistant Professor
2023 Tang Ping Bentley University Assistant Professor
2022 Zhang Haozhao Chinese University of Hong Kong Assistant Professor
2021 Zhang Chenglong Chinese University of Hong Kong Assistant Professor
2020 Hosseini Leila Temple University Assistant Professor
2020 Mehta Sameer University of Illinois at Urbana-Champaign Assistant Professor
2020 Tunc Murat Tilburg University Assistant Professor
2019 Bao Chenzhang Oklahoma State University Assistant Professor
2019 Yang Mingwen University of Washington Assistant Professor
2018 YeongIn Kim Virginia Commonwealth University Assistant Professor
2018 Manmohan Aseri Carnegie Mellon University Visiting Assistant Professor
2018 Cheng Nie Iowa State University Assistant Professor
2018 Danish Saifee University of Alabama Assistant Professor
2018 Jayarajan Samuel University of Texas at Arlington Assistant Professor
2017 Li Lusi California State University, Los Angeles Assistant Professor
2016 Sun Zhen George Washington University Assistant Professor
2014 Mo Jiahui Nanyang Technological University Assistant Professor
2013 Ceran Yasin Santa Clara University Assistant Professor
2013 Kwark Young University of Florida Assistant Professor
2012 Chen Hongyu California State University, Long Beach Assistant Professor
2012 Lee Chul Ho Xavier University Visiting Assistant Professor
2011 Ghoshal Abhijeet University of Minnesota Visiting Professor
2011 Jabr Wael University of Calgary Visiting Professor

The close interaction with faculty enables students to quickly learn to identify and develop research ideas and create their own research agenda. Students also develop their teaching skills under faculty mentorship by teaching organized classes.

Below are examples of student publications in 24 leading business journals from 2017-2021.

Abhijeet Ghoshal , Atanu Lahiri, Debabrata Dey, 2021. “Support forums and software vendor’s pricing strategy.” Information Systems Research , vol. 32.

Srinivasan Raghunathan, Mehmet Ayvaci, YeongIn Kim , Huseyin Cavusoglu , 2021. “Designing payment contracts for healthcare services to induce information sharing: the adoption and the value of health information exchanges (hies).” MIS Quarterly , vol. 45.

Subodha Kumar , Min Chen , Min-Seok Pang, 2021. “Do you have room for us in your IT? An economic analysis of shared IT services and implications for IT industries.” MIS Quarterly , vol. 45.

Young Kwark , Liangfei Qiu, Gene Moo Lee, Paul A. Pavlou, 2021. “On the spillover effects of online product reviews on purchases: evidence from clickstream data.” Information Systems Research , vol. 32.

Sirong Luo, Dengpan Liu , Radha Mookerjee, 2021. “The effects of auction-based pricing mechanisms and social characteristics on microloan performance.” Productions and Operations Management , vol. 30.

Ganesh Janakiraman, Sameer Mehta , Vijay Mookerjee, Milind Dawande, 2021. “How to sell a data set? Pricing policies for data monetization.” Information Systems Research , vol. 32.

Jiahui Mo , Sumit Sarkar, Syam Menon, 2021. “Competing tasks and task quality: an empirical study of crowdsourcing contests.” MIS Quarterly , vol 45.

Murat M. Tunc , Huseyin Cavusoglu , Srinivasan Raghunathan, 2021. “Online product reviews: is a finer-grained rating scheme superior to a coarser one?” MIS Quarterly , vol. 45.

Mingwen Yang , Zhiqiang (Eric) Zheng, Vijay Mookerjee, 2021. “The race for online reputation: implications for platforms, firms, and consumers.” Information Systems Research , vol. 32.

Mingwen Yang , Varghese S. Jacob, Srinivasan Raghunathan, 2021. “Cloud service model’s role in provider and user security investment incentives.” Production and Operations Management , vol. 30.

Milind Dawande, Ganesh Janakiraman, Manmohan Aseri , Vijay S. Mookerjee, 2020. “Ad-blockers: a blessing or a curse?” Information Systems Research , vol. 31.

Chenzhang Bao , Kirk Kirksey, Indranil R. Bardhan, Bruce A. Myers, Harpreet Singh, 2020. “Patient–provider engagement and its impact on health outcomes: a longitudinal study of patient portal use.” MIS Quarterly , vol. 44.

Leila Hosseini , Vijay Mookerjee, Chelliah Sriskandarajah, Shaojie Tang, 2020. “A switch in time saves the dime: a model to reduce rental cost in cloud computing.” Information Systems Research , vol. 31.

Jianqing Chen, Srinivasan Raghunathan, Lusi Li , 2020. “Informative role of recommender systems in electronic marketplaces: a boon or a bane for competing sellers.” MIS Quarterly , vol. 44.

Sameer Mehta , Vijay Mookerjee, Milind Dawande, Ganesh Janakiraman, 2020. “Sustaining a good impression: mechanisms for selling partitioned impressions at ad exchanges.” Information Systems Research , vol. 31.

Zhiqiang (Eric) Zheng, Danish H. Saifee , Atanu Lahiri, Indranil R. Bardhan, 2020. “Are online reviews of physicians reliable indicators of clinical outcomes? a focus on chronic disease management.” Information Systems Research , vol. 31.

Ying Xie, Jayarajan Samuel , Zhiqiang (Eric) Zheng, 2020. “Value of local showrooms to online competitors.” MIS Quarterly , vol. 44.

Jyotishka Ray , Syam Menon, Vijay Mookerjee, 2020. “Bargaining over data: when does making the buyer more informed help?” Information Systems Research , vol. 31.

Zhiqiang (Eric) Zheng, Vijay Mookerjee, Mingwen Yang , 2019. “Prescribing response strategies to manage customer opinions: a stochastic differential equation approach.” Information Systems Research , vol. 30.

Zhengrui Jiang , Dipak C Jain, Xinxue Shawn Qu, 2019. “Optimal market entry timing for successive generations of technological innovations.” MIS Quarterly , vol. 43.

Xinxue Shawn Qu, Zhengrui Jiang, 2019. “A time-based dynamic synchronization policy for consolidated database systems.” MIS Quarterly , vol. 43.

Byungwan, Koh , Il-Horn Hann, Srinivasan Raghunathan, 2019. “Digitization of music: consumer adoption amidst piracy, unbundling, and rebundling.” MIS Quarterly , vol. 43.

Milind Dawande, Ganesh Janakiraman, Zhen Sun , Vijay Mookerjee, 2019. “Data-driven decisions for problems with an unspecified objective function.” Journal on Computing , vol. 31.

Yue Zhang , Jian-Yu Fisher Ke, Nan Hu , Ling Liu, 2019. “Risk pooling, supply chain hierarchy, and analysts’ forecasts.” Production and Operations Management , vol. 28.

Vijay Mookerjee, Milind Dawande, Ganesh Janakiraman, Manmohan Aseri , 2018. “Procurement policies for mobile-promotion platforms.” Management Science , vol. 64.

Srinivasan Raghunathan, Lusi Li , Jianqing Chen, 2018. “Recommender system rethink: implications for an electronic marketplace with competing manufacturers.” Information Systems Research , vol. 29.

Srinivasan Raghunathan, Young Kwark , Jianqing Chen, 2018. “User-generated content and competing firms product design.” Management Science , vol. 64.

Vijay Mookerjee, Yong Tan, Depngpan Liu , 2018. “When can ignorance be bliss: organizational structure and coordination in electronic retailing.” Information Systems Research , vol. 29.

Vijay Mookerjee, Dengpan Liu , 2018. “Advertising competition on the internet: operational and strategic considerations.” Production and Operations Management , vol. 27.

Mingzheng Wang, Yu Zhang, Zhengrui Jiang , Haifang Yang, 2018. “T-closeness slicing: a new privacy-preserving approach for transactional data publishing.” Journal on Computing , vol. 30.

Sarkar, Sumit, Menon, Syam, Mo, Jiahui , 2018. “Know when to run: recommendations in crowdsourcing contests.” MIS Quarterly , vol. 42.

Feng, Haiyang, Jiang, Zhengrui , Liu, Dengpan . “Quality, pricing, and release time: optimal market entry strategy for software-as-a-service vendors.” MIS Quarterly , 2018, vol. 42.

Bardhan, Indranil, Zheng, Zhiqiang, Ayabakan, Sezgin . “A data envelopment analysis approach to estimate it-enabled production capability.” MIS Quarterly , 2017, vol. 41.

Janakiraman, Ganesh, Sun, Zhen , Mookerjee, Vijay, Dawande, Milind. “Not just a fad: optimal sequencing in mobile in-app advertising.” Information Systems Research , 2017, vol. 28.

Ghoshal, Abhijeet , Lahiri, Atanu, Dey, Debabrata. “Drawing a line in the sand: Commitment problem in ending software support.” MIS Quarterly , 2017, vol. 41.

Mookerjee, Vijay, Cai, Yuanfeng, Jiang, Zhengrui . “How to deal with liars? Designing intelligent rule-based expert systems to increase accuracy or reduce cost.” Journal on Computing , 2017, vol. 29.

Nault, Barrie, Raghunathan, Srinivasan, Koh, Byungwan . “Is voluntary profiling welfare enhancing?” MIS Quarterly , 2017, vol. 41.

Raghunathan, Srinivasan, Cezar, Asunur , Cavusoglu, Huseyin. “Sourcing information security operations: the role of risk interdependency and competitive externality in outsourcing decisions.” Production and Operations Management , 2017, vol. 26.

Raghunathan, Srinivasan, Kwark, Young , Chen, Jianqing. “Platform or wholesale? A strategic tool for online retailers to benefit from third-party information.” MIS Quarterly , 2017, vol. 41.

Ray, Jyotishka , Samuel, Jayarajan , Menon, Syam, Mookerjee, Vijay. “The design of feature-limited demonstration software: choosing the right features to include.” Production and Operations Management , 2017, vol. 26.

Zhang, Jie, Hu, Nan , Pavlou, Paul. “On self-selection biases in online product reviews.” MIS Quarterly , 2017, vol. 41.

Zheng, Zhiqiang, Ayabakan, Sezgin , Kirksey, Kirk, Bardhan, Indranil. “The impact of health information sharing on duplicate testing.” MIS Quarterly , 2017, vol. 41.

Chen, Hongyu , Zheng, Zhiqiang, Ceran, Yasin. “De-biasing the reporting bias in social media analytics.” Production and Operations Management , 2016, vol. 25.

Hann, Il-Horn, Koh, Byungwan , Niculescu, Marius. “The double-edged sword of backward compatibility: the adoption of multigenerational platforms in the presence of intergenerational services.” Information Systems Research , 2016, vol. 27.

Janakiraman, Ganesh, Sun, Zhen , Mookerjee, Vijay, Dawande, Milind. “The making of a good impression: information hiding in ad exchanges.” MIS Quarterly, 2016, vol. 40.

Lee, Chul Ho , Geng, Xianjun, Raghunathan, Srinivasan. “Mandatory standards and organizational information security.” Information Systems Research , 2016, vol. 27.

Mookerjee, Vijay, Ceran, Yasin , Singh, Harpreet. “Knowing what your customer wants: improving inventory allocation decisions in online movie rental systems.” Production and Operations Management , 2016, vol. 25.

Xia, Hao , Dawande, Milind, Mookerjee, Vijay. “Optimal coordination in distributed software development.” Production and Operations Management , 2016, vol. 25.

Admission Procedures

Applicants should have at least a bachelor’s degree. Admission is based on grade point average, graduate examination test score (GMAT* or GRE), letters of reference (at least three, with two from academic references), business and professional experience (if applicable), a written statement of personal objectives and compatibility with faculty research activities. Since the School of Management starts making first-round admission decisions on December 9, it is best to complete the entire application process no later than December 8. While applications will be accepted after that date, applying after December 8 may significantly lower your chance of acceptance. Applications for admission can be made using the UT Dallas Graduate Application website .

* UT Dallas Naveen Jindal School of Management prefers the GMAT admission test, however, we gladly accept the GRE test as well.

Degree Requirements

Calculus, matrix algebra, computer programming and statistics are prerequisites for the doctoral program – every admitted student is responsible for ensuring he/she has satisfied these prerequisite requirements before joining the program.

Doctoral students in Management Science benefit from exposure to multiple functional areas in management. To ensure this benefit, students who enter the program without an MBA (or equivalent degree) are required to complete a combined minimum of four courses (at the master’s or doctoral level) in at least three functional areas. This cross-functional exposure is beneficial for students engaging in cross-functional research, positioning their research for broader appeal and effectively teaching business school students with diverse specializations.

The Management Science PhD core curriculum consists of a minimum of 9 courses.

Please visit the Management Science Degree Plan page for core and secondary core course requirements.

Nine hours in any approved field.

Students are required to take a sequence of specific courses. Students should consult with faculty members in their respective areas to decide on the sequence of courses.

Twelve hours of special topics and seminars in the information systems area.

Students are required to write original research papers in both their first and second summers. The second-year paper is presented in a seminar attended by faculty and other students and must be judged to be passing by the faculty before the student can advance to candidacy.

PhD in Information Systems students take a written preliminary exam at the end of their first year in the program over a set of core methodology courses ( MECO 6315 Approaches to Statistical Inference, MECO 6345 Advanced Managerial Economics, MECO 6350 Game Theory, OPRE 7353 Optimization). At the end of their fifth semester in the program, students take a qualifying exam (consisting of two parts: a written exam that tests their knowledge of information systems theory and applications and a completed research paper).

PhD students must successfully complete the preliminary and qualifying examinations, respectively, to enter PhD candidacy. The area faculty will determine whether a student has successfully completed the exam requirements based on the student’s performance. Criteria to evaluate students may include results from the in-class written portion of the exams, quality of research papers and/or presentations, performance in special courses (e.g., seminar courses), satisfactory GPA as determined by area faculty and other forms of assessment as required by the student’s area. An unsatisfactory performance in any one of the criteria for either the preliminary examination or the qualifying examination may result in dismissal from the program.

Once the student has passed the qualifying exam and paper requirements, work on the dissertation can commence. The dissertation is written under the direction of the dissertation committee. Twelve to 24 semester hours may be granted for the dissertation toward the minimum 75-hour requirement for the degree. At a time mutually agreeable to the candidate and the dissertation committee, the candidate must orally defend the dissertation to the committee.

The Dissertation Proposal must be successfully defended at least one semester prior to the term of graduation. The requirements for the proposal defense should be discussed with the dissertation committee prior to scheduling the defense. Dissertation Proposal Defenses will be open to all faculty and PhD students of the Jindal School of Management.

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PhD in Information Systems

Build your foundation as an Information Systems scholar.

The PhD in Information Systems seeks to train future researchers to analyze and understand the multifaceted impact of information systems and technologies on individuals, organizations and society. Students consider the positive and negative consequences of information systems as well as the moral and ethical dilemmas introduced in societies coordinated by and highly dependent upon information systems.

Here are some reasons to consider the PhD in Information Systems at Baylor University.

  • Our faculty have diverse research interests with experience publishing in the most respected journals in our discipline, such as MIS Quarterly , Information Systems Research , Journal of the Association for Information Systems , Journal of Management Information Systems and many others.
  • Our current PhD students and graduates are diverse in terms of their backgrounds, nationalities and interests.
  • Our PhD in IS program is a STEM-designated program.
  • Students accepted into our program receive four years of funding, which includes a stipend, tuition remission and subsidized health insurance.

Our program admits students in even-numbered years. The application deadline for the Fall 2024 cohort is January 15, 2024.

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PhD in Information Systems and Technology Management

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The Information Systems and Technology Management (ISTM) program investigates behavioral, design, and economic issues related to the use and impact of information technology. Examples of focal research themes are analytics, behavioral security, consumer and firm behavior in the digital age, digital platform strategy, health care systems, human factors in information systems design, IT governance, and software product development and services delivery. Through coursework, research seminars, and participation in faculty research projects, our students gain the core theoretical and methodological skills needed to conduct independent research and to answer meaningful research questions.

We actively collaborate with our PhD students. Our faculty members frequently work with students to co-publish articles in top journals. View a list of collaborative research by ISTM faculty and PhD students.

According to a 2011 paper in Communications of the AIS by Clark et al., our graduates ranked 4th out of 109 PhD programs studied when considering the top 6 journals (as defined by the AIS Senior Scholars). Our faculty ranked 6th out of 109 schools studied when considering the same target journals.

Katz offers a breadth of research topics–many students can find a home here. Recent PhD dissertation topics have included e-commerce, online communities, project management, IS security and trust, knowledge management, user participation, digital-goods economics, and software measurement. Of course, your thesis topic is limited only by your imagination and drive.

We are a leading program with respect to placement. Over the last decade 100 percent of students who actively sought university positions obtained them, and our recent alumni were placed as faculty members at the following institutions:

  • University of Colorado, Boulder
  • University of Michigan
  • University of Maryland
  • University of Arizona
  • Cleveland State University
  • University of Nevada, Las Vegas
  • College of William and Mary
  • University of Texas, San Antonio
  • University of Delaware
  • Bentley University
  • Youngstown State University
  • Robert Morris University
  • Duquesne University
  • University of Oklahoma
  • Tennessee Technological University

Year 1 Curriculum: Fall Term

An exploration of critical thinking.

BMIS 3 Katz PhD Seminar in Major Area
Research Methods Course
Research Methods Course

Year 1 Curriculum: Spring Term

A thorough examination of research methods.

BMIS 3 Katz PhD Seminar in Major Area
BMIS 3 Katz PhD Seminar in Major Area
Research Methods Course
Research Methods Course

Year 1 Curriculum: Summer Term

Research paper with faculty guidance.

Major Course Choic3

Year 2 Curriculum: Fall Term

A continued study in critical thinking and research methods.

BMIS 3 Katz PhD Seminar in Major Area
Major Course Choice
Research Methods Course
Research Methods Course

Year 2 Curriculum: Spring Term

A further dive into major concentrations, critical thinking, and research methods.

Major Course Choice
Major Course Choice
Research Methods Course

Year 2 Curriculum: Summer Term

A comprehensive independent study.

BMIS 3010 Independent Study in Strategy

Year 3 Curriculum: Fall, Spring, and Summer Terms

Fully concentrated work on dissertation and service as teaching assistant.

BUSADM 3199 (9 Dissertation Credits)
FTDF 0000 Full-Time Dissertation Study

Curriculum: Years 4 and 5

Teach and enter the job market to defend your dissertation.

FTDF 0000 Full-Time Dissertation Study

STUDENT SPOTLIGHT

Why I came to Pitt: I selected Pitt for its reputation of rigorous training and culture of collaboration among students and faculty.  Level of faculty mentoring: I could not be more pleased with the level of faculty mentoring I receive. I have felt supported and appropriately challenged throughout my entire time at Pitt.  I love Pitt because of the people. The faculty, staff, and students all support each other and genuinely want each other to succeed. 

PhD, Accounting

I came to Pitt because of its reputation for educational excellence and its top-tier research faculty. The reputation of the faculty for being not only great researchers, publishing in top journals, but also as effective and available mentors made my decision easy. I have never once second guessed my decision to come to Pitt.   The level of mentoring I receive at the Katz Graduate School of Business at the University of Pittsburgh is beyond what I ever imagined. The faculty are legitimately invested in my success, and devote an inordinate amount of time to helping me grow and develop as scholar and a researcher. I know this is not the case at all institutions, as I have family members in PhD programs at other top institutions, and they are in awe of the level of mentoring I receive. 

PhD, Strategy and Entrepreneurship

  • How we work
  • PhD Research
  • Information Technology PhD

PhD in Information Technology: Research, Making the Difference

Information technology research: roadmapping your career.

There’s no need to question whether the career prospects of earning a PhD in information technology are worth it. Despite the high competition in the program, only a small number of people make it to the end. The math is simple: in the United States, only about 2% of IT workers have a doctorate. That means that from every 100 IT workers, you’ll be one of two with a PhD.

The opportunities that this education and experience provide are undeniable. So does the fact that earning an information technology PhD is challenging. Yet, the length of the program is relatively short. Two to five years of effort is a small investment compared to a future career of at least two decades at a much higher level and salary rate.

Subject Matter Aside: About the Information Technology Research Papers

Research is a key driver of scientific progress in any field, including information technology. It provides the evidence base for theories, hypotheses, and refutations in the physical world. Surprisingly, the more research science contains, the more potential information technology topics it opens up for the future.

All human activities are connected with information technologies in one way or another. Therefore, we should take as an axiom the statement that the evolution of the 21st century is the availability and speed of obtaining information. The IT sphere provides a vast field for scientific and practical activity. The main thing here is the choice of information technology research paper topics.

How to Choose the Research Direction for PhD Information Technology

How do you know if you’ve chosen the right subject? There are certain qualities that all research topics for information technology should have. This should be:

  • Contemporary. Your topic should be meaningful and relevant to the current needs of society and the latest scientific advances.
  • Original and novel. It should offer new insights or solutions to challenges.
  • Practically valuable. PhD research results should have practical value, that is, help solve specific problems or tasks.

phd research in information systems

If your research aligns with these points, congratulations – you’ve chosen a worthy PhD information technology idea! Perhaps this choice will become the starting point for innovation in the future.

Read also: How to write a computer science research paper

  • FREE topic suggestion
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40+22 Information Technology Research Topics to Get Inspired

The world is increasingly consumed by technology. It’s hard to imagine where it hasn’t already been applied. Of course, any modern tool is designed to improve the daily routine. But using modern technologies also entails new challenges, difficulties, questions, and dilemmas. All this forms the basis for numerous questions and the answers search to them in information technology research topics.

The IT industry includes more than one discipline. Depending on the specialization, the PhD research topics will, of course, differ. However, we have highlighted three themes with one thing in common: they create interactions between people and computers. But let’s move on to the specifics of information technology research papers and practical computer research topics that may inspire you.

Information Technology Topics for Research Paper: AI, VR and VA

Artificial intelligence (AI), Virtual reality (VR), and virtual assistants (VA) are three potential game-changers in people’s lives. If the use of AI is already quite widespread, then VR and VA are still alarming phenomena.

These virtual world guides implementation entails not only benefits. The full capabilities of AI, VR, and VA are being discovered, invented, and developed. But even at this stage, it raises questions and ethical issues you must consider in the information technology PhD study.

  • Recognizing dangerous emotions in VA users.
  • VR technologies in school teaching.
  • Using AI to improve protection against cyberattacks.
  • New methods of reproducing feelings in VR.
  • The influence of VR on emotional intelligence.
  • VR in the treatment of PTSD.
  • New context-understanding methods for VA.
  • AI modeling of machinery testing.
  • AI in solving the logistics problem.
  • Research of the impact of VA on human behavior.
  • Application of VA in the individual training and student onboarding.
  • VA in business: impact on customer loyalty.
  • VA in automating tasks and improving work efficiency.
  • Ethical issues of using VR: propaganda or manipulation of people.
  • AI in the creation of information management new models.
  • Ethics in AI-driven Decision Systems.
  • AI and employment: threat or forced transformation?
  • ТNew word addictions: AI/VR/VA
  • VR: avoidance and addition
  • Fight or flight: how to implement and use genetic reactions in VR with maximum efficiency?

technology research paper

This is just a small list of PhD research ideas. AI/VR/VA provides good ground for research. We’ve taken a few ideas of the information technology topics for research paper off the surface, but we think you’ll find a starting point for your subject matter here.

Resourceful List of Ideas for PhD in Information Technology

In this resourceful list, information systems research topics touch on various areas. It’s worth mentioning that these ideas are adaptive, so you can easily use one in your Ph.D. studies in information systems & management , for example. Add there 22 topics from the picture, and you’ll have a promising guide to choosing your PhD research subject matte.

phd research topics in information technology

  • Cyber security challenges: blockchain and crypto.
  • Digital intellectual property issues.
  • Protecting privacy in the digital world: user awareness.
  • Information systems’ role in increasing organizations’ effectiveness in the digital economy.
  • Critical information systems protection.
  • Personal data protection from leaks and theft.
  • Information security: increasing level of trust in businesses.
  • Protection against cyber-attacks using autonomous systems.
  • Effectiveness of algorithms in crime prevention.
  • Use of digital technologies to improve learning efficiency.
  • Computer network security new methods.
  • New methods of mobile devices supporting computer networks.
  • Improving mobile security resilience.
  • Intelligent systems: management of objects.
  • Compilation optimization methods: performance and energy efficiency.
  • Effectiveness of algorithms in crime solving.
  • Using algorithms to train AI to solve complex tasks.
  • Ethical norms regulating the digital economy.
  • Advanced data analytics in climate change modeling.
  • Human-centric design of intelligent transportation systems.

You can’t overestimate the importance of choosing an information technology research subject. It’s not just your ticket to getting a PhD. The question you raise in the research that you seek answers to has implications in the physical world. This PhD work can become a subsequent improvement starting point, innovative discoveries, or, on the contrary, global problem justification to be solved.

Don’t Pause Life, Ask the Information Technology Academic Writing Help

Serious education requires effort. But the result is definitely worth going PhD route. Should you put life on hold while you get the education you want? Not necessarily. Did you know that according to the National Center for Education Statistics (NCES) of the United States, 81% of PhD students worked during the 2021 academic year? And the lion part worked a full day. How did they manage?

They had a life buoy! It’s ok to seek information technology academic writing help while obtaining your PhD. Once you feel overwhelmed, it’s time to ask our expert to step up and help you at any research stage.

Information Systems

Effective solutions for complex business problems..

Simon’s PhD in Information Systems focuses on the business aspects of Information Technology use and management, and the analytic and quantitative tools and techniques to address them. The research conducted by the students and faculty has significantly advanced the state of the art in research on information systems in general, with an emphasis on information systems economics and machine learning modeling.

Meet IS Faculty

Prepare with Math Camp.

Program outline: information systems, the first year.

The first year provides students with a strong methodological foundation in order to prepare them for research in the advanced years of the program. Students are required to take courses offered by Simon Business School as well as the University of Rochester. First year students are required to pass Preliminary Requirements by demonstrating proficiency in specific courses. A research-oriented first-year paper is due August 31 of the start of the second year.

The Second Year

The second year deepens students' knowledge of Information Systems. Students work in conjunction with the faculty to write two papers on two individual research topics. These papers are due by May 31 of the second year. Students are also required to write a Qualifying Exam paper, due by November 30 of the third year. This paper is usually a more in-depth version of one of the two papers used for the second-year papers requirement.

The Third Year and Beyond

Students will take additional courses in the third year after passing the Qualifying Exam. Students also begin work on a dissertation. Research in the field of Information Systems focuses on problems of design, performance analysis, and optimization of information systems.

Students are expected to submit a Thesis proposal paper along with a faculty advisor and committee that has agreed to serve on the dissertation committee. The dissertation must be an original critical or synthetic treatment of a suitable subject, an original contribution to creative art, or a report on independent research formulated in a manner worthy of publication. The exact form and timing of this proposal is defined by the area requirements.

This is achieved after passing the Preliminary and Qualifying Exams. At the conclusion of the qualifying exam, the faculty administering the qualifying exam evaluate the student and through that evaluation make a recommendation to the PhD Faculty Director and Senior Associate Dean of Faculty that the student advance to candidacy.

Required Courses.

The Course Catalog contains degree requirements and course descriptions. Please refer the Simon Registrar's website for the current Course Catalog.

Simon Registrar

Course Catalog

PhD Information Systems Courses

This course introduces important research methods/topics for IS researchers, covering analytical modeling, causal inference, and machine learning. For analytical modeling, we will discuss auction and mechanism design for information/ computation goods. For causal inference, we will establish the probabilistic foundation of commonly used methods such as DID, matching, IV, and regression discontinuity. For machine learning, we will introduce the statistical learning theory, SVM, EM, MCMC, variational inference, and deep learning.

This course introduces students to research in Information Systems (IS) and Operations Management (OM). Multiple lectures will be dedicated to each topic, covering the necessary mathematical background, primary analysis techniques, and important, seminal, or recent papers within each area. The course aims to attain the following objectives: learn about what constitutes research in IS and OM, develop critical thinking about academic papers, familiarize students with new research areas, provide opportunity to think about new research problems, and practice constructing and delivering academic talks.

Information Science: PhD

University of California, Berkeley

About the Program

The doctoral program.

The doctoral program in Information Science is a research-oriented program in which the student chooses specific fields of specialization, prepares sufficiently in the literature and the research of those fields to pass a qualifying examination, and completes original research culminating in the written dissertation. The degree of Doctor of Philosophy is conferred in recognition of a candidate's grasp of a broad field of learning and distinguished accomplishment in that field through the contribution of an original piece of research revealing high critical ability and powers of imagination and synthesis.

The I School also offers a master's in Information Management and Systems (MIMS), a master's in  Information and Data Science  (MIDS), and a master's in  Information and Cybersecurity (MICS).

Visit School Website

Admission to the PhD Program

We welcome students from a diverse set of backgrounds; some will be technically educated, some educated in the humanities and social sciences.

The I School typically accepts 3-7 PhD students each year from more than 100 applications. Applications are reviewed by a committee of faculty.

Applicants are evaluated holistically on a number of factors. A strong academic record is important, but not sufficient. A critical factor is the ability to demonstrate a research record and agenda that fit well with specific I School faculty. In a small, interdisciplinary program, it is important that applicants clearly indicate in their Statement of Purpose which faculty member(s) they are interested in researching with, and why.

To be eligible to apply to the PhD in Information Management and Systems program, applicants must meet the following requirements:

A bachelor's degree or its recognized equivalent from an accredited institution.

Superior scholastic record, normally well above a 3.0 GPA.

Indication of appropriate research goals, described in the Statement of Purpose.

For applicants whose academic work has been in a language other than English, the Test of English as a Foreign Language (TOEFL) or International English Language Testing System (IELTS).

Not required: GRE/GMAT. Starting Fall 2021, we no longer require the GRE or GMAT. We recommend you put your time and effort towards the required application materials.

Further  information about I School Ph.D. Admissions  can be found on the I School website. 

Applying for Graduate Admission

Thank you for considering UC Berkeley for graduate study! UC Berkeley offers more than 120 graduate programs representing the breadth and depth of interdisciplinary scholarship. The Graduate Division hosts a complete list of graduate academic programs, departments, degrees offered, and application deadlines can be found on the Graduate Division website.

Prospective students must submit an online application to be considered for admission, in addition to any supplemental materials specific to the program for which they are applying. The online application and steps to take to apply can be found on the Graduate Division website .

Admission Requirements

The minimum graduate admission requirements are:

A bachelor’s degree or recognized equivalent from an accredited institution;

A satisfactory scholastic average, usually a minimum grade-point average (GPA) of 3.0 (B) on a 4.0 scale; and

Enough undergraduate training to do graduate work in your chosen field.

For a list of requirements to complete your graduate application, please see the Graduate Division’s Admissions Requirements page . It is also important to check with the program or department of interest, as they may have additional requirements specific to their program of study and degree. Department contact information can be found here .

Where to apply?

Visit the Berkeley Graduate Division application page .

Doctoral Degree Requirements

Program design.

The School of Information is an interdisciplinary school examining the design, organization, and management of information and information systems. The School of Information draws on the expertise not only of its own faculty but of the full Berkeley campus. We encourage students to take full advantage of being at this world-class University and not feel bound by disciplinary boundaries.

The PhD degree program at the School of Information is a research program. Each student is expected to work with his or her adviser to ensure that the program of study includes:

  • A thorough understanding of research methods and research design.
  • The ability to review current research critically.
  • The ability to understand emerging trends from an interdisciplinary perspective.

Expected PhD Timeline:

  • Semester 1:  Identify a faculty adviser
  • Semesters 1–4:  Complete breadth courses; complete major and minor requirements
  • Semester 4:  Complete the preliminary research paper
  • Semester 5:  Complete preliminary exam
  • Semester 6–8:  Complete qualifying exam; advance to candidacy
  • Four semesters after qualifying exam:  Complete dissertation and give presentation

Please refer to  the School of Information website  for more information.

Breadth Courses

Course List
CodeTitleUnits
I. Foundation
Concepts of Information3
II. Engineering and Design
Information Organization and Retrieval3
Introduction to Programming and Computation2
Introduction to Data Structures and Analytics2
Introduction to User Experience Design4
Information Visualization and Presentation4
Applied Machine Learning4
Front-End Web Architecture3
Back-End Web Architecture3
Applied Natural Language Processing3
Natural Language Processing4
Theory and Practice of Tangible User Interfaces4
Interface Aesthetics3
III. Social Aspects of Information
Research Design and Applications for Data and Analysis3
Social Issues of Information3
User Experience Research3
Human-Computer Interaction (HCI) Research3
Leadership and Management3
Social Psychology and Information Technology3
Quantitative Research Methods for Information Systems and Management3
Qualitative Research Methods for Information Systems and Management3
Information and Communications Technology for Development3
Big Data and Development3
IV. Information Economics, Law and Policy
Information Law and Policy3
Information Technology Economics, Strategy, and Policy3
Technology and Delegation3
Public Interest Cybersecurity: The Citizen Clinic Practicum3
Special Topics in Social Science and Policy2-4

Major/Minor Areas

Course List
CodeTitleUnits
Human-Computer Interaction
Introduction to User Experience Design4
User Experience Research3
Human-Computer Interaction (HCI) Research3
Information Visualization and Presentation4
Theory and Practice of Tangible User Interfaces4
Interface Aesthetics3
Special Topics in Information (Advanced HCI Research and Interaction Design only)1-4
Special Topics in Technology (Biosensory Computing only)2-4
Plus outside courses upon approval of your advisor
Information Economics and Policy
Information Technology Economics, Strategy, and Policy3
Plus outside courses upon approval of your advisor
Information Law and Policy
Information Law and Policy3
Technology and Delegation3
Public Interest Cybersecurity: The Citizen Clinic Practicum3
Special Topics in Social Science and Policy (Introduction to Politics of Information and Seminar in the Politics of Information only)2-4
Plus outside courses upon approval of your advisor
Information Organization and Retrieval
Information Organization and Retrieval3
Information Visualization and Presentation4
Applied Machine Learning4
Applied Natural Language Processing3
Data Engineering4
Natural Language Processing4
Plus outside courses upon approval of your advisor
Information Systems Design
Introduction to Programming and Computation2
Introduction to Data Structures and Analytics2
Applied Machine Learning4
Front-End Web Architecture3
Back-End Web Architecture3
Privacy Engineering3
Data Engineering4
Applied Natural Language Processing3
Natural Language Processing4
Plus outside courses upon approval of your advisor
Social Aspects of Information
Research Design and Applications for Data and Analysis3
Social Issues of Information3
User Experience Research3
Concepts of Information3
Leadership and Management3
Social Psychology and Information Technology3
Experiments and Causal Inference3
Quantitative Research Methods for Information Systems and Management3
Qualitative Research Methods for Information Systems and Management3
Big Data and Development3
Plus outside courses upon approval of your advisor
Information and Communication Technologies and Devleopment
Social Issues of Information3
Introduction to User Experience Design4
User Experience Research3
Information and Communications Technology for Development3
Big Data and Development3
Plus outside courses upon approval of your advisor

Related Courses

Info 201 research design and applications for data and analysis 3 units.

Terms offered: Fall 2024, Spring 2024, Fall 2023 Introduces the data sciences landscape, with a particular focus on learning data science techniques to uncover and answer the questions students will encounter in industry. Lectures, readings, discussions, and assignments will teach how to apply disciplined, creative methods to ask better questions, gather data, interpret results, and convey findings to various audiences. The emphasis throughout is on making practical contributions to real decisions that organizations will and should make. Course must be taken for a letter grade to fulfill degree requirements. Research Design and Applications for Data and Analysis: Read More [+]

Hours & Format

Fall and/or spring: 15 weeks - 1.5 hours of lecture per week

Additional Format: One and one-half hours of lecture per week.

Additional Details

Subject/Course Level: Information/Graduate

Grading: Letter grade.

Research Design and Applications for Data and Analysis: Read Less [-]

INFO 202 Information Organization and Retrieval 3 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 This course introduces the intellectual foundations of information organization and retrieval: conceptual modeling, semantic representation, vocabulary and metadata design, classification, and standardization, as well as information retrieval practices, technology, and applications, including computational processes for analyzing information in both textual and non-textual formats. Information Organization and Retrieval: Read More [+]

Rules & Requirements

Prerequisites: Students should have a working knowledge of the Python programming language

Fall and/or spring: 15 weeks - 3 hours of lecture per week

Additional Format: Three hours of lecture per week.

Information Organization and Retrieval: Read Less [-]

INFO 203 Social Issues of Information 3 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 This course is designed to be an introduction to the topics and issues associated with information and information technology and its role in society. Throughout the semester we will consider both the consequence and impact of technologies on social groups and on social interaction and how society defines and shapes the technologies that are produced. Students will be exposed to a broad range of applied and practical problems, theoretical issues, as well as methods used in social scientific analysis. The four sections of the course are: 1) theories of technology in society, 2) information technology in workplaces 3) automation vs. humans, and 4) networked sociability. Social Issues of Information: Read More [+]

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INFO 205 Information Law and Policy 3 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 This course uses examples from various commercial domains—retail, health, credit, entertainment, social media, and biosensing/quantified self—to explore legal and ethical issues including freedom of expression, privacy, research ethics, consumer protection, information and cybersecurity, and copyright. The class emphasizes how existing legal and policy frameworks constrain, inform, and enable the architecture, interfaces, data practices, and consumer facing policies and documentation of such offerings; and, fosters reflection on the ethical impact of information and communication technologies and the role of information professionals in legal and ethical work. Information Law and Policy: Read More [+]

Prerequisites: Consent of instructor required for nonmajors

Instructor: Mulligan

Information Law and Policy: Read Less [-]

INFO 206A Introduction to Programming and Computation 2 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 This course introduces the basics of computer programming that are essential for those interested in computer science, data science, and information management. Students will write their own interactive programs (in Python) to analyze data, process text, draw graphics, manipulate images, and simulate physical systems. Problem decomposition, program efficiency, and good programming style are emphasized throughout the course. Introduction to Programming and Computation: Read More [+]

Fall and/or spring: 7.5 weeks - 4 hours of lecture per week

Additional Format: Four hours of lecture per week for seven and one-half weeks.

Instructor: Farid

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INFO 206B Introduction to Data Structures and Analytics 2 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 The ability to represent, manipulate, and analyze structured data sets is foundational to the modern practice of data science. This course introduces students to the fundamentals of data structures and data analysis (in Python). Best practices for writing code are emphasized throughout the course. This course forms the second half of a sequence that begins with INFO 106. It may also be taken as a stand-alone course by any student that has sufficient Python experience. Introduction to Data Structures and Analytics: Read More [+]

Prerequisites: INFO 206A or equivalent, or permission of instructor

Credit Restrictions: Course must be completed for a letter grade to fulfill degree requirements.

Formerly known as: Information 206

Introduction to Data Structures and Analytics: Read Less [-]

INFO 213 Introduction to User Experience Design 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 This course will provide an introduction to the field of Human-Computer Interaction (HCI). Students will learn to apply design thinking to User Experience (UX) design, prototyping, & evaluation. The course will also cover special topic areas within HCI. Introduction to User Experience Design: Read More [+]

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INFO 214 User Experience Research 3 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 This course addresses concepts and methods of user experience research, from understanding and identifying needs, to evaluating concepts and designs, to assessing the usability of products and solutions. We emphasize methods of collecting and interpreting qualitative data about user activities, working both individually and in teams, and translating them into design decisions. Students gain hands-on practice with observation, interview, survey , focus groups, and expert review. Team activities and group work are required during class and for most assignments. Additional topics include research in enterprise, consulting, and startup organizations, lean/agile techniques, mobile research approaches, and strategies for communicating findings. User Experience Research: Read More [+]

Additional Format: Three hours of Lecture per week for 15 weeks.

User Experience Research: Read Less [-]

INFO 215 Product Design Studio 3 Units

Terms offered: Fall 2024, Fall 2023 This course will give participants hands-on digital product design experience oriented around current industry practice. The course will be project-based with an emphasis on iteration, practice, and critique. During the course, participants will work on a series of design projects through a full design process, including developing appropriate design deliverables, gathering feedback, and iterating on designs. Product Design Studio: Read More [+]

Objectives & Outcomes

Course Objectives: The course objective is to provide students interested in web and mobile Product Design with skills, practice, and experience that will prepare them for careers in product design and design-related roles.

Prerequisites: DES INV 15 or COMPSCI 160 or INFO 213 AND INFO 214; or permission of the instructor. Students can take INFO 214 and INFO 215 concurrently, but students may not drop INFO 214 and remain in INFO 215

Formerly known as: Information Systems and Management 215

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INFO 217A Human-Computer Interaction (HCI) Research 3 Units

Terms offered: Spring 2024, Fall 2021, Fall 2020 This course is a graduate-level introduction to HCI research. Students will learn to conduct original HCI research by reading and discussing research papers while collaborating on a semester-long research project. Each week the class will focus on a theme of HCI research and review foundational and cutting-edge research relevant to that theme. The class will focus on the following areas of HCI research: ubiquitous computing , social computing, critical theory, and human-AI interaction. In addition to these research topics the class will introduce common qualitative and quantitative methodologies in HCI research. Human-Computer Interaction (HCI) Research: Read More [+]

Instructor: Salehi

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INFO 218 Concepts of Information 3 Units

Terms offered: Spring 2024, Spring 2022, Spring 2020 As it's generally used, "information" is a collection of notions, rather than a single coherent concept. In this course, we'll examine conceptions of information based in information theory, philosophy, social science, economics, and history. Issues include: How compatible are these conceptions; can we talk about "information" in the abstract? What work do these various notions play in discussions of literacy, intellectual property, advertising, and the political process? And where does this leave "information studies" and "the information society"? Concepts of Information: Read More [+]

Prerequisites: Graduate standing

Instructors: Duguid, Nunberg

Concepts of Information: Read Less [-]

INFO 225 Leadership and Management 3 Units

Terms offered: Fall 2024, Fall 2023, Fall 2021 This course focuses on the practice of leadership, collaboration, and people management in contemporary, distributed, information and technology-rich organizations. Not just for potential people managers, this course is derived from the premise that a foundation in leadership, management, and collaboration is essential for individuals in all roles, at any stage of their career. To build this foundation we will take a hybrid approach, engaging literature from disciplines such as social psychology, management, and organizational behavior, as well as leveraging case studies and practical exercises. The course will place a special emphasis on understanding and reacting to social dynamics in workplace hierarchies and teams. Leadership and Management: Read More [+]

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INFO 231 Decisions and Algorithms 3 Units

Terms offered: Fall 2024, Spring 2013, Spring 2011 This class is for graduate students interested in getting an advanced understanding of judgments and decisions made with predictive algorithms. The course will survey the vast literature on the psychology of how people arrive at judgments and make decisions with the help of statistical information, focused mostly on experimental lab evidence from cognitive and social psychology. Then study the burgeoning evidence on how people use statistical algorithms in practice, exploring field evidence from a range of settings from criminal justice and healthcare to housing and labor markets. Special attention is paid to psychological principles that impact the effectiveness and fairness of algorithms deployed at scale. Decisions and Algorithms: Read More [+]

Course Objectives: Help students understand systematic human errors and explore potential algorithmic solutions.

Decisions and Algorithms: Read Less [-]

INFO 233 Social Psychology and Information Technology 3 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 Discusses application of social psychological theory and research to information technologies and systems; we focus on sociological social psychology, which largely focuses on group processes, networks, and interpersonal relationships. Information technologies considered include software systems used on the internet such as social networks, email, and social games, as well as specific hardware technologies such as mobile devices, computers , wearables, and virtual/augmented reality devices. We examine human communication practices, through the lens of different social psychology theories, including: symbolic interaction, identity theories, social exchange theory, status construction theory, and social networks and social structure theory. Social Psychology and Information Technology: Read More [+]

Instructor: Cheshire

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INFO 234 Information Technology Economics, Strategy, and Policy 3 Units

Terms offered: Spring 2024, Spring 2022, Spring 2021 This course applies economic tools and principles, including game theory, industrial organization, information economics, and behavioral economics, to analyze business strategies and public policy issues surrounding information technologies and IT industries. Topics include: economics of information goods, services, and platforms; economics of information and asymmetric information; economics of artificial intelligence, cybersecurity, data privacy, and peer production; strategic pricing; strategic complements and substitutes; competition and antitrust; Internet industry structure and regulation; network cascades, network formation, and network structure. Information Technology Economics, Strategy, and Policy: Read More [+]

Course Objectives: INFO234 is a graduate level course in the school's topical area of Information Economics and Policy, and can be taken by the masters and doctoral students to satisfy their respective degree requirements.

Student Learning Outcomes: Students will learn to identify, describe, and analyze business strategies and public policy issues of particular relevance to the information industry. Students will learn and apply economic tools and principles to analyze phenomena such as platform competition, social epidemics, and peer production, and current policy issues such as network neutrality and information privacy. Through integrated assignments and project work, the students will apply the theoretical concepts and analytic tools learned in lectures and readings to develop and evaluate a business model, product, or service of their choosing, e.g., a start-up idea they are pursuing.

Instructor: Chuang

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INFO 239 Technology and Delegation 3 Units

Terms offered: Fall 2021, Fall 2019, Fall 2018 The introduction of technology increasingly delegates responsibility to technical actors, often reducing traditional forms of transparency and challenging traditional methods for accountability. This course explores the interaction between technical design and values including: privacy, accessibility, fairness, and freedom of expression. We will draw on literature from design, science and technology studies, computer science, law, and ethics, as well as primary sources in policy, standards and source code. We will investigate approaches to identifying the value implications of technical designs and use methods and tools for intentionally building in values at the outset. Technology and Delegation: Read More [+]

Technology and Delegation: Read Less [-]

INFO 241 Experiments and Causal Inference 3 Units

Terms offered: Fall 2024, Spring 2024, Fall 2022 This course introduces students to experimentation in data science. Particular attention is paid to the formation of causal questions, and the design and analysis of experiments to provide answers to these questions. This topic has increased considerably in importance since 1995, as researchers have learned to think creatively about how to generate data in more scientific ways, and developments in information technology has facilitated the development of better data gathering. Experiments and Causal Inference: Read More [+]

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INFO 247 Information Visualization and Presentation 4 Units

Terms offered: Spring 2023, Spring 2022, Spring 2021 The design and presentation of digital information. Use of graphics, animation, sound, visualization software, and hypermedia in presenting information to the user. Methods of presenting complex information to enhance comprehension and analysis. Incorporation of visualization techniques into human-computer interfaces. Course must be completed for a letter grade to fulfill degree requirements. Information Visualization and Presentation: Read More [+]

Prerequisites: INFO 206B or knowledge of programming and data structures with consent of instructor

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of laboratory per week

Additional Format: Three hours of lecture and one hour of laboratory per week.

Instructor: Hearst

Information Visualization and Presentation: Read Less [-]

INFO 251 Applied Machine Learning 4 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 Provides a theoretical and practical introduction to modern techniques in applied machine learning. Covers key concepts in supervised and unsupervised machine learning, including the design of machine learning experiments, algorithms for prediction and inference, optimization, and evaluation. Students will learn functional, procedural, and statistical programming techniques for working with real-world data. Applied Machine Learning: Read More [+]

Student Learning Outcomes: • Effectively design, execute, and critique experimental and non-experimental methods from statistics, machine learning, and econometrics. • Implement basic algorithms on structured and unstructured data, and evaluate the performance of these algorithms on a variety of real-world datasets. • Understand the difference between causal and non-causal relationships, and which situations and methods are appropriate for both forms of analysis. • Understand the principles, advantages, and disadvantages of different algorithms for supervised and unsupervised machine learning.

Prerequisites: INFO 206B , or equivalent course in Python programming; INFO 271B , or equivalent graduate-level course in statistics or econometrics; or permission of instructor

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Additional Format: Three hours of lecture and one hour of discussion per week.

Instructor: Blumenstock

Applied Machine Learning: Read Less [-]

INFO 253A Front-End Web Architecture 3 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 This course is a survey of technologies that power the user interfaces of web applications on a variety of devices today, including desktop, mobile, and tablet devices. This course will delve into some of the core Front-End languages and frameworks (HTML/CSS/JS/React/Redux), as well as the underlying technologies enable web applications (HTTP, URI, JSON). The goal of this course is to provide an overview of the technical issues surrounding user interfaces powered by the web today, and to provide a solid and comprehensive perspective of the Web's constantly evolving landscape. Front-End Web Architecture: Read More [+]

Prerequisites: Introductory programming

Fall and/or spring: 15 weeks - 2 hours of lecture and 1 hour of laboratory per week

Additional Format: Two hours of lecture and one hour of laboratory per week.

Formerly known as: Information 253

Front-End Web Architecture: Read Less [-]

INFO 253B Back-End Web Architecture 3 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 This course is a survey of web technologies that are used to build back-end systems that enable rich web applications. Utilizing technologies such as Python, Flask, Docker, RDBMS/NoSQL databases, and Spark, this class aims to cover the foundational concepts that drive the web today. This class focuses on building APIs using micro-services that power everything from content management systems to data engineering pipelines that provide insights by processing large amounts of data. The goal of this course is to provide an overview of the technical issues surrounding back-end systems today, and to provide a solid and comprehensive perspective of the web's constantly evolving landscape. Back-End Web Architecture: Read More [+]

Back-End Web Architecture: Read Less [-]

INFO 255 Privacy Engineering 3 Units

Terms offered: Spring 2024, Spring 2023 The course overviews a broad number of paradigms of privacy from a technical point of view. The course is designed to assist system engineers and information systems professionals in getting familiar with the subject of privacy engineering and train them in implementing those mechanisms. In addition, the course is designed to coach those professionals to critically think about the strengths and weaknesses of the different privacy paradigms. These skills are important for cybersecurity professionals and enable them to effectively incorporate privacy-awareness in the design phase of their products. Privacy Engineering: Read More [+]

Course Objectives: Critique the strengths and weaknesses of the different privacy paradigms Describe the different technical paradigms of privacy that are applicable for systems engineering Implement such privacy paradigms, and embed them in information systems during the design process and the implementation phase Stay updated about the state of the art in the field of privacy engineering

Credit Restrictions: Students will receive no credit for INFO 255 after completing INFO 255 . A deficient grade in INFO 255 may be removed by taking INFO 255 .

Privacy Engineering: Read Less [-]

INFO 256 Applied Natural Language Processing 3 Units

Terms offered: Fall 2024, Fall 2023, Fall 2021 This course examines the use of natural language processing as a set of methods for exploring and reasoning about text as data, focusing especially on the applied side of NLP — using existing NLP methods and libraries in Python in new and creative ways. Topics include part-of-speech tagging, shallow parsing, text classification, information extraction, incorporation of lexicons and ontologies into text analysis, and question answering. Students will apply and extend existing software tools to text-processing problems. Applied Natural Language Processing: Read More [+]

Prerequisites: INFO 206A and INFO 206B or proficient programming in Python (programs of at least 200 lines of code). Proficient with basic statistics and probabilities

Instructor: Bamman

Applied Natural Language Processing: Read Less [-]

INFO 258 Data Engineering 4 Units

Terms offered: Spring 2024, Fall 2022 This course will cover the principles and practices of managing data at scale, with a focus on use cases in data analysis and machine learning. We will cover the entire life cycle of data management and science, ranging from data preparation to exploration, visualization and analysis, to machine learning and collaboration, with a focus on ensuring reliable, scalable operationalization. ensuring reliable, scalable operationalization. Data Engineering: Read More [+]

Prerequisites: INFO 206B or equivalent college-level course in computer science in Python with a C- or better AND COMPSCI C100/ DATA C100 / STAT C100 or COMPSCI 189 or INFO 251 or DATA 144 or equivalent college-level course in data science with a C- or better

Instructors: Hellerstein, Parameswaran, Jain

Data Engineering: Read Less [-]

INFO 259 Natural Language Processing 4 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 This course introduces students to natural language processing and exposes them to the variety of methods available for reasoning about text in computational systems. NLP is deeply interdisciplinary, drawing on both linguistics and computer science, and helps drive much contemporary work in text analysis (as used in computational social science, the digital humanities, and computational journalism). We will focus on major algorithms used in NLP for various applications (part-of-speech tagging, parsing, coreference resolution, machine translation) and on the linguistic phenomena those algorithms attempt to model. Students will implement algorithms and create linguistically annotated data on which those algorithms depend. Natural Language Processing: Read More [+]

Prerequisites: Familiarity with data structures, algorithms, linear algebra, and probability

Natural Language Processing: Read Less [-]

INFO C262 Theory and Practice of Tangible User Interfaces 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 This course explores the theory and practice of Tangible User Interfaces, a new approach to Human Computer Interaction that focuses on the physical interaction with computational media. The topics covered in the course include theoretical framework, design examples, enabling technologies, and evaluation of Tangible User Interfaces. Students will design and develop experimental Tangible User Interfaces using physical computing prototyping tools and write a final project report. Theory and Practice of Tangible User Interfaces: Read More [+]

Instructor: Ryokai

Also listed as: NWMEDIA C262

Theory and Practice of Tangible User Interfaces: Read Less [-]

INFO C265 Interface Aesthetics 3 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 This course will cover new interface metaphors beyond desktops (e.g., for mobile devices, computationally enhanced environments, tangible user interfaces) but will also cover visual design basics (e.g., color, layout, typography, iconography) so that we have systematic and critical understanding of aesthetically engaging interfaces. Students will get a hands-on learning experience on these topics through course projects, design critiques , and discussions, in addition to lectures and readings. Interface Aesthetics: Read More [+]

Also listed as: NWMEDIA C265

Interface Aesthetics: Read Less [-]

INFO 271B Quantitative Research Methods for Information Systems and Management 3 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Introduction to many different types of quantitative research methods, with an emphasis on linking quantitative statistical techniques to real-world research methods. Introductory and intermediate topics include: defining research problems, theory testing, casual inference, probability, and univariate statistics. Research design and methodology topics include: primary/secondary survey data analysis, experimental designs, and coding qualitative data for quantitative analysis. Quantitative Research Methods for Information Systems and Management: Read More [+]

Prerequisites: Introductory statistics recommended

Quantitative Research Methods for Information Systems and Management: Read Less [-]

INFO 272 Qualitative Research Methods for Information Systems and Management 3 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Theory and practice of naturalistic inquiry. Grounded theory. Ethnographic methods including interviews, focus groups, naturalistic observation. Case studies. Analysis of qualitative data. Issues of validity and generalizability in qualitative research. Qualitative Research Methods for Information Systems and Management: Read More [+]

Instructor: Burrell

Qualitative Research Methods for Information Systems and Management: Read Less [-]

INFO 283 Information and Communications Technology for Development 3 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 This seminar reviews current literature and debates regarding Information and Communication Technologies and Development (ICTD). This is an interdisciplinary and practice-oriented field that draws on insights from economics, sociology, engineering, computer science, management, public health, etc. Information and Communications Technology for Development: Read More [+]

Fall and/or spring: 15 weeks - 3 hours of seminar per week

Additional Format: Three hours of seminar per week.

Instructor: Saxenian

Formerly known as: Information C283

Information and Communications Technology for Development: Read Less [-]

INFO 288 Big Data and Development 3 Units

Terms offered: Spring 2024, Spring 2021, Spring 2019 As new sources of digital data proliferate in developing economies, there is the exciting possibility that such data could be used to benefit the world’s poor. Through a careful reading of recent research and through hands-on analysis of large-scale datasets, this course introduces students to the opportunities and challenges for data-intensive approaches to international development. Students should be prepared to dissect, discuss, and replicate academic publications from several fields including development economics, machine learning, information science, and computational social science. Students will also conduct original statistical and computational analysis of real-world data. Big Data and Development: Read More [+]

Prerequisites: Students are expected to have prior graduate training in machine learning, econometrics, or a related field

Big Data and Development: Read Less [-]

INFO 289 Public Interest Cybersecurity: The Citizen Clinic Practicum 3 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 This course provides students with real-world experience assisting politically vulnerable organizations and persons around the world to develop and implement sound cybersecurity practices. In the classroom, students study basic theories and practices of digital security, intricacies of protecting largely under-resourced organizations, and tools needed to manage risk in complex political, sociological, legal, and ethical contexts. In the clinic , students work in teams supervised by Clinic staff to provide direct cybersecurity assistance to civil society organizations. We emphasize pragmatic, workable solutions that take into account the unique needs of each partner organization. Public Interest Cybersecurity: The Citizen Clinic Practicum: Read More [+]

Repeat rules: Course may be repeated for credit with instructor consent.

Public Interest Cybersecurity: The Citizen Clinic Practicum: Read Less [-]

INFO 290 Special Topics in Information 1 - 4 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 Specific topics, hours, and credit may vary from section to section, year to year. Special Topics in Information: Read More [+]

Repeat rules: Course may be repeated for credit when topic changes. Students may enroll in multiple sections of this course within the same semester.

Fall and/or spring: 8 weeks - 2-8 hours of lecture per week 15 weeks - 1-4 hours of lecture per week

Summer: 10 weeks - 1.5-6 hours of lecture per week

Additional Format: One to four hours of lecture per week. One and one-half to six hours of lecture per week for 10 weeks. Two to eight hours of lecture per week for 8 weeks.

Special Topics in Information: Read Less [-]

INFO 290M Special Topics in Management 1 - 4 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 Specific topics, hours, and credit may vary from section to section and year to year. Special Topics in Management: Read More [+]

Additional Format: One to four hours of lecture per week. Two to eight hours of lecture per week for 8 weeks.

Special Topics in Management: Read Less [-]

INFO 290S Special Topics in Social Science and Policy 2 - 4 Units

Terms offered: Fall 2024, Fall 2023, Spring 2023 Specific topics, hours, and credit may vary from section to section and year to year. Special Topics in Social Science and Policy: Read More [+]

Fall and/or spring: 8 weeks - 4-8 hours of lecture per week 15 weeks - 2-4 hours of lecture per week

Additional Format: Two to four hours of lecture per week. Four to eight hours of lecture per week for 8 weeks.

Special Topics in Social Science and Policy: Read Less [-]

INFO 290T Special Topics in Technology 2 - 4 Units

Terms offered: Spring 2024, Fall 2023, Spring 2023 Specific topics, hours, and credit may vary from section to section and year to year. Special Topics in Technology: Read More [+]

Special Topics in Technology: Read Less [-]

INFO 291 Special Topics in Information 1 - 4 Units

Terms offered: Prior to 2007 Specific topics, hours, and credit may vary from section to section, year to year. Special Topics in Information: Read More [+]

Repeat rules: Course may be repeated for credit when topic changes.

Fall and/or spring: 15 weeks - 1-4 hours of lecture per week

Additional Format: One to four hours of lecture per week.

Grading: Offered for satisfactory/unsatisfactory grade only.

Instructor: Hoofnagle

INFO 293 Information Management Practicum 0.5 Units

Terms offered: Fall 2016, Summer 2016 10 Week Session, Spring 2016 This course is designed to help School of Information graduate students maximize their internship, practicum, or independent research experiences. Information Management Practicum: Read More [+]

Course Objectives: Experience the practical application of your academic knowledge to real-world professional contexts; Gain insight into an organization and how one might make a valuable contribution; Reflect on the information the experience has provided, to see if it fits within one’s personal value set and work/life manifestos. Try out various professional activities to see when you are in ‘flow’;

Student Learning Outcomes: Assess the organizational culture of a company, governmental body, or non-governmental organization Connect academic knowledge about information management to real-world professional contexts Evaluate the effectiveness of a variety of information science techniques when deployed in organizational situations Integrate the student's own individual professional goals with the organization's needs relevant to the internship or practicum Reflect critically on the internship or practicum experience

Prerequisites: Consent of a Head Graduate Adviser for the School of Information

Repeat rules: Course may be repeated for credit without restriction.

Fall and/or spring: 15 weeks - 1 hour of internship per week

Summer: 10 weeks - 1.5 hours of internship per week

Additional Format: One hour of internship per week. One and one-half hours of internship per week for 10 weeks.

Information Management Practicum: Read Less [-]

INFO 294 Doctoral Research and Theory Workshop 2 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 An intensive weekly discussion of current and ongoing research by Ph.D. students with a research interest in issues of information (social, legal, technical, theoretical, etc.). Our goal is to focus on critiquing research problems, theories, and methodologies from multiple perspectives so that we can produce high-quality, publishable work in the interdisciplinary area of information research. Circulated material may include dissertation chapters , qualifying papers, article drafts, and/or new project ideas. We want to have critical and productive discussion, but above all else we want to make our work better: more interesting, more accessible, more rigorous, more theoretically grounded, and more like the stuff we enjoy reading. Doctoral Research and Theory Workshop: Read More [+]

Prerequisites: PhD students only

Fall and/or spring: 15 weeks - 2 hours of workshop per week

Additional Format: Two hours of workshop per week.

Doctoral Research and Theory Workshop: Read Less [-]

INFO 295 Doctoral Colloquium 1 Unit

Terms offered: Fall 2024, Fall 2023, Spring 2023 Colloquia, discussion and readings designed to introduce students to the range of interests of the school. Doctoral Colloquium: Read More [+]

Prerequisites: Ph.D. standing in the School of Information

Fall and/or spring: 15 weeks - 1 hour of colloquium per week

Additional Format: One hour of colloquium per week.

Doctoral Colloquium: Read Less [-]

INFO 296A Seminar 2 - 4 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 Topics in information management and systems and related fields. Specific topics vary from year to year. Seminar: Read More [+]

Prerequisites: Consent of instructor

Fall and/or spring: 15 weeks - 2-4 hours of seminar per week

Additional Format: Two to Four hours of Seminar per week for 15 weeks.

Seminar: Read Less [-]

INFO 298 Directed Group Study 1 - 4 Units

Terms offered: Fall 2019, Spring 2016, Fall 2015 Group projects on special topics in information management and systems. Directed Group Study: Read More [+]

Credit Restrictions: Students will receive no credit for INFO 298 after completing INFOSYS 298.

Fall and/or spring: 15 weeks - 1-4 hours of directed group study per week

Summer: 8 weeks - 1.5-7.5 hours of directed group study per week

Additional Format: One to four hours of directed group study per week. One and one-half to seven and one-half hours of directed group study per week for 8 weeks.

Directed Group Study: Read Less [-]

INFO 298A Directed Group Work on Final Project 1 - 4 Units

Terms offered: Spring 2022, Spring 2016, Spring 2015 The final project is designed to integrate the skills and concepts learned during the Information School Master's program and helps prepare students to compete in the job market. It provides experience in formulating and carrying out a sustained, coherent, and significant course of work resulting in a tangible work product; in project management, in presenting work in both written and oral form; and, when appropriate, in working in a multidisciplinary team. Projects may take the form of research papers or professionally-oriented applied work. Directed Group Work on Final Project: Read More [+]

Prerequisites: Consent of instructor. Course must be taken for a letter grade to fulfill degree requirements

Additional Format: One to four hours of directed group study per week.

Directed Group Work on Final Project: Read Less [-]

INFO 299 Individual Study 1 - 12 Units

Terms offered: Fall 2023, Summer 2016 8 Week Session, Spring 2016 Individual study of topics in information management and systems under faculty supervision. Individual Study: Read More [+]

Fall and/or spring: 15 weeks - 1-12 hours of independent study per week

Summer: 8 weeks - 2-22.5 hours of independent study per week

Additional Format: Format varies.

Individual Study: Read Less [-]

INFO 375 Teaching Assistance Practicum 2 Units

Terms offered: Spring 2024, Fall 2021, Fall 2020 Discussion, reading, preparation, and practical experience under faculty supervision in the teaching of specific topics within information management and systems. Does not count toward a degree. Teaching Assistance Practicum: Read More [+]

Fall and/or spring: 15 weeks - 2 hours of lecture per week

Additional Format: Two hours of lecture per week.

Subject/Course Level: Information/Professional course for teachers or prospective teachers

Instructor: Duguid

Teaching Assistance Practicum: Read Less [-]

Contact Information

School of information.

102 South Hall

Phone: 510-642-1464

Senior Director of Student Affairs

Siu Yung Wong

[email protected]

Senior Director of Admissions

Julia Sprague

[email protected]

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phd research topics for information systems

Information Systems

This program consists of six three-hour major courses:

  • A fundamental research seminar (S600 IS Research Seminar)
  • Four topics-based seminars
  • One elective course

In addition to the major, information systems doctoral students are required to choose a minor field (normally 3 to 4 courses, or 9 to 12 credit hours). Most students in this area choose minors such as organizational behavior, cognitive psychology, social psychology, and management. Students without prior teaching experience also take one short course ( X630 ) on teaching, prior to teaching their first course, which may be before or after the comprehensive exam.

All information systems doctoral students are required to take S600 in the first semester of the program. S600 is a prerequisite for the topics-based courses and provides the fundamental background in information systems. The topics-based courses are organized around major research topic areas in information systems research. The elective course could be another information systems major course, an independent study course, an IS-related course in another discipline, or a research methods course.

Required Courses

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Additional resources

Information Systems PhD Specialization

General information.

The Department of Information Systems & Operations Management (ISOM) supports two areas for doctoral study: Information Systems (IS) and Operations Management (OM). Both areas are designed for persons seeking academic and research careers.

The area of Information Systems deals with the management of development, use, and impact of information systems and technologies in organizations. It is an interdisciplinary area, combining the study of information technologies and systems with other areas such as economics, operations research, decision theory, and psychology. Information systems have impact on all aspects of a modern organization — from providing solution to current problems to new business models and opportunities. With the rapid growth and globalization of businesses, information systems have taken on a more important role.

Department web site Information Systems Faculty

Admission Requirements

Applicants must have completed an undergraduate degree at an accredited university and should have a reasonable training in mathematics and economics. An admission committee of faculty members in the Information Systems & Operations Management Department reviews all completed applications. While the committee considers all relevant factors in its recommendations, important factors include past academic performance, GMAT scores (which are usually above 650 for successful applicants), and previous work experience. The GRE exam can be substituted for the GMAT but the GMAT is strongly preferred. In some cases we may request a personal interview.

Recommended Preparation Prior to Entry

It is assumed that students entering the information systems area are knowledgeable in advanced calculus, linear algebra, basic statistics, and a high level programming language. Any student who is deficient in these areas should consider taking appropriate coursework prior to entering the program.

Information Systems Area Faculty Coordinator

Asst. Prof. Mingwen Yang, Information Systems Area Faculty Coordinator, would be glad to answer your questions. Contact her by email at [email protected] .

Student Advising

The Department’s Doctoral Review Committee will guide new students until they establish a Supervisory Committee. Students are required to establish a Supervisory Committee by the end of their first year. The Supervisory Committee assists the student in choosing appropriate courses, approves course of studies, and monitors the student’s progress.

Course Requirements for Information Systems Major

The following courses are required for the IS major area. The number of credits for each course is indicated in parentheses after the course number.

I S 580 (4) Advanced Research Topics in Information Systems I
I S 581 (4) Advanced Research Topics in Information Systems II
I S 582 (4) Advanced Research Topics in Information Systems III
OPMGT 587 (4) Advances Topics in Inventory Management
OPMGT 590 (4) Stochastic Models for Business
QMETH 592 (4) Stochastic Models: Queueing and Simulation
I S 599 (1,1,1) Doctoral Seminar

All IS students must enroll in the doctoral seminar (IS 599) until all coursework is completed and the IS area examination is successfully completed; after this milestone, we strongly encourage all students to continue participating in the doctoral seminar.

Additionally, the following are strongly recommended courses for IS majors.

QMETH 580 (4) Mathematical programming
CSE 546 (4) Machine Learning
CSE 417 (4) Natural Language Processing

Research Methods Minor Area Requirements All students majoring in Information Systems must select Research Methods as one of their minor areas. The Research Methods area is designed to ensure that all students are knowledgeable with research tools needed to conduct high-level research in Information Systems.

The requirements below are viewed as minimal preparation for conducting doctoral level research; we strongly recommend that students expand their research methods area beyond the courses listed below. Certain substitutions of courses, upon approval from the chair of the supervisory committee may be allowed.

Microeconomics

ECON 500 (4) Microeconomic Analysis I
ECON 501 (4) Microeconomic Analysis II
ECON 508 (4) Microeconomic Analysis III

Econometrics

ECON 580 (4) Econometrics I
ECON 581 (4) Econometrics II
ECON 582 (4) Econometrics III

Other Minor Area Requirements In addition to Research Methods, IS students must select one additional minor area depending on the student’s interest. Possible minor areas include:

  • Computer Science
  • Economics or Business Economics
  • Mathematics
  • Operations Management

A Typical Course Schedule

Assuming adequate background preparation, students are expected to complete the following coursework in their first and second years. The normal schedule is as follows but course offerings and quarter offerings might change depending on faculty availability.

Autumn Winter Spring Summer
ECON 500 Microeconomics I ECON 501 Microeconomics II ECON 508

Microeconomics III

Internship / Independent Research
ECON 580

Econometrics I

ECON 581

Econometrics II

ECON 582

Econometrics III

OPMGT 590

Stochastic Model

QMETH 580

Math Programming

QMETH 592

Queuing Theory

IS 599

Doctoral Seminar

IS 599

Doctoral Seminar

IS 599

Doctoral Seminar

Teaching Effectiveness Seminar

Second Year

Autumn Winter Spring Summer
IS 580

Ad. Research Topics I

IS 581

Ad. Research Topics II

IS 582

Ad. Research Topics III

Area Exam
OPMGT 587

Topics in Inventory Management

Elective Elective Second year paper
Elective Elective Elective
IS 599

Doctoral Seminar

IS 599

Doctoral Seminar

IS 599

Doctoral Seminar

Course Requirements for Information Systems Minor

Students who select Information Systems as a minor area must take all three courses in Group I and two courses from Group II.

Group I. MBA level courses:

I S 545 (4) Database Management
I S 560 (4) Information Systems Development
I S 570 (4) Business Data Communications

If an MBA course in the above list is not offered, students may take a corresponding undergraduate course with permission.

Group II. Doctoral level courses:

IS 580 (4) Advanced Research Topics I
IS 581 (4) Advanced Research Topics II
IS 582 (4) Finance Research Workshop

Other Requirements

Written Area or Qualifying Examination After completing all coursework in his or her major area, each student will take a written area examination consisting of questions contributed by all appropriate area faculty and administered by the chair of the student’s Supervisory Committee. The exam is graded on a high pass, pass, low pass, or fail basis; if appropriate, the departmental faculty members in the Supervisory Committee may require additional work and/or classes as a condition for passing the exam. If the student fails the exam, he or she can take the exam one additional time after satisfying deficiencies.

Second Year Paper At the end of the second year, in order to demonstrate competency and ability to conduct research in IS, each student is required to write a paper. The work is to be supervised by the chair of the student’s Supervisory Committee and then graded by the departmental faculty members in the student’s Supervisory Committee on a high pass, pass, low pass, or fail basis. The departmental faculty members in the Supervisory Committee may require additional work as a condition for passing the paper.

General Examination After successfully completing the written area exam, each student takes a general (oral) examination. Members of the Supervisory Committee which includes a representative of the Graduate School and any other interested faculty and students, administer this examination. Typically, this exam involves a defense of the student’s dissertation proposal; however, the chair of the Supervisory Committee determines the precise format of the general exam.

Dissertation After successfully completing the general examination, the student is admitted to Candidacy and continues work on his/her dissertation research. A Reading Committee guides the student in working with the dissertation. It is also expected that the student will present his or her research to the Information Systems and Operations Management Department at the doctoral seminar.

Final Examination When the dissertation is completed, the Supervisory Committee administers a final defense of the dissertation.

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Areas of Research

Ph.D. in Information Technology Management Research Topics

Current Ph.D. faculty research programs span a wide range of technology management topics. Examples include:

  • Analytics in organizations and social media
  • Organizational diffusion, adoption and use of information technologies
  • Role of routines and organizational work processes in IT-mediated transformations
  • The roles of trust in the adoption of new technology and sharing economy services
  • Organizational capabilities, structures and skills for leveraging IT value
  • Impacts of IT investments on customer relationship, supply chain and knowledge management
  • IT capabilities and the dynamics of competitive action

Publications

Our doctoral students work with faculty members on many intriguing topics. Here are some research projects involving faculty and either current Ph.D. students or graduates:

“Understanding User Participation in Crowdsourced Mobile Apps: A Geo-Spatial Analysis” ( Tae Hun Kim, graduated 2018 ) “Dynamics of Online Word of Mouth Spillover Effects” ( Yen-Yao Wang, graduated 2017 ) “The Effect of Mergers and Acquisitions on Firm Performance: Evidence from Digital Industries” ( Kangkang Qi, graduated 2016 ) “Community Engagement and Collective Evaluation in Crowdfunding” ( Eun Ju Jung, graduated 2015 ) “A Process Theory of Technology Trust Change” ( Peng Liu, graduated 2013 ) “Technology, Humanness and Trust: Rethinking Trust in Technology” ( John Tripp, graduated 2012 ) “The (N)Ever-Changing World: Stability and Change in Organizational Routines” ( Derek Hillison, graduated 2009 ) “How Peripheral Developers Contribute to Open-Source Software Development” ( Pankaj Setia, graduated 2008 ) “Team Documentation Influences Clinic Complexity and Patient Satisfaction” ( Inkyu Kim and Dr. Brian Pentland )

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Distinguish your education

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PhD in IS Program Flyer    Meet Current Students    Explore FAQs

Doctoral students walk and talk.

PhD in Business Administration with a Specialization in Information Systems

You are here.

The Information Systems (IS) doctoral program is a research-based program where students work with world-renowned scholars to build skills that will prepare them for impactful careers as professors in information systems at business schools.

Overall, the doctoral program places a heavy emphasis on training students through active engagement in the research process. Students develop a strong foundation in research methods and statistics, while closely collaborating with multiple faculty members on research projects.

General details about the curriculum, requirements, and structure of the  program can be found here . Please be aware this document is not an exhaustive list of the requirements for the program.

Program Faculty

Our renowned, award-winning IS faculty are published experts on topics like:

Artificial Intelligence and Deep Learning

Cybersecurity, Text Analytics and Face Recognition

Emerging Digital Technologies

Large Language Models (LLMs)

Machine Learning and Semantic/Lexical Analytics

Natural Language Processing

Tools and Methods for Making Sense of Large Data

Jason Thatcher

Jason Bennett Thatcher

Tandean Rustandy Esteemed Endowed Chair

David Dobolyi

David Dobolyi

Assistant Professor

Abram Handler

Abram Handler

Kai Larsen

Associate Professor

Ramiro Montealegre

Ramiro Montealegre

Zhiyi Wang

Assistant Professor • Information Analytics PhD Program Director

Program Graduates

The PhD program prepares students to be researchers and teachers at major universities. See where our graduates started their careers and published research.

  • Publications
  • Aakash Saxena PhD: 2020 Placement: Sykes Enterprises, Inc. Dissertation: A Method to Extract Context-Sensitive Semantics of a Concept Using Word-Embedding Space and Its Application  
  • Jaebong Son  PhD: 2017 Placement - California State University Dissertation: What Have We Missed When Examining Twitter as a Communication Medium during Disasters  
  • Jeffrey Sweeney  PhD: 2016 Placement: Erasmus University Dissertation: On Value Creation from Knowledge Management Systems  
  • Jose Ramirez  PhD: 2015 Placement: United States Military Academy at West Point Dissertation: Essays on Military and Civilian Manpower Planning  
  • Mark Zais  PhD: 2014 Placement: Office of the Secretary of Defense Dissertation: Simulation-Optimization, Markov Chain and Graph Coloring Approaches to Military Manpower Modeling and Deployment Sourcing  
  • Subhamoy Ganguly PhD: 2013 Placement: Indian Institute of Management – Udaipur Dissertation: Essays in Scheduling: Applications in Health Care and Manufacturing  
  • Jingjing Li  PhD: 2013 Placement: University of Virginia Dissertation: Addressing Information Proliferation: Applications of Information Extraction and Text Mining  
  • Michele Samorani  PhD: 2012 Placement: University of Alberta, Canada Dissertation: Data Mining For Enhanced Operations Management Decision Making: Applications in Health Care  
  • Tomasz Miaskiewicz  PhD: 2010 Initial Placement: The New University of Lisbon Current Placement: NOVA School of Business and Economics Dissertation: Bridging the Gap Between Consumers and Designers: The Role of Accurate and Effective Personas  
  • Harald Reinertsen  PhD: 2010 Placement: Smith Stål Øst AS, Norway Dissertation: Optimization of the Industrial Cutting Stock Problems – Production Scheduling in a Dynamic Stochastic Environment  
  • Zainab AlQenaei  PhD: 2009 Placement: Kuwait University Dissertation: An Investigation of the Relationship Between Consumer Mental Health Recovery Measures and Clinicians’ Reports Using Multivariate Analysis of the Singular Value Decomposition of a Textual Corpus  
  • Marco Better  PhD: 2007 Placement: OptTek Systems, Inc. Dissertation: Data Mining Techniques for Prediction in Discrete Data Applications  
  • Fang Liang  PhD: 2007 Placement: PROS Pricing Solutions Dissertation: The Hyperplan-Based Classification Techniques  
  • Dirk Hovorka PhD: 2006 Placement: Bond University, Australia Dissertation: Information Systems Foundations: Four Research Essays  
  • Rahul Patil  PhD: 2006 Placement: Indian Institute of Management Dissertation: Improved Techniques for Due Date Quotation in Realistic Production Environments  
  • Emilio Collar  PhD: 2005 Placement: Western Connecticut State University Dissertation: An Investigation of Programming Code Textbase Readability Based on a Cognitive Readability Model  
  • Younghwa Lee  PhD: 2005 Placement: University of Kansas Dissertation: Developing Theoretical Models of Website Usability  
  • Mark W.S. Chun  PhD: 2003 Placement: Pepperdine University Dissertation: Embedded Knowledge, Embedded Information Systems: A Resource-Based Perspective on the System Integration Process During a Corporate Merger

Management Information Systems Quarterly   ( WITS 2016 Best Prototype Award ) Unlocking Knowledge Inheritance of Behavioral Research: A Design Framework and an Instantiation (Conditional acceptance) Jingjing Li—University of Virginia (PhD 2013) Kai Larsen –University of Colorado Boulder Ahmed Abbasi –University of Virginia

Information Systems Research Don’t Mention It? Analyzing User-generated Content Signals for Early Adverse Event Warnings   (2019) Ahmed Abbasi –University of Virginia Jingjing Li—University of Virginia (PhD 2013) Donald Adjeroh –West Virginia University Marie Abate—West Virginia University Wanhong Zheng –West Virginia University

MIS Quarterly      Information technology use as a learning mechanism: The impact of it  use on knowledge transfer effectiveness, absorptive capacity, and franchisee performance (2015) Kishen Iyengar - University of Colorado at Boulder Jeffrey R Sweeney - Maastricht University (PhD 2016) Ramiro Montealegre - University of Colorado at Boulder

MIS Quarterly      Can online wait be managed? The effect of filler interfaces and presentation modes on perceived waiting time online (2012) Younghwa Lee - University of Northern Iowa (PhD 2005) Andrew N.K. Chen - University of Kansas Virgina Ilie - California Luthern University

Learn more about

Research requirements

Teaching Requirements

PhD Research Topics in Information Technology

PhD Research Topics in Information Technology is our prime research service. Moreover, we will offer ‘ top grade guidance’  for all future PhD/MS scholars. We also help you to achieve greater heights in your career by considering all the things.

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PhD research topics in Information technology  has brought the  “world into our palm with advanced techs.” For the moment, it SOUNDS GOOD IN THE MODERN RESEARCH WORLD. Hence, we fork out all technical info related to new techs. IT maximizes the digital vision in  “data process, analyze, retrieval and control.”

Innovative PhD Research Topics in Information Technology

Top Two Demanding Areas for Research Topics in Information Technology

Distributed grid system.

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Edge and Fog Computing

  • SDN Communication
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We all know that today’s society shares a huge volume of data. This vast data processing takes place through AI methodologies like ML, DL, and so on. Get in touch with us to know more in detail about PhD research topics in information technology .

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We have experts in all parts of the world. So, it enables us to achieve the  “global level newness”  in your research. In the light of our experts, you can ‘SPARKLE IN YOUR RESEARCH REALM.’ For your benefit, we offer our  mentor support  online and offline. By the by, you can use this to clarify all your doubts in the research process. In other words, we give  “inch by inch assist”  in your study to make you clear.

Other amazing topics from PhD Research Topics in Information Technology are grouped together for you,

An effective process of Memory Leakage-Resilient Dynamic and Verifiable Multi-keyword Ranked Search based on Encrypted Smart BSND merthod

A framework process of Intelligent Home Security Monitoring System Based on Android system

An original mechanism for  Dynamic Degenerative Neural Network used Classification of Images by Live Network Data

An inventive process of Deep Abstraction and Weighted Feature Selection intended for Wi-Fi Impersonation Detection

An effeicent mechanism for Anomaly Detection of Cyber Physical Network Data used by 2D Images

An inventive research mechanism for Data Fusion based on Predicting Novel Activity in Enterprise Cyber-Security system

An effectual functkion of Detecting Hidden User Behavior for Network Data Stream method

The novel technology for  Rapid Malware Analysis and Reverse Engineering by Visual Analytics

An effective mechanism for Identification of heavy hitters for network data streams with probabilistic sketch scheme

An kinnovative technique for Detection of super nodes based on connection metrics for network data streams

A fresh competent process of fast multi-pattern matching algorithm for mining big network data

A design and development of new research based LoRa network for cities Private and complete secured by information system

A novel technology for Attacker Behavior-Based Metric for Security Monitoring Applied to Darknet Analysis scheme

Design methodology function of Security Analytics forWBAN/WLAN Healthcare Network

An effective mechanism for Evaluation and Cluster Analysis of E-Businesses with Perishable Products and Cold Supply Chain

An innovative function of Security in Modern Smart Cities by Information Technology Perspective

Design a new methodology of Application in Natural Language Processing in Machine Translation method

An effective process of Enhancing Cloud-Based on IoT Security through Trustworthy Cloud Service

An effective function of Integrated Cloud Storage based on Paperless Thesis Examination by information technology

Effectual process of Research for Security Situational Awareness and Visualization Approach in Cloud Computing

PhD Research Topics in Information Technology

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The objectives of the PhD specialization in Information Systems (IS) are to promote theoretical and applied research on topics related to information systems practice with a combined focus on scientific rigour and relevance. 

Faculty in the IS Area conduct research on diverse topics including the business value and impacts of information technology, managing resistance to information systems implementation, Coordination in fast response organizations, Knowledge management, management of information security, predictive analytics, adoption and impact of healthcare IT, online search and advertising, and social media, by using a variety of methodologies. IS faculty members are funded by external agencies and also enjoy a fruitful working relationship with the business community that provides a rich environment for field research. 

Current Area Research Topics

  • The Societal and Economic Impacts of Information Systems
  • AI and Society/Work Intersections
  • Technology and Labor Transformation
  • Online Platforms and Gig Economy
  • Development of qualitative, interpretive, and case methodologies for use in research on how organizations use information technology
  • Understanding and managing resistance to change associated with information systems implementation
  • IT and Behavioral Economics (i.e., Understanding individual biases caused or remedied by IT)
  • Healthcare and IT Intersections
  • Management of information security
  • IT innovation, IT championship, IT Entrepreneurship, and IT literacy

Typical Course Structure

Fall semester

INSY 704 Organizational Impacts of Information Technology MGSC 706 Management Research Statistics ECON 662 Econometrics I

Winter semester

INSY 706 IT Acceptance and Usage MGMT 709 Designing for Causal Inference ECON 663 Econometrics II

Second Year

INSY 709 IT & Digital Economy INSY 705 Seminar in IS - AI Applications in Information Systems Elective

INSY 705 Seminar in IS - Decision Making with IT EDPH 689 Teaching and Learning in Higher Education Elective

MGPO 704 Organizational Theory Seminar MGPO 705 Seminar in Policy MGPO 706 Perspectives on Innovation ORGB 708 Social Network Analysis for Social Science Research POLI 666 Causal Inference for Political Science ECON 706 Selected Topics ECON 742 Empirical Microeconomics MPHE 742 HEC80774411 Theory Building MPHE 743 HEC80109A Textual Analysis and Psychophysiological Measures MPHE 745 HEC80-724A Research Methods IT MPHE 746 HEC8062917A Machine Learning I: Large-Scale Data Analysis and Decision Making

Typical Support Fields

  • Strategy/Organizational Theory
  • Economics and Applied Economics
  • Computer Science

Information Systems Events

Phd thesis defense presentation:, related content.

Taha Havakhor

Prof. Taha Havakhor Information Systems Area PhD Representative

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Doctor of Philosophy in Business Administration in Management Information Systems (PhD)

Go to programs search

An active and collegial group of senior and junior academics is the distinguishing feature of the MIS faculty at UBC. These researchers investigate a wide variety of topics, including electronic commerce, economics of information systems, systems analysis, intelligent systems, strategic and organizational issues, and planning for MIS. Their methods include laboratory experiments, field studies, survey methods, econometrics, conceptual modelling, and computational simulation. This rich portfolio of topics and research methods allows substantial flexibility for our PhD students in selecting a research topic. 

For specific program requirements, please refer to the departmental program website

What makes the program unique?

The PhD Program in MIS in terms of its reputation, research record and research grants received, stands at the top of Canadian business schools and at the very top rank in the international arena. A recent assessment of research productivity showed that the UBC MIS program ranks 6th in the world among public universities, and 9th overall, in terms of publications in top MIS journals. 

Quick Facts

Program enquiries, admission information & requirements, 1) check eligibility, minimum academic requirements.

The Faculty of Graduate and Postdoctoral Studies establishes the minimum admission requirements common to all applicants, usually a minimum overall average in the B+ range (76% at UBC). The graduate program that you are applying to may have additional requirements. Please review the specific requirements for applicants with credentials from institutions in:

  • Canada or the United States
  • International countries other than the United States

Each program may set higher academic minimum requirements. Please review the program website carefully to understand the program requirements. Meeting the minimum requirements does not guarantee admission as it is a competitive process.

English Language Test

Applicants from a university outside Canada in which English is not the primary language of instruction must provide results of an English language proficiency examination as part of their application. Tests must have been taken within the last 24 months at the time of submission of your application.

Minimum requirements for the two most common English language proficiency tests to apply to this program are listed below:

TOEFL: Test of English as a Foreign Language - internet-based

Overall score requirement : 100

IELTS: International English Language Testing System

Overall score requirement : 7.0

Other Test Scores

Some programs require additional test scores such as the Graduate Record Examination (GRE) or the Graduate Management Test (GMAT). The requirements for this program are:

The GRE or a comparable test is required. Please check the program website.

2) Meet Deadlines

September 2025 intake, application open date, canadian applicants, international applicants, deadline explanations.

Deadline to submit online application. No changes can be made to the application after submission.

Deadline to upload scans of official transcripts through the applicant portal in support of a submitted application. Information for accessing the applicant portal will be provided after submitting an online application for admission.

Deadline for the referees identified in the application for admission to submit references. See Letters of Reference for more information.

3) Prepare Application

Transcripts.

All applicants have to submit transcripts from all past post-secondary study. Document submission requirements depend on whether your institution of study is within Canada or outside of Canada.

Letters of Reference

A minimum of three references are required for application to graduate programs at UBC. References should be requested from individuals who are prepared to provide a report on your academic ability and qualifications.

Statement of Interest

Many programs require a statement of interest , sometimes called a "statement of intent", "description of research interests" or something similar.

  • Supervision

Students in research-based programs usually require a faculty member to function as their thesis supervisor. Please follow the instructions provided by each program whether applicants should contact faculty members.

Instructions regarding thesis supervisor contact for Doctor of Philosophy in Business Administration in Management Information Systems (PhD)

Citizenship verification.

Permanent Residents of Canada must provide a clear photocopy of both sides of the Permanent Resident card.

4) Apply Online

All applicants must complete an online application form and pay the application fee to be considered for admission to UBC.

Tuition & Financial Support

FeesCanadian Citizen / Permanent Resident / Refugee / DiplomatInternational
$114.00$168.25
Tuition *
Installments per year33
Tuition $1,838.57$3,230.06
Tuition
(plus annual increase, usually 2%-5%)
$5,515.71$9,690.18
Int. Tuition Award (ITA) per year ( ) $3,200.00 (-)
Other Fees and Costs
(yearly)$1,116.60 (approx.)
Estimate your with our interactive tool in order to start developing a financial plan for your graduate studies.

Financial Support

Applicants to UBC have access to a variety of funding options, including merit-based (i.e. based on your academic performance) and need-based (i.e. based on your financial situation) opportunities.

Program Funding Packages

We provide a financial package that includes tuition plus $30,000 per year for the first five years of the PhD Program.

Average Funding

  • 2 students received Teaching Assistantships. Average TA funding based on 2 students was $4,335.
  • 4 students received Research Assistantships. Average RA funding based on 4 students was $25,426.
  • 5 students received internal awards. Average internal award funding based on 5 students was $16,505.

Scholarships & awards (merit-based funding)

All applicants are encouraged to review the awards listing to identify potential opportunities to fund their graduate education. The database lists merit-based scholarships and awards and allows for filtering by various criteria, such as domestic vs. international or degree level.

Graduate Research Assistantships (GRA)

Many professors are able to provide Research Assistantships (GRA) from their research grants to support full-time graduate students studying under their supervision. The duties constitute part of the student's graduate degree requirements. A Graduate Research Assistantship is considered a form of fellowship for a period of graduate study and is therefore not covered by a collective agreement. Stipends vary widely, and are dependent on the field of study and the type of research grant from which the assistantship is being funded.

Graduate Teaching Assistantships (GTA)

Graduate programs may have Teaching Assistantships available for registered full-time graduate students. Full teaching assistantships involve 12 hours work per week in preparation, lecturing, or laboratory instruction although many graduate programs offer partial TA appointments at less than 12 hours per week. Teaching assistantship rates are set by collective bargaining between the University and the Teaching Assistants' Union .

Graduate Academic Assistantships (GAA)

Academic Assistantships are employment opportunities to perform work that is relevant to the university or to an individual faculty member, but not to support the student’s graduate research and thesis. Wages are considered regular earnings and when paid monthly, include vacation pay.

Financial aid (need-based funding)

Canadian and US applicants may qualify for governmental loans to finance their studies. Please review eligibility and types of loans .

All students may be able to access private sector or bank loans.

Foreign government scholarships

Many foreign governments provide support to their citizens in pursuing education abroad. International applicants should check the various governmental resources in their home country, such as the Department of Education, for available scholarships.

Working while studying

The possibility to pursue work to supplement income may depend on the demands the program has on students. It should be carefully weighed if work leads to prolonged program durations or whether work placements can be meaningfully embedded into a program.

International students enrolled as full-time students with a valid study permit can work on campus for unlimited hours and work off-campus for no more than 20 hours a week.

A good starting point to explore student jobs is the UBC Work Learn program or a Co-Op placement .

Tax credits and RRSP withdrawals

Students with taxable income in Canada may be able to claim federal or provincial tax credits.

Canadian residents with RRSP accounts may be able to use the Lifelong Learning Plan (LLP) which allows students to withdraw amounts from their registered retirement savings plan (RRSPs) to finance full-time training or education for themselves or their partner.

Please review Filing taxes in Canada on the student services website for more information.

Cost Estimator

Applicants have access to the cost estimator to develop a financial plan that takes into account various income sources and expenses.

Career Outcomes

102 students graduated between 2005 and 2013. Of these, career information was obtained for 100 alumni (based on research conducted between Feb-May 2016):

phd research topics for information systems

Sample Employers in Higher Education

Sample employers outside higher education, sample job titles outside higher education, phd career outcome survey, career options.

Our graduates have academic positions in Canada, the USA, Asia, and Europe. Students in our program work closely with faculty and other students in a stimulating intellectual environment to create outstanding research. 

Alumni on Success

phd research topics for information systems

Job Title Assistant professor

Employer Kuwait University

Enrolment, Duration & Other Stats

These statistics show data for the Doctor of Philosophy in Business Administration in Management Information Systems (PhD). Data are separated for each degree program combination. You may view data for other degree options in the respective program profile.

ENROLMENT DATA

 20232022202120202019
Applications2726363026
Offers20153
New Registrations20131
Total Enrolment76767

Completion Rates & Times

  • Research Supervisors

This list shows faculty members with full supervisory privileges who are affiliated with this program. It is not a comprehensive list of all potential supervisors as faculty from other programs or faculty members without full supervisory privileges can request approvals to supervise graduate students in this program.

  • Cavusoglu, Hasan (IT investment, information security, value of information technology, online product differentiation, information system security, Economics & sustainability, Management Information Systems)
  • Cenfetelli, Ronald Timothy (Human computer interaction, E-Business, IT-mediated customer service, Negative aspects of technology, Structural equation modeling, Survey research techniques, Multi-level modeling)
  • Chamberlain, Sandra (Accounting and the economics of, Valuation of publicly traded firms using accounting information, Accounting and contracting, Earnings quality, Accounting for Financial Institutions)
  • D Adduzio, Jenna (Voluntary and mandatory disclosures; Regulatory and standard setting issues; Disclosure materiality)
  • Lee, Gene (Economics and business administration; Management information systems; Applied Machine Learning; Business Analytics; Computer Science and Statistics; Cybersecurity; Information Systems; Mobile Ecosystem; Social Media Analysis; Text Mining)
  • Li, J. Frank
  • Lo, Kin (financial reports; financial disclosures; financial statements; accounting; stock options; executive compensation; auditing; stock valuation; securities regulation; tax planning; tax policy, Empirical research in financial accounting and reporting, Investigating the motives and effects of voluntary disclosures, The effects of alternative regulated reporting regimes, Refinement of accounting research methodology, Examining the role of accounting in equity valuation)
  • Lundholm, Russell (Financial statement analysis )
  • Nan, Ning (Economics and business administration; Management information systems; blockchain governance; complex adaptive systems; digital business; evolvable information systems; Information Systems; Management; online community)
  • Vijayaraghavan, Rajesh (Accounting and risk management in financial institutions, Disclosure, Corporate governance and performance measurement, Corporate Finance, Applications of machine learning)
  • Wu, Joy (Behavioral experiments, Data privacy, Information markets, Fairness Views, Platform strategy)
  • Yeung, Ira (Voluntary Disclosure; Information Quality; Financial Institutions)
  • Zhang, Jenny Li (Accounting; Economics and business administration; Management information systems; Finance and Accounting; Financial reporting, corporate disclosure, restatements, pension)
  • Zheng, Xin (SEC Enforcement, Securities Litigation, Auditing, Fraud, Regulation, Financial Reporting)
  • Zhou, Mi (Management information systems; Economics of Digitization; Digital Media Products; Digital Marketing; Omnichannel Customer Behavior)

Doctoral Citations

Year Citation
2016 Dr. Saghafi studied business information modelling. He investigated approaches that could improve the performance of users in business analytics, and enhance their overall understanding of the application requirements. His research has important implications for both researchers and practitioners in the area of information systems development.

Sample Thesis Submissions

  • Three essays on AI strategies and innovation

Related Programs

Same academic unit.

  • Doctor of Philosophy in Business Administration in Accounting (PhD)
  • Doctor of Philosophy in Business Administration in Finance (PhD)
  • Doctor of Philosophy in Business Administration in Management Science (PhD)
  • Doctor of Philosophy in Business Administration in Marketing and Behavioural Science (PhD)
  • Doctor of Philosophy in Business Administration in Organizational Behaviour (PhD)
  • Doctor of Philosophy in Business Administration in Strategy and Business Economics (PhD)
  • Doctor of Philosophy in Business Administration in Transportation and Logistics (PhD)
  • Doctor of Philosophy in Business Administration in Urban Land Economics (PhD)
  • Master of Business Administration (MBA)
  • Master of Business Analytics (MBAN)
  • Master of Management (MM)
  • Master of Science in Business Administration in Finance (MSCB)
  • Master of Science in Business Administration in Management Information Systems (MSCB)
  • Master of Science in Business Administration in Transportation and Logistics (MSCB)
  • Professional Master of Business Administration (PMBA)

Further Information

Specialization.

Management Information Systems covers information on systems analysis, databases, telecommunications, electronic commerce, economics of information systems, intelligent systems, strategic and organizational issues, and planning for MIS.

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Program website, faculty overview, academic unit, program identifier, classification, social media channels, supervisor search.

Departments/Programs may update graduate degree program details through the Faculty & Staff portal. To update contact details for application inquiries, please use this form .

phd research topics for information systems

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Here, you can choose from more than 300 graduate degree program options and 2000+ research supervisors. You can even design your own program.

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Doctor of Philosophy (PhD) Information Systems and Technology Management

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How to apply

If you are interested in pursuing a PhD with the School of Information Systems and Technology Management (UNSW Business), then you will need to receive an Invitation to Apply from the school’s Postgraduate Research Coordinator in charge of Admissions ( Dr. Eric Lim ).

To receive an Invitation to Apply, you should 1) self-assess your eligibility, and 2) then submit an Expression of Interest (EOI) . Details of what to include in your EOI can be found here .

About our Higher Degree Research Program

UNSW’s Doctor of Philosophy (PhD) Information Systems & Technology Management (Program code 1525) will prepare you to become a globally focused and socially engaged research leader. You’ll join a cohort of high-achieving research students and benefit from interdisciplinary engagement with your cohort. This is your opportunity to become integrated into the UNSW Business School’s community of scholars.

You’ll first undertake rigorous coursework covering research methods and theoretical foundations of information systems and operations management. As part of the program, you’ll also be involved in research projects from an early stage. This research training will equip you with the skills required to identify, analyse and solve problems in the field.

You’ll then pursue full-time research under the supervision of high-profile UNSW academics, culminating in a doctoral thesis. There will also be exciting opportunities to develop your teaching portfolio. There will also be exciting opportunities throughout to interact with industry leaders. The training is geared toward preparing you for a career in academia, although other career paths (e.g., consulting, government, industry, non-profit) are also enabled.

Your PhD thesis will showcase your research skills and enable you to make an original contribution to knowledge in your field.

PhD program structure

Year one: master of pre-doctoral business studies (mpdbs).

The first year of study is designed to provide a rigorous foundation to conduct independent research. You’ll learn a range of methodologies and build your communication and presentation skills.

Your first year of coursework study will cover fundamental qualitative and quantitative research methods. You’ll also start developing key research skills such as identifying, framing, presenting, and writing academic contributions. You’ll work as a research assistant on faculty research projects and start working on your own research as your skills develop.

The first year will help you identify your potential thesis supervisor for the PhD component of the program. Upon successful completion of the first year, you’ll be awarded a Master of Pre-Doctoral Business Studies. A brief overview of the first year is presented below. Please visit the UNSW Handbook for full course structure details.

  • Program Core Course:  COMM8100
  • Stream Core Courses: INFS5987 + INFS5993
  • Program Core Course:  COMM8101  or  COMM8102
  • Stream Core Courses: COMM8001 + Research Assistance Work
  • Program Core Course:  COMM8103
  • Stream Core Courses: COMM8002 + COMM8003

Year Two: Specialised Coursework & Identification of Thesis Topic

During the second year, you’ll continue in the Information Systems & Technology Management stream with a further year of advanced coursework.

You’ll choose up to eight additional research courses from a range of electives in consultation with your supervisor and PGRC, with an opportunity to take several courses outside of your specialised discipline to prepare you for interdisciplinary collaborations.

Your second year is also when you’ll identify your thesis topic with your supervisor. You’ll engage in literature review and research design, and present your research proposal to the School at the end of the year.

Years Three – Four: Original Research & Your Doctoral Thesis

The final two years of the program are focused on conducting full-time research and completing your doctoral thesis. This is your opportunity to address some of the biggest challenges in Information Systems & Technology Management and make a significant contribution to knowledge in your field. Your research will offer new critical thinking and withstand critical analysis from expert researchers in the area.

As with most other OECD countries, the number of PhD completions in Australia has grown dramatically – more than doubling in the last two decades.

An increasing number of PhD graduates find employment in business, government, and the non-profit sector. Nineteen of the largest ASX companies have PhD graduates on their senior executive teams.

Your PhD thesis will set you on the path to a career in a premier research institution, anywhere in the world. Throughout your research degree, you’ll also have many opportunities to develop your teaching portfolio.

Beyond academia, there is also significant demand in the private and public sectors for people with deep knowledge and sound research and analytical skills*.

Whether you’re looking to pursue a career in academia, or take your research skills out to industry, a PhD in Information Systems & Technology Management from UNSW will get you there.

* Source:  2019 Advancing Australia’s Knowledge Economy Report

Degree type

Postgraduate Research

4 years full-time, 5-8 years part-time

Commencing terms

Term 1 – February

Program code

Delivery mode, domestic / international.

International

Useful links

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Student research

phd research topics for information systems

Scholarships

phd research topics for information systems

Student life

phd research topics for information systems

Find a research supervisor

phd research topics for information systems

Accreditation

AACSB Accredited

IMAGES

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