job performance literature review pdf

To read this content please select one of the options below:

Please note you do not have access to teaching notes, factors affecting job performance: an integrative review of literature.

Management Research Review

ISSN : 2040-8269

Article publication date: 10 October 2018

Issue publication date: 13 February 2019

Job performance is an important variable, which primarily affects outcomes at three levels: the micro level (i.e. the individual), the meso level (i.e. the group) and the macro level (i.e. the organisation). This paper aims to identify, analyse and synthesise factors that affect job performance.

Design/methodology/approach

Through an extensive integrative review of literature, this study identifies and classifies the factors that affect job performance. A synthesised model based on the schema of demands, resources and stressors is also developed.

The demands identified are grouped into physical, cognitive and affective. Stressors adversely affecting job performance are classified at an individual level, job level and family level. Finally, resources are classified at an individual level, job level, organisational level and social level.

Research limitations/implications

This review enhances the job demands-resources (JD-R) model to job demands-resources-stressors (JD-R-S) model by identifying a separate category of variables that are neither job demands nor resources, but still impede job performance.

Practical implications

The subgroups identified under demands, resources and stressors provide insights into job performance enhancement strategies, by changing, managing or optimising them.

Originality/value

This study helps in better understanding the factors that go on to impact job performance differentially, depending on the group to which they belong. It gives a holistic picture of factors affecting job performance, thereby integrating classifying and synthesising the vast literature on the topic.

• Job performance
• Job demands
• Organizational theory and behaviour
• Job demands-resources-stressors model

Acknowledgements

This paper is based on the FPM Thesis of the author. The author would like to thank his Thesis Advisory Committee members Prof. Manjari Singh, Prof. Biju Varkkey and Prof. Dileep Mavalankar. In addition the author appreciates the constructive comments from the thesis examiners Prof. T.V. Rao and Prof. Asha Kaul.

Pandey, J. (2019), "Factors affecting job performance: an integrative review of literature", Management Research Review , Vol. 42 No. 2, pp. 263-289. https://doi.org/10.1108/MRR-02-2018-0051

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited

Related articles

All feedback is valuable.

Please share your general feedback

Report an issue or find answers to frequently asked questions

Contact Customer Support

• DOI: 10.30560/jems.v3n3p14
• Corpus ID: 221697589

The Determinants of Employee’s Performance: A Literature Review

• Mohand Tuffaha
• Published 23 August 2020

Figures from this paper

15 Citations

The effect of psychological empowerment on employee performance, an empirical study on personal factors of employee engagement in bpo industry, perceived organizational support and career adaptability towards work performance: a literature review, the effect of digital leadership, information technology and digital competency on employee performance in the digital era: mediating role of job satisfaction, the impact of transformational leadership on employee performance: an intermediary function of organizational commitment and job satisfaction, driving performance through innovation: the roles of communication and competence in public sector employees, the effect of job satisfaction and organizational communication on employee performance at pt. x during the covid-19 pandemic, determinant of job performance among lower-level employees: a case in the central region of malaysia, the impact of digital transformation dimensions on the employees job performance: applying on four and five star hotels in the red sea governorate, the influence of training and development on employee performance in port of salalah in the sultanate of oman, 90 references, conceptual framework of corporate culture influenced on employees commitment to organization, the relationship between the enabling use of controls, employee empowerment, and performance, the impact of organizational culture on organizational performance: the mediating role of employee’s organizational commitment.

• Highly Influential

Knowledge processes and firm performance: the mediating effect of employee creativity

Team building, employee empowerment and employee competencies, employee perception of impact of knowledge management processes on public sector performance, determinants of employee engagement and their impact on employee performance, from knowledge management to organizational performance, innovation leadership: best-practice recommendations for promoting employee creativity, voice, and knowledge sharing, organizational culture and innovation performance in pakistan's software industry, related papers.

Showing 1 through 3 of 0 Related Papers

How management support systems affect job performance: a systematic literature review and research agenda

• Published: 11 July 2023

Cite this article

• Jan A. Kempkes   ORCID: orcid.org/0000-0002-7054-624X 1 ,
• Francesco Suprano   ORCID: orcid.org/0000-0001-5980-452X 1 &
• Andreas Wömpener 1  

427 Accesses

Explore all metrics

In recent years new digital technologies, such as self-service systems and big data analytics, have brought about important changes across all organizational divisions. However, although these technologies offer new features to support all kinds of business activities, they share some of the same characteristics as prior technologies and may therefore induce some of the same behavioral implications that have already been studied for different waves of technological innovations. Thus, to comprehensively assess whether employees’ job performance benefits from the adoption of digital technologies, research and practice should build upon the significant amount of knowledge that exists in different but related research fields. This study presents a systematic literature review of research examining the effects of management support systems (MSSs) on various facets of job performance. The review is guided by our conceptual framework that aims to facilitate the understanding of the MSS-job performance relation by integrating both mechanisms by which MSS effects can occur and variables that specify the form and/or magnitude of this relationship. Through this theoretical lens, we analyze 271 empirical articles published in leading academic journals between 1974 and 2019. Based on the synthesis of the vast body of empirical evidence, we critically reflect on the current state of knowledge and outline fruitful avenues for future research. We find that while especially task performance effects have received much attention in the literature, effects on behaviors going beyond the prescribed tasks of a job, such as employees’ willingness to exert effort, compliance, and knowledge acquisition, are still underrepresented.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Job flourishing research: A systematic literature review

Investigate the Effects of Behavioral Factors on Job Performance: A Conceptual Paper

Does public service motivation predict performance in public sector organizations? A longitudinal science mapping study

Explore related subjects.

• Artificial Intelligence

Data availability

Data are available upon request.

Note that the term MSS is inconsistently used in the literature. Although some researchers narrowly define MSSs as computer-based information systems combining the capabilities of different systems (e.g., Forgionne and Kohli 2000 ), the term has typically been applied more broadly to encompass all information systems that may be used to support managerial activities (e.g., Benbasat and Nault 1990 ; Clark et al. 2007 ; Gelderman 2002 ). Consistent with contemporary studies (e.g., Marakas and O’Brien 2011 ), we use the term MSS throughout the paper to refer to information systems that focus on providing information and support for effective managerial decision-making.

The term DSS was firstly introduced by Gorry and Scott Morton ( 1971 ). Originally, they conceived DSSs as systems supporting unstructured or semi-structured managerial decisions. However, Keen and Scott Morton ( 1978 ) later narrowed the definition to managerial decisions in semi-structured task domains (see Arnott and Pervan 2005 , for a review of the DSS history).

Arnott and Pervan ( 2005 ) term these systems data-oriented DSSs. However, their DSS definition is essentially the same as our MSS definition.

For an extensive comparison of these concepts, see Borman and Motowidlo ( 1993 ).

Accounting firms, for example, even develop their own MSSs, which provide the rationale behind their tax calculations and/or reference the tax code, in expectation that staff accountants learn through the use of these systems (Rose and Wolfe 2000 ).

For a discussion of the relevance of rule obedience in the context of job performance, see Conway ( 1999 ).

Production blocking occurs when group members cannot contribute their ideas because another group member is talking (Gallupe et al. 1992 ).

Social science termed this class of variables as specification variables. By definition, moderator variables are a subset of this class of variables. However, specification variables additionally include independent predictor variables. See Sharma et al. ( 1981 ) for a typology of specification variables.

This categorization is not only frequently used in other frameworks within accounting research (e.g., Bonner and Sprinkle 2002 ; Schnieder 2021 ) but also allows for a complete, yet parsimonious, consideration of numerous accounting-related variables potentially affecting the relationship between MSSs and job performance. Moreover, prominent studies within information system research focusing on the DSS-performance relation employ a similar categorization of variables (e.g., Eierman et al. 1995 ; Todd and Benbasat 1999 ).

Several studies within the accounting literature examining the effects of computer-based information systems on the decision-making process have assigned a key role to information system features by considering them as a distinct variable within their frameworks (e.g., Mauldin and Ruchala 1999 ; O’Donnell and David 2000 ; Rom and Rohde 2007 ).

Plenty of empirical evidence demonstrates that studies with statistically significant results are more likely to be published (e.g., Lindsay 1994 ; Pomeroy and Thornton 2008 ). Hence, due to this publication bias, literature reviews solely focusing on published articles may systematically overrepresent positive results. However, prior research suggests that relying on published articles is appropriate when the selected research area contains a large number of studies (e.g., Cooper 1989 ). Although, in such a case, the magnitude of the observed relation may be overestimated, the direction of the relation will probably be identified (David and Han 2004 ). Given that we are primarily interested in examining the direction of effects (see the preceding section), we are less concerned about the potentially negligible bias introduced by solely considering published articles in our review (see David and Han 2004 for a similar line of thought).

Relying on journal ratings as quality assessment criteria is common practice in systematic reviews in management research (Tranfield et al. 2003 ). Although the validity of any journal ranking is naturally debatable, the frequently used CABS ranking (e.g., Kienzler and Kowalkowski 2017 ; Saebi et al. 2019 ) is widely regarded as a dependable measure of research rigor and quality (Johnsen et al. 2017 ).

While some researchers (e.g., Power 2008b ) trace the roots of this research stream to Scott Morton ( 1967 ), others (e.g., Angehrn and Jelassi 1994 ) attribute its roots to Gorry and Scott Morton ( 1971 ). Although we did not limit our literature search to a specific time frame, we found no article published prior to 1967 using our search strategy. For a more comprehensive historical overview of the MSS research field, see Arnott and Pervan ( 2005 ).

To ensure that our database selection fits our search strategy, we analyzed the journal coverage of the selected databases prior to conducting the literature search. We note that our database selection covers 432 out of the 433 journals satisfying our CABS rating criteria (see Table 10 in the appendix). Although we would have covered the same total number of journals by using just Scopus and Web of Science, further analysis yielded that other databases provide better coverage of past volumes (see Panel A of Fig.  3 in the appendix). Hence, by utilizing ABI/INFORM Collection, EBSCO Business Source Complete, and JSTOR, we obtained exclusive access to past volumes of individual journals that are only covered by these databases (see Panel B of Fig.  3 in the appendix).

Note that asterisks (*) in the search terms act as wildcards accounting for variations in the root word (e.g., by using behavio*, the returned results will capture both behavior and behaviour).

While logical operators only ensure that the defined relationships between and within search term groups is maintained, proximity operators preserve the specified relationship within multiword search terms (Blumenthal-Barby and Krieger 2015 ).

Searching multiple databases increases the coverage of both unique and overlapping content (Hood and Wilson 2003 ). Thus, a large number of results is not uncommon in the first round of a systematic literature review (e.g., Bakker 2010 ; Calabrò et al. 2019 ; Dinh and Calabrò 2019 ).

The reference lists of the remaining 189 articles contain in total 9.565 references. After eliminating all articles that have already been excluded within the primarily selection process and those that did not fulfill our journal rating criteria, we screened the remaining articles using the above-described procedure.

We attempted to adjust our search terms to capture these articles systematically. However, it proved difficult to improve the output obtained from our current search term structure without additionally obtaining numerous irrelevant articles. This can mostly be attributed to the inconsistent use of terms in the literature. For example, the vague term decision aid is frequently used in the academic literature to refer to a range of both non-computerized decision aids (e.g., Spence and Brucks 1997 ) and computerized decision aids (e.g., Arnold 2018 ; Glover et al. 1997 ). While studies of the former category clearly deviate from the scope of our review, studies of the latter are essential for the comprehensiveness of our review and hence need to be included.

The coding guidelines are disclosed in full detail in Table 11 in the appendix.

Note that studies using primarily qualitative data, such as case and field studies, generally examine the relationship between variables through descriptive narrations, rather than statistical procedures (Panya and Nyarwath 2022 ). Thus, in the case of qualitative studies, our analysis is often based on the data interpretations of the author of the article, rather than on statistical results.

The assessment of the relative strength of agreement is based on the widely used nomenclature of Landis and Koch ( 1977 ).

While behavioral science has its roots in natural science and seeks to develop and verify theories that explain or predict organizational or human behavior, design science stems from the engineering discipline and seeks to solve identified organizational problems by creating and evaluating innovative information technology artifacts (Hevner et al. 2004 ). Although these two research directions represent two distinct paradigms, several researchers emphasize that it is inevitable to engage in a complementary research cycle between behavioral science and design science to address essential problems in the effective application of information technology (e.g., Hevner et al. 2004 ; March and Smith 1995 ).

This finding is in line with related published evidence (e.g., Arnott and Pervan 2014 ) noticing an overall decline in annual MSS publications.

As can be seen in Panel C, much of the overall decline in relevant articles can be attributed to the waning interest in GSSs (see Arnott and Pervan 2005 for a similar observation with regard to the entire MSS research stream).

Given that studies can be assigned to multiple job performance dimensions at once (e.g., when they measure task performance and knowledge acquisition at the same time), the sum of articles across all job performance dimensions exceeds the total number of articles reviewed.

Note that for reasons of consistency we will mostly use the term MSS throughout the following sections of this chapter although the authors may have referred to their system as one specific subset of a MSS (e.g., DSS, GSS, or BIS).

For more detailed information on each article examining the MSS-task performance relation (e.g., on method, specification variables, or findings), refer to Online Appendix A.

Although two completely different types of systems undoubtably differ in their degree of restrictiveness, it is often impossible to infer which system is the more restrictive (Silver 1988 ). Hence, we consider the comparison of system types as a distinct feature variable. In total, we identified 9 studies examining the impact of the type of system on individuals’ task performance.

Note that the terms skill and expertise are used synonymously in the literature (Reuber 1997 ). For reasons of consistency, we will use the term skill throughout this section although the authors may have referred to their person variable as expertise.

Experience is a commonly used surrogate to measure an individual’s level of skill (Bonner and Pennington 1991 ).

Although Davern and Kamis ( 2010 ) note that domain knowledge is a necessary but not sufficient requirement for being an expert, it still represents a proper subset of a person’s skill.

Cognitive style refers to the way individuals process information and make decisions (Mills 1996 ).

While the psychology literature utilizes the terms field dependent and field independent (e.g., Witkin et al. 1979 ), the managerial literature prefers the terms low-analytic and high-analytic (e.g., Benbasat and Dexter 1982 ). However, as there are no conceptual differences between these two term pairs, we will use the latter variant throughout this chapter although the authors may have used the former.

Given that the oversimplified dichotomy between simple and complex tasks cannot be applied to all types of tasks (McGrath 1984 ), we consider the comparison of different task types as a distinct task variable.

Note that we considered all studies that aim to shed light on the MSS-effort exertion relation regardless of their used effort measure. Consequently, we also included studies that use decision time as a surrogate for cognitive effort (e.g., Adelman et al. 1998 ; Sengupta 1995 ), even though decision time is also a commonly used measure of task performance (Mauldin and Ruchala 1999 ).

For more detailed information on each article examining the MSS-effort exertion relation (e.g., on method, specification variables, or findings), refer to Online Appendix B.

Note that Todd and Benbasat ( 2000 ) argue that the cognitive effort required to learn and use a MSS might affect the effort required to execute the strategies it supports and hence its overall impact on individuals’ effort exertion.

For more detailed information on each article examining the MSS-knowledge acquisition relation (e.g., on method, specification variables, or findings), refer to Online Appendix C.

Although Alavi et al. ( 2002 ) compare two different types of systems, they clearly state which one is more complex and hence less restrictive.

Rose and Wolfe ( 2000 ) as well as Rose ( 2005 ) show that MSSs imposing high levels of cognitive load reduce knowledge acquisition. We therefore assume that technologies fueled with big data potentially induce similar effects.

For an in-depth discussion of collaborative and socioemotional group interactions, see Rogat and Linnenbrink-Garcia ( 2011 ).

For more detailed information on each article examining the MSS-employee interaction (e.g., on method, specification variables, or findings), refer to Online Appendix D.

There are only two exceptions. Ghasemaghaei ( 2019 ) examines how the use of BISs affects knowledge sharing within firms, while Udo and Guimaraes ( 1994 ) examine in a field study how DSS usage affects communications within an organization.

While influence behavior is defined as all individual actions that attempt to affect the course of group behavior, influence distribution is defined as the within-group variance of influence behavior (Zigurs et al. 1988 ).

As we define compliance as the degree to which an individual’s behavior coincides with relevant laws, regulations, social norms or corporate policies, we did not include studies measuring compliance as the degree to which an individual obeys to a system’s recommendations (e.g., Madhavan and Phillips 2010 ; Wiczorek and Manzey 2014 ).

For more detailed information on each article examining the MSS-compliance relation (e.g., on method, specification variables, or findings), refer to Online Appendix E.

While precision denotes the possible cost range created by the system, accuracy denotes the probability that the estimated range contains the true costs (Abdel-Rahim and Stevens 2018 ).

For more detailed information on each article examining the MSS-innovativeness relation (e.g., on method, specification variables, or findings), refer to Online Appendix F.

Nominal groups are defined as groups in which individuals work in the presence of others but without interacting with them (Delbecq and Van de Ven 1971 ). Given that prior research shows that in contrast to interacting groups, nominal groups show a positive relationship between group size and the number of ideas generated, it is not surprising that it is the most widely used group type for group idea generation (Dowling and Louis 2000 ; Valacich et al. 1994 ).

Articles included in the literature review are marked with an asterisk

Abbasi A, Sarker S, Chiang RH (2016) Big data research in information systems: toward an inclusive research agenda. J Assoc Inf Syst 17:1–x

Google Scholar  

*Abdel-Rahim HY, Stevens DE (2018) Information system precision and honesty in managerial reporting: a re-examination of information asymmetry effects. Acc Organ Soc 64:31–43

Article   Google Scholar  

Abdolmohammadi M, Wright A (1987) An examination of the effects of experience and task complexity on audit judgments. Acc Rev 62:1–13

Adams R, Jeanrenaud S, Bessant J, Denyer D, Overy P (2016) Sustainability-oriented innovation: a systematic review. Int J Manag Rev 18:180–205

*Adelman L, Cohen MS, Bresnick TA, Chinnis JO Jr, Laskey KB (1993) Real-time expert system interfaces cognitive processes and task performance: an empirical assessment. Hum Factors 35:243–261

*Adelman L, Christian M, Gualtieri J, Johnson KL (1998) Examining the effects of cognitive consistency between training and displays. IEEE Trans Syst Man Cybern Part A 28:1–16

*Aiken M, Krosp J, Shirani A, Martin J (1994) Electronic brainstorming in small and large groups. Inf Manag 27:141–149

*Alavi M (1993) An assessment of electronic meeting systems in a corporate setting. Inf Manag 25:175–182

*Alavi M (1994) Computer-mediated collaborative learning: an empirical evaluation. MIS Q 18:159–174

*Alavi M, Wheeler BC, Valacich JS (1995) Using IT to reengineer business education: an exploratory investigation of collaborative telelearning. MIS Q 19:293–312

*Alavi M, Marakas GM, Yoo Y (2002) A comparative study of distributed learning environments on learning outcomes. Inf Syst Res 13:404–415

*Alge BJ, Wiethoff C, Klein HJ (2003) When does the medium matter? Knowledge-building experiences and opportunities in decision-making teams. Organ Behav Hum Decis Process 91:26–37

Al-Htaybat K, Alberti-Alhtaybat L (2017) Big data and corporate reporting: impacts and paradoxes. Acc Audit Acc J 30:850–873

Alpar P, Schulz M (2016) Self-service business intelligence. Bus Inf Syst Eng 58:151–155

*Althuizen N, Reichel A, Wierenga B (2012) Help that is not recognized: harmful neglect of decision support systems. Decis Support Syst 54:719–728

Anderson N, De Dreu CK, Nijstad BA (2004) The routinization of innovation research: a constructively critical review of the state-of-the-science. J Organ Behav 25:147–173

Angehrn AA, Jelassi T (1994) DSS research and practice in perspective. Decis Support Syst 12(4–5):267–275

*Anson R, Bostrom R, Wynne B (1995) An experiment assessing group support system and facilitator effects on meeting outcomes. Manag Sci 41:189–208

*Antony S, Santhanam R (2007) Could the use of a knowledge-based system lead to implicit learning? Decis Support Syst 43:141–151

*Antony S, Batra D, Santhanam R (2005) The use of a knowledge-based system in conceptual data modeling. Decis Support Syst 41:176–188

Arnold V (2006) Behavioral research opportunities: understanding the impact of enterprise systems. Int J Acc Inf Syst 7:7–17

Arnold V (2018) The changing technological environment and the future of behavioural research in accounting. Acc Financ 58:315–339

Arnold V, Sutton SG (1998) The theory of technology dominance: understanding the impact of intelligent decisions aids on decision makers’ judgements. Adv Acc Behav Res 1:175–194

*Arnold V, Sutton SG, Hayne SC, Smith CA (2000) Group decision making: the impact of opportunity-cost time pressure and group support systems. Behav Res Acc 12:69–96

Arnold V, Clark N, Collier PA, Leech SA, Sutton SG (2006) The differential use and effect of knowledge-based system explanations in novice and expert judgment decisions. MIS Q 30:79–97

*Arnott D, O’Donnell P (2008) A note on an experimental study of DSS and forecasting exponential growth. Decis Support Syst 45:180–186

Arnott D, Pervan G (2005) A critical analysis of decision support systems research. J Inf Technol 20:67–87

Arnott D, Pervan G (2014) A critical analysis of decision support systems research revisited: the rise of design science. J Inf Technol 29:269–293

Arnott D, Lizama F, Song Y (2017) Patterns of business intelligence systems use in organizations. Decis Support Syst 97:58–68

*Aron R, Dutta S, Janakiraman R, Pathak PA (2011) The impact of automation of systems on medical errors: evidence from field research. Inf Syst Res 22:429–446

Bakker RM (2010) Taking stock of temporary organizational forms: a systematic review and research agenda. Int J Manag Rev 12:466–486

*Bamber EM, Watson RT, Hill MC (1996) The effects of group support system technology on audit group decision making. Audit J Pract Theory 15:122–134

Bandura A (1991) Social cognitive theory of moral thought and action. In: Kurtines WM, Gewirtz JL (eds) Handbook of moral behavior and development. Erlbaum, Hillsdale, pp 45–103

*Barkhi R (2002) The effects of decision guidance and problem modeling on group decision-making. J Manag Inf Syst 18:259–282

*Barr SH, Sharda R (1997) Effectiveness of decision support systems: development or reliance effect? Decis Support Syst 21:133–146

*Benbasat I, Dexter AS (1982) Individual differences in the use of decision support aids. J Acc Res 20:1–11

*Benbasat I, Dexter AS (1985) An experimental evaluation of graphical and color-enhanced information presentation. Manag Sci 31:1348–1364

*Benbasat I, Dexter AS (1986) An investigation of the effectiveness of color and graphical information presentation under varying time constraints. MIS Q 10:59–83

Benbasat I, Nault BR (1990) An evaluation of empirical research in managerial support systems. Decis Support Syst 6:203–226

*Benbasat I, Schroeder RG (1977) An experimental investigation of some MIS design variables. MIS Q 1:37–49

*Benbunan-Fich R, Hiltz SR, Turoff M (2002) A comparative content analysis of face-to-face vs. asynchronous group decision making. Decis Support Syst 34:457–469

Bergmann M, Brück C, Knauer T, Schwering A (2020) Digitization of the budgeting process: determinants of the use of business analytics and its effect on satisfaction with the budgeting process. J Manag Control 31:25–54

*Bhandari G, Hassanein K, Deaves R (2008) Debiasing investors with decision support systems: an experimental investigation. Decis Support Syst 46:399–410

*Bhargava HK, Mishra AN (2014) Electronic medical records and physician productivity: evidence from panel data analysis. Manag Sci 60:2543–2562

Bhimani A, Willcocks L (2014) Digitisation ‘Big Data’ and the transformation of accounting information. Acc Bus Res 44:469–490

Bible L, Graham L, Rosman A (2005) The effect of electronic audit environments on performance. J Acc Audit Financ 20:27–42

Birnberg JG (2011) A proposed framework for behavioral accounting research. Behav Res Acc 23:1–43

Bloomfield R, Nelson MW, Soltes E (2016) Gathering data for archival field survey and experimental accounting research. J Acc Res 54:341–395

Blumenthal-Barby JS, Krieger H (2015) Cognitive biases and heuristics in medical decision making: a critical review using a systematic search strategy. Med Decis Mak 35:539–557

Boell SK, Cecez-Kecmanovic D (2015) On being ‘systematic’ in literature reviews in IS. J Inf Technol 30:161–173

Bonner SE (1994) A model of the effects of audit task complexity. Acc Organ Soc 19:213–234

Bonner SE (1999) Judgment and decision-making research in accounting. Acc Horiz 13:385–398

Bonner SE, Pennington N (1991) Cognitive processes and knowledge as determinants of auditor expertise. J Acc Lit 10:1–50

Bonner SE, Sprinkle GB (2002) The effects of monetary incentives on effort and task performance: theories evidence and a framework for research. Acc Organ Soc 27:303–345

Borman WC, Motowidlo SJ (1993) Expanding the criterion domain to include elements of contextual performance. In: Schmitt N, Borman WC (eds) Personnel selection in organizations. Jossey-Bass, San Francisco, pp 71–98

Brecht H, Martin MP (1996) Accounting information systems: the challenge of extending their scope to business and information strategy. Acc Horiz 10:16–22

Brief AP, Motowidlo SJ (1986) Prosocial organizational behaviors. Acad Manag Rev 11:710–725

*Brink WD, Lee LS (2015) The effect of tax preparation software on tax compliance: a research note. Behav Res Acc 27:121–135

Brown-Liburd H, Issa H, Lombardi D (2015) Behavioral implications of Big Data’s impact on audit judgment and decision making and future research directions. Acc Horiz 29:451–468

*Burton-Jones A, Straub DW Jr (2006) Reconceptualizing system usage: an approach and empirical test. Inf Syst Res 17:228–246

Calabrò A, Vecchiarini M, Gast J, Campopiano G, De Massis A, Kraus S (2019) Innovation in family firms: a systematic literature review and guidance for future research. Int J Manag Rev 21:317–355

*Cappel JJ, Windsor JC (2000) Ethical decision making: a comparison of computer-supported and face-to-face group. J Bus Ethics 28:95–107

*Cardinaels E (2008) The interplay between cost accounting knowledge and presentation formats in cost-based decision-making. Acc Organ Soc 33:582–602

*Cardinaels E (2016) Earnings benchmarks information systems and their impact on the degree of honesty in managerial reporting. Acc Organ Soc 52:50–62

*Carey JM, Kacmar CJ (1997) The impact of communication mode and task complexity on small group performance and member satisfaction. Comput Hum Behav 13:23–49

Cascio WF (2019) Training trends: macro micro and policy issues. Hum Resour Manag Rev 29:284–297

*Cats-Baril WL, Huber GP (1987) Decision support systems for ill-structured problems: an empirical study. Decis Sci 18:350–372

*Chan SH, Song Q, Yao LJ (2015) The moderating roles of subjective (perceived) and objective task complexity in system use and performance. Comput Hum Behav 51:393–402

*Chan SH, Song Q, Sarker S, Plumlee RD (2017) Decision support system (DSS) use and decision performance: DSS motivation and its antecedents. Inf Manag 54:934–947

*Changchit C, Holsapple CW, Viator RE (2001) Transferring auditors’ internal control evaluation knowledge to management. Expert Syst Appl 20:275–291

Chartered Association of Business Schools (2018) Academic journal guide 2018. https://charteredabs.org/academic-journal-guide-2018/ . Accessed 8 Nov 2022

*Chau PY, Bell PC (1995) Designing effective simulation-based decision support systems: an empirical assessment of three types of decision support systems. J Oper Res Soc 46:315–331

*Chen M, Liou Y, Wang C-W, Fan Y-W, Chi Y-P (2007) TeamSpirit: design implementation and evaluation of a web-based group decision support system. Decis Support Syst 43:1186–1202

*Chervany NL, Dickson GW (1974) An experimental evaluation of information overload in a production environment. Manag Sci 20:1335–1344

*Chidambaram L (1996) Relational development in computer-supported groups. MIS Q 20:143–165

*Chidambaram L, Jones B (1993) Impact of communication medium and computer support on group perceptions and performance: a comparison of face-to-face and dispersed meetings. MIS Q 17:465–491

*Chidambaram L, Bostrom RP, Wynne BE (1990) A longitudinal study of the impact of group decision support systems on group development. J Manag Inf Syst 7:7–25

Chong V, Wang IZ (2018) Delegation of decision rights and misreporting: the roles of incentive-based compensation schemes and responsibility rationalization. Eur Acc Rev 28:275–307

*Chu PC, Spires EE (2000) The joint effects of effort and quality on decision strategy choice with computerized decision aids. Decis Sci 31:259–292

*Chung QB, Willemain TR, O’Keefe RM (2000) Influence of model management systems on decision making: empirical evidence and implications. J Oper Res Soc 51:936–948

Clark TD, Jones MC, Armstrong CP (2007) The dynamic structure of management support systems: theory development research focus and direction. MIS Q 31:579–615

Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20:37–46

*Coll R, Coll JH, Rein D (1991) The effect of computerized decision aids on decision time and decision quality. Inf Manag 20:75–81

*Connolly T, Jessup LM, Valacich JS (1990) Effects of anonymity and evaluative tone on idea generation in computer-mediated groups. Manag Sci 36:689–703

Conway JM (1999) Distinguishing contextual performance from task performance for managerial jobs. J Appl Psychol 84:3–13

Cooper HM (1989) Integrating research: a guide for literature reviews, 2nd edn. Sage Publications, Thousand Oaks

Cooper DJ, Morgan W (2008) Case study research in accounting. Acc Horiz 22:159–178

Crossan MM, Apaydin M (2010) A multi-dimensional framework of organizational innovation: a systematic review of the literature. J Manag Stud 47:1154–1191

*Crossland MD, Wynne BE, Perkins WC (1995) Spatial decision support systems: an overview of technology and a test of efficacy. Decis Support Syst 14:219–235

Cummings ML (2006) Automation and accountability in decision support system interface design. J Technol Stud 32:23–31

*Daily BF, Steiner RL (1998) The influence of group decision support systems on contribution and commitment levels in multicultural and culturally homogeneous decision-making groups. Comput Hum Behav 14:147–162

*Daily BF, Teich JE (2001) Perceptions of contribution in multi-cultural groups in non-GDSS and GDSS environments. Eur J Oper Res 134:70–83

*Daily B, Whatley A, Ash SR, Steiner RL (1996) The effects of a group decision support system on culturally diverse and culturally homogeneous group decision making. Inf Manag 30:281–289

*Dalal NP, Kasper GM (1994) The design of joint cognitive systems: the effect of cognitive coupling on performance. Int J Hum Comput Stud 40:677–702

*Danziger JN, Kraemer KL (1985) Computerized data-based systems and productivity among professional workers: the case of detectives. Public Adm Rev 45:196–209

*Davern MJ, Kamis A (2010) Knowledge matters: restrictiveness and performance with decision support. Decis Support Syst 49:343–353

David RJ, Han S-K (2004) A systematic assessment of the empirical support for transaction cost economics. Strateg Manag J 25:39–58

*Davis DL, Elnicki RA (1984) User cognitive types for decision support systems. Omega 12:601–614

*Davis FD, Kottemann JE (1994) User perceptions of decision support effectiveness: two production planning experiments. Decis Sci 25:57–78

*Davis DL, Davis RD, Shrode WS (1987) Decision support systems (DSS) design for operations managers: an empirical study of the impact of report design and decision style on effective choice. J Oper Manag 7:47–62

De Jong J, Den Hartog D (2010) Measuring innovative work behaviour. Creat Innov Manag 19:23–36

*De Guinea AO, Webster J, Staples DS (2012) A meta-analysis of the consequences of virtualness on team functioning. Inf Manag 49:301–308

Deci EL, Ryan RM (1985) Intrinsic motivation and self-determination in human behavior. Plenum Press, New York

Book   Google Scholar  

Deci EL, Vallerand RJ, Pelletier LG, Ryan RM (1991) Motivation and education: the self-determination perspective. Educ Psychol 26:325–346

Delbecq AL, Van de Ven AH (1971) A group process model for problem identification and program planning. J Appl Behav Sci 7:466–492

*Dennis AR (1996) Information exchange and use in group decision making: you can lead a group to information but you can’t make it think. MIS Q 20:433–457

*Dennis AR, Garfield MJ (2003) The adoption and use of GSS in project teams: toward more participative processes and outcomes. MIS Q 27:289–323

*Dennis AR, Kinney ST (1998) Testing media richness theory in the new media: the effects of cues feedback and task equivocality. Inf Syst Res 9:256–274

*Dennis AR, Valacich JS (1993) Computer brainstorms: more heads are better than one. J Appl Psychol 78:531–537

*Dennis AR, Wixom BH (2002) Investigating the moderators of the group support systems use with meta-analysis. J Manag Inf Syst 18:235–257

*Dennis AR, Hilmer KM, Taylor NJ (1997a) Information exchange and use in GSS and verbal group decision making: effects of minority influence. J Manag Inf Syst 14:61–88

*Dennis AR, Tyran CK, Vogel DR, NunamakerJr JF (1997b) Group support systems for strategic planning. J Manag Inf Syst 14:155–184

*Dennis AR, Aronson JE, Heninger WG, Walker ED II (1999a) Structuring time and task in electronic brainstorming. MIS Q 23:95–108

*Dennis AR, Hayes GS, Daniels RM Jr (1999b) Business process modeling with group support systems. J Manag Inf Syst 15:115–142

*Dennis AR, Wixom BH, Vandenberg RJ (2001) Understanding fit and appropriation effects in group support systems via meta-analysis. MIS Q 25:167–193

DeSanctis G, Gallupe RB (1985) Group decision support systems: a new frontier. Data Base 16:3–10

DeSanctis G, Gallupe RB (1987) A foundation for the study of group decision support systems. Manag Sci 33:589–609

Diehl M, Stroebe W (1987) Productivity loss in brainstorming groups: toward the solution of a riddle. J Pers Soc Psychol 53:497–509

Dinh TQ, Calabrò A (2019) Asian family firms through corporate governance and institutions a systematic review of the literature and agenda for future research. Int J Manag Rev 21:50–75

Dirks KT (1999) The effects of interpersonal trust on work group performance. J Appl Psychol 84:445–455

*Dos Santos BL, Bariff ML (1988) A study of user interface aids for model-oriented decision support systems. Manag Sci 34:461–468

*Dowling KL, St LRD (2000) Asynchronous implementation of the nominal group technique: is it effective? Decis Support Syst 29:229–248

*Durand DE, VanHuss SH (1992) Creativity software and DSS: cautionary findings. Inf Manag 23:1–6

Dzindolet MT, Peterson SA, Pomranky RA, Pierce LG, Beck HP (2003) The role of trust in automation reliance. Int J Hum Comput Stud 58:697–718

*Easton GK, George JF, Nunamaker JF Jr, Pendergast MO (1990) Using two different electronic meeting system tools for the same task: an experimental comparison. J Manag Inf Syst 7:85–100

Edwards JS (1992) Expert systems in management and administration: are they really different from decision support systems? Eur J Oper Res 61:114–121

Eierman MA, Niderman F, Adams C (1995) DSS theory: a model of constructs and relationships. Decis Support Syst 14:1–26

*Eining MM, Jones DR, Loebbecke JK (1997) Reliance on decision aids: an examination of auditors’ assessment of management fraud. Audit J Pract Theory 16:1–19

*Elam JJ, Mead M (1990) Can software influence creativity? Inf Syst Res 1:1–22

Elbashir MZ, Collier PA, Sutton SG (2011) The role of organizational absorptive capacity in strategic use of business intelligence to support integrated management control systems. Acc Rev 86:155–184

Elkin RA, Leippe MR (1986) Physiological arousal dissonance and attitude change: evidence for a dissonance arousal link and a “don’t remind me” effect. J Pers Soc Psychol 51:55–65

Elliot AJ, Devine PG (1994) On the motivational nature of cognitive dissonance: dissonance as psychological discomfort. J Pers Soc Psychol 67:382–394

*El-Shinnawy M, Vinze AS (1997) Technology culture and persuasiveness: a study of choice-shifts in group settings. Int J Hum Comput Stud 47:473–496

Endenich C, Trapp R (2020) Ethical implications of management accounting and control: a systematic review of the contributions from the Journal of Business Ethics. J Bus Ethics 163:309–328

Endsley MR (1995) Toward a theory of situation awareness in dynamic systems. Hum Factors 37:32–64

*Erskine MA, Khojah M, McDaniel AE (2019) Location selection using heat maps: relative advantage task-technology fit and decision-making performance. Comput Hum Behav 101:151–162

*Fedorowicz J, Oz E, Berger PD (1992) A learning curve analysis of expert system use. Decis Sci 23:797–818

Festinger L (1957) A theory of cognitive dissonance. Stanford University Press, Stanford

*Fjermestad J (2004) An analysis of communication mode in group support systems research. Decis Support Syst 37:239–263

Floyd E, List JA (2016) Using field experiments in accounting and finance. J Acc Res 54:437–475

Ford FN (1985) Decision support systems and expert systems: a comparison. Inf & Manag 8:21–26

Forgionne GA, Kohli R (2000) Management support system effectiveness: further empirical evidence. J Assoc Inf Syst 1:1–37

*Fuller RM, Dennis AR (2009) Does fit matter? The impact of task-technology fit and appropriation on team performance in repeated tasks. Inf Syst Res 20:2–17

*Galegher J, Kraut RE (1994) Computer-mediated communication for intellectual teamwork: an experiment in group writing. Inf Syst Res 5:110–138

*Gallupe RB, McKeen JD (1990) Enhancing computer-mediated communication: an experimental investigation into the use of a group decision support system for face-to-face versus remote meetings. Inf Manag 18:1–13

*Gallupe RB, DeSanctis G, Dickson GW (1988) Computer-based support for group problem-finding: an experimental investigation. MIS Q 12:277–296

*Gallupe RB, Bastianutti LM, Cooper WH (1991) Unblocking brainstorms. J Appl Psychol 76:137–142

*Gallupe RB, Dennis AR, Cooper WH, Valacich JS, Bastianutti LM, Nunamaker JF Jr (1992) Electronic brainstorming and group size. Acad Manag J 35:350–369

*Gallupe RB, Cooper WH, Grisé ML, Bastianutti LM (1994) Blocking electronic brainstorms. J Appl Psychol 79:77–86

*Gelderman M (1998) The relation between user satisfaction usage of information systems and performance. Inf Manag 34:11–18

Gelderman M (2002) Task difficulty task variability and satisfaction with management support systems. Inf Manag 39:593–604

*George JM, Brief AP (1992) Feeling good-doing good: a conceptual analysis of the mood at work-organizational spontaneity relationship. Psychol Bull 112:310–329

George JF, Easton GK, Nunamaker JF Jr, Northcraft GB (1990) A study of collaborative group work with and without computer-based support. Inf Syst Res 1:394–415

Gerhart N, Ogbanufe O, Torres R, Sidorova A, Evangelopoulos N (2021) Effort minimization theory in the data analytics era. J Comput Inf Syst 62:1–13

*Ghasemaghaei M (2019) Does data analytics use improve firm decision making quality? The role of knowledge sharing and data analytics competency. Decis Support Syst 120:14–24

Glaser R, Bassok M (1989) Learning theory and the study of instruction. Annu Rev Psychol 40:631–666

*Glover SM, Prawitt DF, Spilker B (1997) The influence of decision aids on user behavior: implications for knowledge acquisition and inappropriate reliance. Organ Behav Hum Decis Process 72:232–255

*González C, Kasper GM (1997) Animation in user interfaces designed for decision support systems: the effects of image abstraction transition and interactivity on decision quality. Decis Sci 28:793–823

*Goodhue DL, Thompson RL (1995) Task-technology fit and individual performance. MIS Q 19:213–236

Gorry GA, Scott Morton MS (1971) A framework for management information systems. Sloan Manag Rev 13:55–70

*Goslar MD, Green GI, Hughes TH (1986) Decision support systems: an empirical assessment for decision making. Decis Sci 17:79–91

*Goul M, Shane B, Tonge FM (1986) Using a knowledge-based decision support system in strategic planning decisions: an empirical study. J Manag Inf Syst 2:70–84

*Grabowski M, Sanborn SD (2003) Human performance and embedded intelligent technology in safety-critical systems. Int J Hum Comput Stud 58:637–670

*Grabowski M, Wallace WA (1993) An expert system for maritime pilots: its design and assessment using gaming. Manag Sci 39:1506–1520

Griffiths M, Dyble M (2018) Learning as a platform: redefining how learning delivers value to the business. Deloitte. https://www2.deloitte.com/content/dam/Deloitte/us/Documents/human-capital/us-cons-learning-platform.pdf . Accessed 8 Nov 2022

*Grohowski R, McGoff C, Vogel D, Martz B, Nunamaker J (1990) Implementing electronic meeting systems at IBM: lessons learned and success factors. MIS Q 14:369–383

*Guerlain SA, Smith PJ, Obradovich JH, Rudmann S, Strohm P, Smith JW, Svirbely J, Sachs L (1999) Interactive critiquing as a form of decision support: an empirical evaluation. Hum Factors 41:72–89

Guimaraes T, Yoon Y, Clevenson A (1996) Factors important to expert system success: a field test. Inf & Manag 30:119–130

Guragai B, Hunt NC, Neri MP, Taylor EZ (2017) Accounting information systems and ethics research: review synthesis and the future. J Inf Syst 31:65–81

Haidich A-B (2010) Meta-analysis in medical research. Hippokratia 14:29–37

*Haines R, Mann JEC (2011) A new perspective on de-individuation via computer-mediated communication. Eur J Inf Syst 20:156–167

*Hannan RL, Rankin FW, Towry KL (2006) The effect of information systems on honesty in managerial reporting: a behavioral perspective. Contemp Acc Res 23:885–918

*Harmon J, Schneer JA, Hoffman LR (1995) Electronic meetings and established decision groups: audioconferencing effects on performance and structural stability. Organ Behav Hum Decis Process 61:138–147

Hartono E, Santhanam R, Holsapple CW (2007) Factors that contribute to management support system success: an analysis of field studies. Decis Support Syst 43:256–268

Hasan SK, Sarker R, Essam D (2011) Genetic algorithm for job-shop scheduling with machine unavailability and breakdowns. Int J Prod Res 49(16):4999–5015

*Haseman WD, Nazareth DL, Paul S (2005) Implementation of a group decision support system utilizing collective memory. Inf Manag 42:591–605

*Hayne SC, Smith CAP, Turk D (2003) The effectiveness of groups recognizing patterns. Int J Hum Comput Stud 59:523–543

*Hedlund J, Ilgen DR, Hollenbeck JR (1998) Decision accuracy in computer-mediated versus face-to-face decision-making teams. Organ Behav Hum Decis Process 76:30–47

*Hertel G, Meessen SM, Riehle DM, Thielsch MT, Nohe C, Becker J (2019) Directed forgetting in organisations: the positive effects of decision support systems on mental resources and well-being. Ergon 62:597–611

Hesford JW, Lee S-H, Van der Stede WA, Young SM (2007) Management accounting: a bibliographic study. In: Chapman CS, Hopwood AG, Shields MD (eds) Handbook of management accounting research. Elsevier, Amsterdam, pp 3–26

Hevner AR, March ST, Park J, Ram S (2004) Design science in information systems research. MIS Q 28:75–105

*Hightower R, Sayeed L (1995) The impact of computer-mediated communication systems on biased group discussion. Comput Hum Behav 11:33–44

*Hightower R, Sayeed L (1996) Effects of communication mode and prediscussion information distribution characteristics on information exchange in groups. Inf Syst Res 7:451–465

*Ho TH, Raman KS (1991) The effect of GDSS and elected leadership on small group meetings. J Manag Inf Syst 8:109–133

*Hoch SJ, Schkade DA (1996) A psychological approach to decision support systems. Manag Sci 42:51–64

*Hodgetts HM, Tremblay S, Vallières BR, Vachon F (2015) Decision support and vulnerability to interruption in a dynamic multitasking environment. Int J Hum Comput Stud 79:106–117

Hood WW, Wilson CS (2003) Overlap in bibliographic databases. J Am Soc Inf Sci Technol 54:1091–1103

Horton R (2015) The robots are coming: a Deloitte insight report. Deloitte. https://www2.deloitte.com/content/dam/Deloitte/uk/Documents/finance/deloitte-uk-finance-robots-are-coming.pdf . Accessed 8 Nov 2022

*Huang W, Li D (2007) Opening up the black box in GSS research: explaining group decision outcome with group process. Comput Hum Behav 23:58–78

*Huang W, Wei K-K (1997) Task as a moderator for the effects of group support systems on group influence processes. Eur J Inf Syst 6:208–217

*Huang W, Wei K-K (2000) An empirical investigation of the effects of group support systems (GSS) and task type on group interactions from an influence perspective. J Manag Inf Syst 17:181–206

*Huang AH, Windsor JC (1998) An empirical assessment of a multimedia executive support system. Inf Manag 33:251–262

*Huang W, Raman KS, Wei K-K (1997) Effects of group support system and task type on social influences in small groups. IEEE Trans Syst Man Cybern Part A 27:578–587

*Huang W, Wei K-K, Tan BCY (1999) Compensating effects of GSS on group performance. Inf Manag 35:195–202

*Huang W, Wei K-K, Watson RT, Tan BCY (2002) Supporting virtual team-building with a GSS: an empirical investigation. Decis Support Syst 34:359–367

*Hung SY (2003) Expert versus novice use of the executive support systems: an empirical study. Inf Manag 40:177–189

*Hung S-Y, Ku Y-C, Liang T-P, Lee C-J (2007) Regret avoidance as a measure of DSS success: an exploratory study. Decis Support Syst 42:2093–2106

*Hwang M (1998) Did task type matter in the use of decision room GSS? A critical review and a meta-analysis. Omega 26:1–15

*Hwang H-G, Guynes J (1994) The effect of group size on group performance in computer-supported decision making. Inf Manag 26:189–198

*Igbaria M, Tan M (1997) The consequences of information technology acceptance on subsequent individual performance. Inf Manag 32:113–121

*Introne J, Iandoli L (2014) Improving decision-making performance through argumentation: an argument-based decision support system to compute with evidence. Decis Support Syst 64:79–89

*Jain HK, Ramamurthy K, Sundaram S (2006) Effectiveness of visual interactive modeling in the context of multiple-criteria group decisions. IEEE Trans Syst Man Cybern Part A 36:298–318

*Jarvenpaa SL (1989) The effect of task demands and graphical format on information processing strategies. Manag Sci 35:285–303

*Jarvenpaa SL, Rao VS, Huber GP (1988) Computer support for meetings of groups working on unstructured problems: a field experiment. MIS Q 12:645–666

*Jensen ML, Lowry PB, Burgoon JK, NunamakerJr JF (2010) Technology dominance in complex decision making: the case of aided credibility assessment. J Manag Inf Syst 27:175–202

*Jensen ML, Lowry PB, Jenkins JL (2011) Effects of automated and participative decision support in computer-aided credibility assessment. J Manag Inf Syst 28:201–233

Jesson JK, Matheson L, Lacey FM (2011) Doing your literature review: traditional and systematic techniques. Sage Publications, Thousand Oaks

*Jessup LM, Tansik DA (1991) Decision making in an automated environment: the effects of anonymity and proximity with a group decision support system. Decis Sci 22:266–279

*Jessup LM, Connolly T, Galegher J (1990) The effects of anonymity on GDSS group process with an idea-generating task. MIS Q 14:313–321

Johnsen TE, Miemczyk J, Howard M (2017) A systematic literature review of sustainable purchasing and supply research: theoretical perspectives and opportunities for IMP-based research. Ind Mark Manag 61:130–143

Johnson JW (2001) The relative importance of task and contextual performance dimensions to supervisor judgments of overall performance. J Appl Psychol 86:984–996

Johnson EJ, Payne JW (1985) Effort and Accuracy in Choice. Manag Sci 31:381–513

*Kahai SS, Avolio BJ, Sosik JJ (1998) Effects of source and participant anonymity and difference in initial opinions in an EMS context. Decis Sci 29:427–458

*Kahai SS, Sosik JJ, Avolio BJ (2003) Effects of leadership style anonymity and rewards on creativity-relevant processes and outcomes in an electronic meeting system context. Leadersh Q 14:499–524

*Kang S, Lim KH, Kim MS, Yang H-D (2012) A multilevel analysis of the effect of group appropriation on collaborative technologies use and performance. Inf Syst Res 23:214–230

*Karan V, Kerr DS, Murthy US, Vinze AS (1996) Information technology support for collaborative decision making in auditing: an experimental investigation. Decis Support Syst 16:181–194

*Kayande U, De Bruyn A, Lilien GL, Rangaswamy A, van Bruggen GH (2009) How incorporating feedback mechanisms in a DSS affects DSS evaluations. Inf Syst Res 20:527–546

Keen PGW, Scott Morton MS (1978) Decision support systems: an organisational perspective. Addison-Wesley

*Khalifa M, Kwok RC-W (1999) Remote learning technologies: effectiveness of hypertext and GSS. Decis Support Syst 26:195–207

Kienzler M, Kowalkowski C (2017) Pricing strategy: a review of 22 years of marketing research. J Bus Res 78:101–110

*King WR, Premkumar G, Ramamurthy K (1990) An evaluation of the role and performance of a decision support system in business education. Decis Sci 21:642–659

*Kohli R, Devaraj S (2004) Contribution of institutional DSS to organizational performance: evidence from a longitudinal study. Decis Support Syst 37:103–118

*Kottemann JE, Remus WE (1992) The effect of planning horizon on the effectiveness of what-if analysis. Omega 20:295–301

*Kottemann JE, Davis FD, Remus WE (1994) Computer-assisted decision making: performance beliefs and the illusion of control. Organ Behav Hum Decis Process 57:26–37

*Krancher O, Dibbern J, Meyer P (2018) How social media-enabled communication awareness enhances project team performance. J Assoc Inf Syst 19:813–856

*Kwok RC-W, Khalifa M (1998) Effect of GSS on knowledge acquisition. Inf Manag 34:307–315

*Kwok RC-W, Lee J-N, Huynh MQ, Pi S-M (2002a) Role of GSS on collaborative problem-based learning: a study on knowledge externalization. Eur J Inf Syst 11:98–107

*Kwok RC-W, Ma J, Vogel DR (2002b) Effects of group support systems and content facilitation on knowledge acquisition. J Manag Inf Syst 19:185–229

*Kwok RC-W, Ma J, Zhou D (2002c) Improving group decision making: a fuzzy GSS approach. IEEE Trans Syst Man Cybern Part C 32:54–63

Lachmann M, Trapp I, Trapp R (2017) Diversity and validity in positivist empirical management accounting research: a longitudinal perspective over four decades. Manag Acc Res 34:42–58

*Lam SSK (1997) The effects of group decision support systems and task structures on group communication and decision quality. J Manag Inf Syst 13:193–215

*Lam SSK, Schaubroeck J (2000) Improving group decisions by better pooling information: a comparative advantage of group decision support systems. J Appl Psychol 85:565–573

*Lamberti DM, Wallace WA (1990) Intelligent interface design: an empirical assessment of knowledge presentation in expert systems. MIS Q 14:279–311

Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biom 33:159–174

*Landsbergen D, Coursey DH, Loveless S, Shangraw RF Jr (1997) Decision quality confidence and commitment with expert systems: an experimental study. J Public Adm Res Theory 7:131–157

Larson D, Chang V (2016) A review and future direction of agile, business intelligence, analytics and data science. Int J Inf Manag 36:700–710

*Lawrence M, Goodwin P, Fildes R (2002) Influence of user participation on DSS use and decision accuracy. Omega 30:381–392

*Le Blanc LA (1991) An assessment of DSS performance: the impact of utilization and closure. Inf Manag 20:137–148

*Le Blanc LA, Kozar KA (1990) An empirical investigation of the relationship between DSS usage and system performance: a case study of a navigation support system. MIS Q 14:263–277

*Lee M-T, Su Z-Y, Hou Y-H, Liao H-C, Lian J-D (2011) A decision support system for diagnosis related groups coding. Expert Syst Appl 38:3626–3631

Libby R, Luft J (1993) Determinants of judgment performance in accounting settings: ability knowledge motivation and environment. Acc Organ Soc 18:425–450

Light RJ, Pillemer DB (1984) Summing up: the science of reviewing research. Harvard University Press, Cambridge

*Lilien GL, Rangaswamy A, Van Bruggen GH, Starke K (2004) DSS effectiveness in marketing resource allocation decisions: reality vs. perception. Inf Syst Res 15:216–235

*Lim L-H, Benbasat I (1997) The debiasing role of group support systems: an experimental investigation of the representativeness bias. Int J Hum Comput Stud 47:453–471

*Lim L-H, Raman KS, Wei K-K (1994) Interacting effects of GDSS and leadership. Decis Support Syst 12:199–211

*Lim KH, O’Connor MJ, Remus WE (2005) The impact of presentation media on decision making: does multimedia improve the effectiveness of feedback? Inf Manag 42:305–316

*Limayem M, DeSanctis G (2000) Providing decisional guidance for multicriteria decision making in groups. Inf Syst Res 11:386–401

Lindsay RM (1994) Publication system biases associated with the statistical testing paradigm. Contemp Acc Res 11:33–57

*Loy SL (1991) The interaction effects between general thinking skills and an interactive graphics-based DSS to support problem structuring. Decis Sci 22:846–868

*Lucas HC Jr (1975) Performance and the use of an information system. Manag Sci 21:908–919

*Lucas HC Jr, Spitler VK (1999) Technology use and performance: a field study of broker workstations. Decis Sci 30:291–311

Luo W (2019) User choice of interactive data visualization format: the effects of cognitive style and spatial ability. Decis Support Syst 122:1–11

Luoh HF, Tsaur SH, Tang YY (2014) Empowering employees: job standardization and innovative behavior. Int J Contemp Hosp Manag 26:1100–1117

*Lynch AL, Murthy US, Engle TJ (2009) Fraud brainstorming using computer-mediated communication: the effects of brainstorming technique and facilitation. Acc Rev 84:1209–1232

*MacCrimmon KR, Wagner C (1994) Stimulating ideas through creative software. Manag Sci 40:1514–1532

*Mackay JM, Barr SH, Kletke MG (1992) An empirical investigation of the effects of decision aids on problem-solving processes. Decis Sci 23:648–672

*Madhavan P, Phillips RR (2010) Effects of computer self-efficacy and system reliability on user interaction with decision support systems. Comput Hum Behav 26:199–204

*Mahoney LS, Roush PB, Bandy D (2003) An investigation of the effects of decisional guidance and cognitive ability on decision-making involving uncertainty data. Inf Organ 13:85–110

*Malaga RA (2000) The effect of stimulus modes and associative distance in individual creativity support systems. Decis Support Syst 29:125–141

*Marakas GM, Elam JJ (1997) Creativity enhancement in problem solving: through software or process? Manag Sci 43:1136–1146

Marakas GM, O’Brien JA (2011) Introduction to information systems. McGraw-Hill, New York

March ST, Smith GF (1995) Design and natural science research on information technology. Decis Support Syst 15:251–266

*Martz WB Jr, Vogel DR, Nunamaker JF Jr (1992) Electronic meeting systems: results from the field. Decis Support Syst 8:141–158

*Massetti B (1996) An empirical examination of the value of creativity support systems on idea generation. MIS Q 20:83–97

*Massey AP, Clapper DL (1995) Element finding: the impact of a group support system on a crucial phase of sense making. J Manag Inf Syst 11:149–176

Mauldin EG, Ruchala LV (1999) Towards a meta-theory of accounting information systems. Acc Organ Soc 24:317–331

Mazar N, Hawkins SA (2015) Choice architecture in conflicts of interest: defaults as physical and psychological barriers to (dis)honesty. J Exp Soc Psychol 59:113–117

McGrath JE (1984) Groups: interaction and performance, 14th edn. Prentice-Hall, Englewood Cliffs

McGrath JE (1991) Time interaction and performance (TIP): a theory of groups. Small Group Res 22:147–174

*McGuire TW, Kiesler S, Siegel J (1987) Group and computer-mediated discussion effects in risk decision making. J Pers Soc Psychol 52:917–930

*McLeod PL, Liker JK (1992) Electronic meeting systems: evidence from a low structure environment. Inf Syst Res 3:195–223

*McLeod PL, Baron RS, Marti MW, Yoon K (1997) The eyes have it: minority influence in face-to-face and computer-mediated group discussion. J Appl Psychol 82:706–718

*Mennecke BE, Valacich JS (1998) Information is what you make of it: the influence of group history and computer support on information sharing decision quality and member perceptions. J Manag Inf Syst 15:173–197

Meredith J (1998) Building operations management theory through case and field research. J Oper Manag 16:441–454

Millet I, Mawhinney CH (1992) Executive information systems: a critical perspective. Inf Manag 23:83–92

Mills TY (1996) The effect of cognitive style on external auditors’ reliance decisions on internal audit functions. Behav Res Acc 8:49–73

*Miranda SM, Bostrom RP (1993) The impact of group support systems on group conflict and conflict management. J Manag Inf Syst 10:63–95

*Miranda SM, Saunders CS (2003) The social construction of meaning: an alternative perspective on information sharing. Inf Syst Res 14:87–106

Moers F (2007) Doing archival research in management accounting. In: Chapman CS, Hopwood AG, Shields MD (eds) Handbook of management accounting research. Elsevier, Amsterdam, pp 399–413

*Montazemi AR, Gupta KM (1997) On the effectiveness of cognitive feedback from an interface agent. Omega 25:643–658

*Montazemi AR, Wang F, Nainar SK, Bart CK (1996) On the effectiveness of decisional guidance. Decis Support Syst 18:181–198

Morrell K (2008) The narrative of ‘evidence based’ management: a polemic. J Manag Stud 45:613–635

Motowidlo SJ, Borman WC, Schmit MJ (1997) A theory of individual differences in task and contextual performance. Hum Perform 10:71–83

*Murthy US, Kerr DS (2003) Decision making performance of interacting groups: an experimental investigation of the effects of task type and communication mode. Inf Manag 40:351–360

*Murthy US, Kerr DS (2004) Comparing audit team effectiveness via alternative modes of computer-mediated communication. Audit J Pract Theory 23:141–152

*Nakatsu RT, Benbasat I (2003) Improving the explanatory power of knowledge-based systems: an investigation of content and interface-based enhancements. IEEE Trans Syst Man Cybern Part A 33:344–357

Negash S (2004) Business intelligence. Commun Assoc. Inf Syst 13:177–195

Neigel AR, Caylor JP, Kase SE, Vanni MT, Hoye J (2018) The role of trust and automation in an intelligence analyst decisional guidance paradigm. J Cogn Eng Decis Mak 12:239–247

*Nunamaker JF Jr, Vogel D, Heminger A, Martz B, Grohowski R, McGoff C (1989) Experience at IBM with group support systems: a field study. Decis Support Syst 5:183–196

*O’Connor RM Jr, Doherty ME, Tweney RD (1989) The effects of system failure error on predictions. Organ Behav Hum Decis Process 44:1–11

O’Donnell E, David JS (2000) How information systems influence user decisions: a research framework and literature review. Int J Acc Inf Syst 1:178–203

*Ocker R, Hiltz SR, Turoff M, Fjermestad J (1995) The effects of distributed group support and process structuring on software requirements development teams: results on creativity and quality. J Manag Inf Syst 12:127–153

*Ocker R, Fjermestad J, Hiltz SR, Johnson K (1998) Effects of four modes of group communication on the outcomes of software requirements determination. J Manag Inf Syst 15:99–118

Organ DW (1997) Organizational citizenship behavior: it’s construct clean-up time. Hum Perform 10:85–97

*Oz E, Fedorowicz J, Stapleton T (1993) Improving quality speed and confidence in decision-making: measuring expert systems benefits. Inf Manag 24:71–82

Panya KO, Nyarwath O (2022) Demystifying philosophies and paradigms underpinning scientific research. Strateg J Bus Chang Manag 9:1367–1382

Parasuraman R, Manzey DH (2010) Complacency and bias in human use of automation: an attentional integration. Hum Factors 52:381–410

*Parent M, Gallupe RB, Salisbury WD, Handelman JM (2000) Knowledge creation in focus groups: can group technologies help? Inf Manag 38:47–58

*Parikh M, Fazlollahi B, Verma S (2001) The effectiveness of decisional guidance: an empirical evaluation. Decis Sci 32:303–331

*Park Y-T (2006) An empirical investigation of the effects of data warehousing on decision performance. Inf Manag 43:51–61

*Parkes A (2013) The effect of task-individual-technology fit on user attitude and performance: an experimental investigation. Decis Support Syst 54:997–1009

Payne JW (1982) Contingent decision behavior. Psychol Bull 92:382–402

*Pei BKW, Reneau JH (1990) The effects of memory structure on using rule-based expert systems for training: a framework and an empirical test. Decis Sci 21:263–286

Perera HN, Hurles J, Fahimnia B, Reisi M (2019) The human factor in supply chain forecasting: a systematic review. Eur J Oper Res 274:574–600

*Perry NC, Wiggins MW, Childs M, Fogarty G (2012) Can reduced processing decision support interfaces improve the decision-making of less-experienced incident commanders? Decis Support Syst 52:497–504

*Perry NC, Wiggins MW, Childs M, Fogarty G (2013) The application of reduced-processing decision support systems to facilitate the acquisition of decision-making skills. Hum Factors 55:535–544

*Petrovic O, Krickl O (1994) Traditionally-moderated versus computer supported brainstorming: a comparative study. Inf Manag 27:233–243

Petticrew M, Roberts H (2006) Systematic reviews in the social sciences: a practical guide. Blackwell, Oxford

Phillips-Wren G, Mora M, Forgionne GA, Gupta JND (2009) An integrative evaluation framework for intelligent decision support systems. Eur J Oper Res 195:642–652

Pinsonneaul A, Kraemer K (1993) Survey research methodology in management information systems: an assessment. J Manag Inf Syst 10:75–105

*Pissarra J, Jesuino JC (2005) Idea generation through computer-mediated communication: the effects of anonymity. J Manag Psychol 20:275–291

Podsakoff PM, Mackenzie SB, Bachrach DG, Podsakoff NP (2005) The influence of management journals in the 1980s and 1990s. Strateg Manag J 26:473–488

Pomeroy B, Thornton DB (2008) Meta-analysis and the accounting literature: the case of audit committee independence and financial reporting quality. Eur Acc Rev 17:305–330

*Poole MS, Holmes M, Desanctis G (1991) Conflict management in a computer-supported meeting environment. Manag Sci 37:926–953

*Postmes T, Lea M (2000) Social processes and group decision making: anonymity in group decision support systems. Ergon 43:1252–1274

Power DJ (2008a) Understanding data-driven decision support systems. Inf Syst Manag 25:149–154

Power DJ (2008b) Decision support systems: a historical overview. In: Burstein F, Holsapple CW (eds) Handbook on decision support systems 1. Springer, Berlin, pp 121–140

Chapter   Google Scholar  

*Power DJ, Meyeraan SL, Aldag RJ (1994) Impacts of problem structure and computerized decision aids on decision attitudes and behaviors. Inf Manag 26:281–294

PricewaterhouseCoopers (2011) Financial planning: realizing the value of budgeting and forecasting. https://www.pwc.com/my/en/assets/services/realizing-the-value-of-budgeting-n-forecasting.pdf . Accessed 26 Mar 2023

Quattrone P (2016) Management accounting goes digital: will the move make it wiser? Manag Acc Res 31:118–122

Rankin FW, Schwartz ST, Young RA (2008) The effect of honesty and superior authority on budget proposals. Acc Rev 83:1083–1099

*Rayo MF, Kowalczyk N, Liston BW, Sanders EB-N, White S, Patterson ES (2015) Comparing the effectiveness of alerts and dynamically annotated visualizations (DAVs) in improving clinical decision making. Hum Factors 57:1002–1014

*Reinig BA, Shin B (2002) The dynamic effects of group support systems on group meetings. J Manag Inf Syst 19:303–325

*Remus W (1984) An empirical investigation of the impact of graphical and tabular data presentations on decision making. Manag Sci 30:533–542

*Reneau JH, Blanthorne C (2001) Effects of information sequence and irrelevant distractor information when using a computer-based decision aid. Decis Sci 32:145–163

Reuber R (1997) Management experience and management expertise. Decis Support Syst 21:51–60

Rogat TK, Linnenbrink-Garcia L (2011) Socially shared regulation in collaborative groups: an analysis of the interplay between quality of social regulation and group processes. Cogn Instr 29:375–415

Rom A, Rohde C (2007) Management accounting and integrated information systems: a literature review. Int J Acc Inf Syst 8:40–68

*Rose JM (2005) Decision aids and experiential learning. Behav Res Acc 17:175–189

*Rose JM, Wolfe CJ (2000) The effects of system design alternatives on the acquisition of tax knowledge from a computerized tax decision aid. Acc Organ Soc 25:285–306

Russo JE, Dosher BA (1983) Strategies for multiattribute binary choice. J Exp Psychol 9:676–696

Saebi T, Foss NJ, Linder S (2019) Social entrepreneurship research: past achievements and future promises. J Manag 45:70–95

*Sambamurthy V, Chin WW (1994) The effects of group attitudes toward alternative GDSS designs on the decision-making performance of computer-supported groups. Decis Sci 25:215–241

*Sambamurthy V, Poole MS (1992) The effects of variations in capabilities of GDSS designs on management of cognitive conflict in groups. Inf Syst Res 3:224–251

*Sarker S, Sarker S, Chatterjee S, Valacich JS (2010) Media effects on group collaboration: an empirical examination in an ethical decision-making context. Decis Sci 41:887–931

*Sarter NB, Schroeder B (2001) Supporting decision making and action selection under time pressure and uncertainty: the case of in-flight icing. Hum Factors 43:573–583

*Sassen JMA, Buiël EFT, Hoegee JH (1994) A laboratory evaluation of a human operator support system. Int J Hum Comput Stud 40:895–931

*Satzinger JW, Garfield MJ, Nagasundaram M (1999) The creative process: the effects of group memory on individual idea generation. J Manag Inf Syst 15:143–160

*Saunders C, Miranda S (1998) Information acquisition in group decision making. Inf Manag 34:55–74

*Schmidt JB, Montoya-Weiss MM, Massey AP (2001) New product development decision-making effectiveness: comparing individuals face-to-face teams and virtual teams. Decis Sci 32:575–600

Schnieder C (2021) How relative performance information affects employee performance: a systematic review of empirical research. J Acc Lit 44:72–107

Schreck P (2015) Honesty in managerial reporting: how competition affects the benefits and costs of lying. Crit Perspect Acc 27:177–188

*Schweitzer L, Duxbury L (2010) Conceptualizing and measuring the virtuality of teams. Inf Syst J 20:267–295

Scott SG, Bruce RA (1994) Determinants of innovative behavior: a path model of individual innovation in the workplace. Acad Manag J 37:580–607

Scott Morton MS (1984) The state of the art of research. In: McFarlan FW (ed) The information research challenge. Harvard University Press, Boston, pp 13–41

Scott Morton MS (1967) Computer-driven visual display devices: their impact on the management decision-making process. Dissertation, Harvard Business School

Senftlechner D, Hiebl MRW (2015) Management accounting and management control in family businesses: past accomplishments and future opportunities. J Acc Organ Chang 11:573–606

*Sengupta K (1995) Cognitive feedback in environments characterized by irrelevant information. Omega 23:125–143

*Sengupta K, Te’eni D (1993) Cognitive feedback in GDSS: improving control and convergence. MIS Q 17:87–113

*Sharda R, Barr SH, McDonnell JC (1988) Decision support system effectiveness: a review and an empirical test. Manag Sci 34:139–159

Sharda R, Dursun D, Turban E (2014) Business intelligence and analytics: systems for decision support, 10th edn. Pearson Education, Harlow

Sharma S, Durand RM, Gur-Arie O (1981) Identification and analysis of moderator variables. J Mark Res 18:291–300

*Shirani AI (2006) Sampling and pooling of decision-relevant information: comparing the efficiency of face-to-face and GSS supported groups. Inf Manag 43:521–529

*Shirani AI, Tafti MHA, Affisco JF (1999) Task and technology fit: a comparison of two technologies for synchronous and asynchronous group communication. Inf Manag 36:139–150

Shroff M, Axson D (2018) Digital finance: beyond the hype. Accenture. https://www.accenture.com/_acnmedia/PDF-90/Accenture-Beyond-the-Hype-PoV.pdf . Accessed 8 Nov 2022

*Sia C-L, Tan BCY, Wei K-K (1996) Exploring the effects of some display and task factors on GSS user groups. Inf Manag 30:35–41

*Sia C-L, Tan BCY, Wei K-K (1997) Effects of GSS interface and task type on group interaction: an empirical study. Decis Support Syst 19:289–299

*Sia C-L, Tan BCY, Wei K-K (1999) Can a GSS stimulate group polarization? An empirical study. IEEE Trans Syst Man Cybern Part C 29:227–237

*Sia C-L, Tan BCY, Wei K-K (2002) Group polarization and computer-mediated communication: effects of communication cues social presence and anonymity. Inf Syst Res 13:70–90

*Siegel J, Dubrovsky V, Kiesler S, McGuire TW (1986) Group processes in computer-mediated communication. Organ Behav Hum Decis Process 37:157–187

Silver MS (1988) User perceptions of decision support system restrictiveness: an experiment. J Manag Inf Syst 5:51–65

*Singh DT (1998) Incorporating cognitive aids into decision support systems: the case of the strategy execution process. Decis Support Syst 24:145–163

*Singh DT, Ginzberg MJ (1996) An empirical investigation of the impact of process monitoring on computer-mediated decision-making performance. Organ Behav Hum Decis Process 67:156–169

*Singh DT, Singh PP (1997) Aiding DSS users in the use of complex OR models. Ann Oper Res 72:5–27

*Skitka LJ, Mosier KL, Burdick M (1999) Does automation bias decision-making? Int J Hum Comput Stud 51:991–1006

*Smith JY, Vanecek MT (1988) Computer conferencing and task-oriented decisions: implications for group decision support. Inf Manag 14:123–132

*Smith JY, Vanecek MT (1990) Dispersed group decision making using nonsimultaneous computer conferencing: a report of research. J Manag Inf Syst 7:71–92

Smith CA, Organ DW, Near JP (1983) Organizational citizenship behavior: its nature and antecedents. J Appl Psychol 68:653–663

*Snitkin SR, King WR (1986) Determinants of the effectiveness of personal decision support systems. Inf Manag 10:83–89

*Song Q, Chan SH, Wright AM (2017) The efficacy of a decision support system in enhancing risk assessment performance. Decis Sci 48:307–335

*Sosik JJ (1997) Effects of transformational leadership and anonymity on idea generation in computer-mediated groups. Group Organ Manag 22:460–487

*Sosik JJ, Avolio BJ, Kahai SS (1997) Effects of leadership style and anonymity on group potency and effectiveness in a group decision support system environment. J Appl Psychol 82:89–103

*Sosik JJ, Avolio BJ, Kahai SS, Jung DI (1998) Computer-supported work group potency and effectiveness: the role of transformational leadership anonymity and task interdependence. Comput Hum Behav 14:491–511

*Speier C (2006) The influence of information presentation formats on complex task decision-making performance. Int J Hum Comput Stud 64:1115–1131

*Speier C, Morris MG (2003) The influence of query interface design on decision-making performance. MIS Q 27:397–423

Spence MT, Brucks M (1997) The moderating effects of problem characteristics on experts’ and novices’ judgments. J Mark Res 34:233–247

Sprinkle GB (2003) Perspectives on experimental research in managerial accounting. Acc Organ Soc 28:287–318

*Straus SG, McGrath JE (1994) Does the medium matter? the interaction of task type and technology on group performance and member reactions. J Appl Psychol 79:87–97

*Sundaram S, Schwarz A, Jones E, Chin WW (2007) Technology use on the front line: how information technology enhances individual performance. J Acad Mark Sci 35:101–112

Sunde U (2009) Heterogeneity and performance in tournaments: a test for incentive effects using professional tennis data. Appl Econ 41:3199–3208

*Swink M (1995) The influences of user characteristics on performance in a logistics DSS application. Decis Sci 26:503–529

Sykes GM, Matza D (1957) Techniques of neutralization: a theory of delinquency. Am Sociol Rev 22:664–670

*Tan BCY, Raman KS, Wei K-K (1994) An empirical study of the task dimension of group support system. IEEE Trans Syst Man Cybern 24:1054–1060

*Tan BCY, Wei K-K, Watson RT, Clapper DL, McLean ER (1998a) Computer-mediated communication and majority influence: assessing the impact in an individualistic and a collectivistic culture. Manag Sci 44:1263–1278

*Tan BCY, Wei K-K, Watson RT, Walczuch RM (1998b) Reducing status effects with computer-mediated communication: evidence from two distinct national cultures. J Manag Inf Syst 15:119–141

*Tan C-H, Teo H-H, Benbasat I (2010) Assessing screening and evaluation decision support systems: a resource-matching approach. Inf Syst Res 21:305–326

Tang J, Karim KE (2019) Financial fraud detection and big data analytics–implications on auditors’ use of fraud brainstorming session. Manag Audit J 34:324–337

*Te'eni D (1989) Determinants and consequences of perceived complexity in human-computer interaction. Decis Sci 20:166–181

*Todd P, Benbasat I (1991) An experimental investigation of the impact of computer based decision aids on decision making strategies. Inf Syst Res 2:87–115

*Todd P, Benbasat I (1992) The use of information in decision making: an experimental investigation of the impact of computer-based decision aids. MIS Q 16:373–393

*Todd P, Benbasat I (1994) The influence of decision aids on choice strategies: an experimental analysis of the role of cognitive effort. Organ Behav Hum Decis Process 60:36–74

*Todd P, Benbasat I (1999) Evaluating the impact of DSS cognitive effort and incentives on strategy selection. Inf Syst Res 10:356–374

*Todd P, Benbasat I (2000) Inducing compensatory information processing through decision aids that facilitate effort reduction: an experimental assessment. J Behav Decis Mak 13:91–106

Tranfield D, Denyer D, Smart P (2003) Towards a methodology for developing evidence-informed management knowledge by means of systematic review. Br J Manag 14:207–222

Troise C (2022) Exploring knowledge visualization in the digital age: an analysis of benefits and risks. Manag Decis 60:1116–1131

*Tsikerdekis M (2013) The effects of perceived anonymity and anonymity states on conformity and groupthink in online communities: a Wikipedia study. J Am Soc Inf Sci Technol 64:1001–1015

Tucker J, Foldesy J, Roos A, Rodt M (2017) How digital CFOs are transforming finance. Boston Consulting Group. https://www.bcg.com/de-de/publications/2017/function-excellence-how-digital-cfo-transforming-finance . Accessed 8 Nov 2022

Turban E, Watkins PR (1986) Integrating expert systems and decision support systems. MIS Q 10:121–136

*Udo GJ, Guimaraes T (1994) Empirically assessing factors related to DSS benefits. Eur J Inf Syst 3:218–227

Vahidov R, Elrod R (1999) Incorporating critique and argumentation in DSS. Decis Support Syst 26:249–258

*Vahidov R, He X (2009) Situated DSS for personal finance management: design and evaluation. Inf Manag 46:453–462

*Valacich JS, Schwenk C (1995a) Devil′ s advocacy and dialectical inquiry effects on face-to-face and computer-mediated group decision making. Organ Behav Hum Decis Process 63:158–173

*Valacich JS, Schwenk C (1995b) Structuring conflict in individual face-to-face and computer-mediated group decision making: carping versus objective devil’s advocacy. Decis Sci 26:369–393

*Valacich JS, Dennis AR, Connolly T (1994) Idea generation in computer-based groups: a new ending to an old story. Organ Behav Hum Decis Process 57:448–467

Van Scotter JR, Motowidlo SJ (1996) Interpersonal facilitation and job dedication as separate facets of contextual performance. J Appl Psychol 81:525–531

*Van Bruggen GH, Smidts A, Wierenga B (1996) The impact of the quality of a marketing decision support system: an experimental study. Int J Res Mark 13:331–343

*Van Bruggen GH, Smidts A, Wierenga B (1998) Improving decision making by means of a marketing decision support system. Manag Sci 44:645–658

Van Scotter JR, Motowidlo SJ, Cross TC (2000) Effects of task performance and contextual performance on systemic rewards. J Appl Psychol 85:526–535

Van der Stede WA, Young SM, Chen CX (2005) Assessing the quality of evidence in empirical management accounting research: the case of survey studies. Acc Organ Soc 30:655–684

*Verstegen JAAM, Huirne RB, Dijkhuizen AA, Sonnemans J, Cox JC (1998) Quantifying the effects of sow-herd management information systems on farmers’ decision making using experimental economics. Am J Agric Econ 80:821–829

Vessey I (1991) Cognitive fit: a theory-based analysis of the graphs versus tables literature. Decis Sci 22:219–240

*Vessey I, Galletta D (1991) Cognitive fit: an empirical study of information acquisition. Inf Syst Res 2:63–84

*Wang W, Reani M (2017) The rise of mobile computing for Group Decision Support Systems: a comparative evaluation of mobile and desktop. Int J Hum Comput Stud 104:16–35

*Warkentin ME, Sayeed L, Hightower R (1997) Virtual teams versus face-to-face teams: an exploratory study of a web-based conference system. Decis Sci 28:975–996

*Watson RT, DeSanctis G, Poole MS (1988) Using a GDSS to facilitate group consensus: some intended and unintended consequences. MIS Q 12:463–478

Watson HJ, Rainer RK Jr, Koh CE (1991) Executive information systems: a framework for development and a survey of current practices. MIS Q 15:13–30

*Webby R, O’Connor M (1994) The effectiveness of decision support systems: the implications of task complexity and DSS sophistication. J Inf Technol 9:19–28

*Weisband SP (1992) Group discussion and first advocacy effects in computer-mediated and face-to-face decision making groups. Organ Behav Hum Decis Process 53:352–380

*Wheeler BC, Valacich JS (1996) Facilitation GSS and training as sources of process restrictiveness and guidance for structured group decision making: an empirical assessment. Inf Syst Res 7:429–450

*Whitecotton SM (1996) The effects of experience and a decision aid on the slope scatter and bias of earnings forecasts. Organ Behav Hum Decis Process 66:111–121

*Wiczorek R, Manzey D (2014) Supporting attention allocation in multitask environments: effects of likelihood alarm systems on trust behavior and performance. Hum Factors 56:1209–1221

*Williams ML, Dennis AR, Stam A, Aronson JE (2007) The impact of DSS use and information load on errors and decision quality. Eur J Oper Res 176:468–481

*Wilson EV, Addo TBA (1994) An investigation of the relative presentation efficiency of computer-displayed graphs. Inf Manag 26:105–115

Witkin HA, Goodenough DR, Oltman PK (1979) Psychological differentiation: current status. J Pers Soc Psychol 37:1127–1145

Wood RE (1986) Task complexity: definitions of the construct. Organ Behav Hum Decis Process 37:60–82

*Workman M (2005) Expert decision support system use disuse and misuse: a study using the theory of planned behavior. Comput Hum Behav 21:211–231

*Yellen RE, Winniford M, Sanford CC (1995) Extraversion and introversion in electronically-supported meetings. Inf Manag 28:63–74

Yin RK (2015) Case study research: design and methods, 5th edn. Sage Publications, Thousand Oaks

Zigurs I, Buckland BK (1998) A theory of task/technology fit and group support systems effectiveness. MIS Q 22:313–334

*Zigurs I, Poole MS, DeSanctis GL (1988) A study of influence in computer-mediated group decision making. MIS Q 12:625–644

Zopounidis C, Doumpos M, Matsatsinis NF (1997) On the use of knowledge-based decision support systems in financial management: a survey. Decis Support Syst 20:259–277

Download references

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Author information

Authors and affiliations.

University of Duisburg-Essen, Lotharstr. 1, 47057, Duisburg, Germany

Jan A. Kempkes, Francesco Suprano & Andreas Wömpener

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Francesco Suprano .

Ethics declarations

Conflict of interest.

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 220 KB)

See Tables 10 , 11 and Fig.  3 .

Journal coverage across databases between 1950 and 2019. In this figure, Panel A displays the number of journals with a Chartered Association of Business Schools (CABS 2018 ) rating of three or better covered by each database between 1950 and 2019; and Panel B shows the number of journals with a CABS rating of three or better that are exclusively covered by one of the corresponding databases between 1950 and 2019. Data collection was conducted on September 22, 2019 primarily using the provided title lists. However, one database (i.e., Web of Science) did not provide any title list. Therefore, in this case we manually screened all relevant database entries

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Kempkes, J.A., Suprano, F. & Wömpener, A. How management support systems affect job performance: a systematic literature review and research agenda. Manag Rev Q (2023). https://doi.org/10.1007/s11301-023-00353-5

Download citation

Received : 08 November 2022

Accepted : 15 May 2023

Published : 11 July 2023

DOI : https://doi.org/10.1007/s11301-023-00353-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Digitalization
• Job performance
• Management support systems
• Systematic literature review

JEL Classification

• Find a journal
• Publish with us
• Track your research

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

A Literature Review on the Effects of Employee Relation on Improving Employee Performance

It is apparent that employees are the major valuable assets of an organization in which without them, hard to realize its basic objectives. To harvest more from employees it requires creating conducive working environment which satisfies the needs of individual employee as well as the manager of an organization. This conceptual paper tries to examine the basic concepts employee relation and its effects on employee performance through investigating a number of employee relationship management components such as communication, participative leadership, shared goals and value, mutual trust, motivation and conflict management. Moreover, the relationship between employee relations and employee performance is explored in-depth. The study also discusses on employee performance which comprises of the basic concept and measurements of performance. From a comprehensive review of literature on earlier studies, it was found that the preceding researches didn't make thorough endeavor to address the effects employee relation on employee performance. Finally, it was suggested that future researchers should investigate profoundly to come up with notable empirical results. Key words: Effects, Employee Relation, Improving, Employee Performance 1. INTRODUCTION

Related Papers

Bolarinwa I B R A H I M Bolarinwa

Maintaining healthy employee relations in an organization is a pre-requisite for organizational success. Strong employee relations are required for high productivity. Employee relations deal with avoiding and resolving issues concerning individuals which might arise out of or influence the work scenario. This study aimed at assessing the impact of good employee relations on employee performance. This study used a descriptive survey method. Convenience sampling technique was used to select the sample size of one hundred and thirty-nine (139). The instrument used was a questionnaire.. The Kendall rank correlation was used as the inferential statistics. This study reveals that a good employee relation has influence on employee performance. The findings also revealed that strong employment relations create a pleasant atmosphere within the work environment, motivation and company rules. It is concluded that employees have been seen as an organization's valuable assets. The nature and amount of work performed by them have a direct impact on the productivity of an organization. It is therefore recommended that a concern for equity and justice should characterize the relationship between management and employee, and this will require the communication of sufficient information about changes and developments. Also, fair policies and practices exhibited by the management of the organisation to create equal opportunities and provide equal treatment to employees with no bias which promotes a positive attitude towards organization and work among employees.

Entrepreneurship and Innovation Management Journal ISSN: 2310-0079

HAL (Le Centre pour la Communication Scientifique Directe)

EL KHAZZAR Aziz

Procedia - Social and Behavioral Sciences

Ömür Hakan Kuzu

Roopali Bajaj & Shalini Sinha

TJPRC Publication

Some HRM determinants, which if improved can build good employee relations in an organization and thus status of Employee Relationship can be improved in the organization. By quantifying HRM determinants and equating it to satisfaction of employees, the study has discovered and established that status of ERM in the state PSUs is not very good and measures of HR practices are not being implemented as they should be.

Budapest International Research and Critics Institute (BIRCI-Journal) : Humanities and Social Sciences

Tati Hartati

The background of this research was the low performance of the employee, which could be seen from the non optimal task implementation, overdue task completion, and low discipline. In this study, the researcher used analytical descriptive method, also literature study and field study as the instruments. The field study consisted of observation, interview, and questionnaire. The result of the research showed that the human relations done by the Sub-District Head had not been fully implemented based on the principles in human relations. It caused the low employee performance in the Argapura Sub-District Office of Majalengka Regency, so that the hypothesis of the researcher is true and can be accepted.

International Journal of Research and Analytical Reviews 2348-1269

Parveen Kumar , Ulka Tewari

Every individual shares a multifaceted relationship with colleagues at the workplace. As it is known to all that human beings are not machines who can start working within seconds or at a push button. They need someone to talk, to discuss ideas and to share their happiness and other emotions. We cannot expect an individual to start working like a robot with complete involvement in the work without knowing with whom he is working. A man is not indifferent; he needs people around. Without people working around the workplace turns hostile. An isolated environment demotivates an individual and spreads negativity which ultimately hampers performance. It will not be wrong to submit that workplaces are like homes; the more we are comfortable with each other, the more prosperity we gain. To achieve a common goal, mutual respect and a sense of safety are must at the workplace. It is an established notion that to work with people having different educational and cultural backgrounds is not that difficult rather than to work with people having different mindsets. It is much essential that employees share a healthy and happy relationship with each other at the workplace. Mutual understanding and mutual respect are the two hallmarks of meaningful Communication.A healthy bonding between the employer and the employee also strengthen productivity. When effective communication practices are in place at the workplace then employees feel more connected and committed to the organization. The Communication Breakdown at the workplace creates problems. This paper attempts to highlight the importance of Strong Employee Relations at Workplace and the Impact of these relations on the organisational success.

Vinayak webworld

JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES

kalpana koneru

Acharya Institute of Technology.

Mahak balani

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

RELATED PAPERS

International Journal of Innovation, Creativity and Change

Burmansah Burmansah

TRJ Tourism Research Journal

Ashish Ranga

Francis Barry

IJIRAE:: AM Publications,India

IJIRIS Journal Division

Pacific International Journal

Sinergy Iris

George Bongo

Proceedings of the 1st UMGESHIC International Seminar on Health, Social Science and Humanities (UMGESHIC-ISHSSH 2020)

Tri Cicik Wijayanti

International Journal of Recent Research in Commerce Economics and Management (IJRRCEM)

James O D H I A M B O Oringo , James Odhiambo Oringo

THE INFLUENCE OF ORGANIZATIONAL COMMUNICATION AND LEADERSHIP FACTORS ON THE PERFORMANCE OF EMPLOYEE

Sukmo Hadi Nugroho

Strategic Innovative Marketing

Petros Kalantonis

Proceeding of International Conference on Business, Economics, Social Sciences, and Humanities

Armitha Widyanti

Peter A. Murray

I Made Putrawan

IJIBE (International Journal of Islamic Business Ethics)

olivia fachrunnisa

Research in Personnel and Human Resources Management

René Schalk

The Ijes The Ijes

Journal of US-China Public Administration

Ikbal Hossain

Proceedings of the International Conference on Business, Economic, Social Science, and Humanities – Economics, Business and Management Track (ICOBEST-EBM 2019)

Rahma Wahdiniwaty

Proceedings of the 2018 International Conference on Islamic Economics and Business (ICONIES 2018)

Achmad Sani Supriyanto

International Journal of …

Harvard Deusto Business Research

Zia Ur Rehman

RELATED TOPICS

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024