The Shift to Socio-Organizational Drivers of Business Intelligence and Analytics Acceptance

The Shift to Socio-Organizational Drivers of Business Intelligence and Analytics Acceptance

Tanja Grublješič (Faculty of Economics, University of Ljubljana, Ljubljana, Slovenia), Pedro Simoes Coelho (NOVA Information Management School, Lisbon, Portugal) and Jurij Jaklič (Faculty of Economics, University of Ljubljana, Ljubljana, Slovenia)
Copyright: © 2019 |Pages: 28
DOI: 10.4018/JOEUC.2019040103
OnDemand PDF Download:
No Current Special Offers


The growing importance of IT in new ways of doing business, bringing with it ever greater empowerment, competencies, and skills of people associated with IT use, reveals that traditional views that individuals decide to accept new or emerging IT mostly based on their effort and performance perceptions or a similar individualistic utilitarian criteria may no longer satisfactorily explain the individual's acceptance behavior. Socio-organizational considerations encompassing normative and behavioral beliefs have so far only been recognized as potential additional predictors of acceptance, moderated, or mediated by certain effects and circumstances, whereas performance perceptions remain the strongest predictor of IS acceptance. The authors' mixed-methods study drives acceptance in the business intelligence and analytics (BI&A) context, comprising literature review, case studies and a survey, which reveals socio-organizational considerations have become more important than individualistic considerations arising from the visibility and recognition of the results of BI&A use in an organization.
Article Preview

1. Introduction

Fact-based (data-driven) decision-making using Business Intelligence and Analytics (BI&A) is regularly emphasized as a foundation for innovation and agility (Mao & Quan, 2015; Davenport et al., 2012; Kiron, Prentice, & Ferguson, 2012; Chen & Siau, 2011) in modern business environments. Organizations are ever more reliant on flexible information technology and big data analytical capabilities in today’s turbulent markets in order to quickly adapt to changes and stay competitive (Chen & Siau, 2011). Research shows that BI&A use provides value to these organizations by increasing organizational performance (Audzeyeva & Hudson, 2015; Sharma, Mithas, & Kankanhalli, 2014; Chen, Chiang, & Storey, 2012). Hence, it is in organizations’ interest to understand the core determinants of its acceptance. Knowing these influential individuals’ beliefs brings value to organizations as they can manage the organizational work environment to foster positive perceptions of BI&A use intentions (Mao & Palvia, 2006). Scholars have already raised the importance of researching individual-level drivers of BI&A acceptance, mentioning that while the adoption of BI&A has been fairly researched, individual-level acceptance has not yet been given adequate attention (Yoon, Ghosh, & Jeong, 2014; Alhyasat & Al-Dalahmeh, 2013).

User acceptance and continued use of IT/IS have already been extensively researched over the past decades (Trieu, 2016; Palvia et al., 2015; Venkatesh & Bala, 2008; Venkatesh et al., 2003). The basic technology acceptance model (TAM) determinants of behavioral intention perceived usefulness and perceived ease of use relate to an individual’s own considerations (Sinha, 2014), such as utility evaluations relating to usefulness of the IT and usability evaluations of the ease of applying the IT to a specific task (Stern et al., 2008). The accumulated evidence has consistently proven that performance perceptions are the main and strongest driver of IT/IS acceptance (Venkatesh, Thong, & Xu, 2012; Stern, Royne, Stafford, & Bienstock, 2008; Venkatesh, Morris, Davis, & Davis, 2003; Davis, 1989). This basic TAM research therefore predominantly looks at the technological aspects of IS acceptance by focusing on the individual’s utilitarian view of IS usage (i.e. Venkatesh & Bala, 2008; Venkatesh et al., 2003; Davis, 1989). The unified theory of acceptance and use of technology (UTAUT), besides the two mentioned TAM determinants, additionally incorporates social influence in the model of behavioral intention predictors. While normative and other socio-organizational aspects of IS acceptance have already been researched (Eckhardt, Laumer, & Waitzel, 2009) and included in core technology acceptance theories (i.e. Venkatesh et al., 2003; Moore & Benbasat, 1991), these have not been given adequate attention in these core models, being usually treated as potential additional predictors of IT/IS acceptance (Xiao & Wang, 2016; Eckhardt et al., 2009; Mao & Palvia, 2006). Petter, Delone, & McLean, (2013) argue that the cultural and people aspects are underrepresented in IS success models.

Complete Article List

Search this Journal:
Volume 34: 1 Issue (2022): Forthcoming, Available for Pre-Order
Volume 33: 6 Issues (2021): 4 Released, 2 Forthcoming
Volume 32: 4 Issues (2020)
Volume 31: 4 Issues (2019)
Volume 30: 4 Issues (2018)
Volume 29: 4 Issues (2017)
Volume 28: 4 Issues (2016)
Volume 27: 4 Issues (2015)
Volume 26: 4 Issues (2014)
Volume 25: 4 Issues (2013)
Volume 24: 4 Issues (2012)
Volume 23: 4 Issues (2011)
Volume 22: 4 Issues (2010)
Volume 21: 4 Issues (2009)
Volume 20: 4 Issues (2008)
Volume 19: 4 Issues (2007)
Volume 18: 4 Issues (2006)
Volume 17: 4 Issues (2005)
Volume 16: 4 Issues (2004)
Volume 15: 4 Issues (2003)
Volume 14: 4 Issues (2002)
Volume 13: 4 Issues (2001)
Volume 12: 4 Issues (2000)
Volume 11: 4 Issues (1999)
Volume 10: 4 Issues (1998)
Volume 9: 4 Issues (1997)
Volume 8: 4 Issues (1996)
Volume 7: 4 Issues (1995)
Volume 6: 4 Issues (1994)
Volume 5: 4 Issues (1993)
Volume 4: 4 Issues (1992)
Volume 3: 4 Issues (1991)
Volume 2: 4 Issues (1990)
Volume 1: 3 Issues (1989)
View Complete Journal Contents Listing