This chapter discusses the results of the theoretical and empirical analysis presented in the previous chapters of the manuscript. The limitations and further developments of the research were also presented. In general, our results show that information overload is less perceived than information underload in all the comparisons performed in the research. The empirical results of our research concerning the relationship between ERP systems and information overload/underload show that ERP systems do not affect the perception of information overload/underload. However, the empirical results show that respondents who adopt ERP perceive higher data accuracy, system reliability and, in general, a higher information processing capacity than do respondents who do not adopt an ERP. Furthermore, our results show that respondents who adopt BI systems do not perceive a different level of information overload/underload compared with respondents who do not adopt. However, a more detailed analysis shows that managers of companies adopting BI systems perceive a higher data accuracy, a higher level of information processing capacity, and a more regular reporting system, based on more systematic frequency. Empirical evidence on the effects of the simultaneous adoption of ERP and BI on information overload/underload and on the features of information flow show that respondents adopting both an ERP and a BI system do not perceive higher or lower information overload or information underload than do the other respondents. Finally, our results confirm prior studies on information processing capacity and information quality and suggest that reporting is one of the drivers of information quality.
The authors gratefully acknowledge the anonymous reviewers for the insightful suggestions provided to enhance the quality of this manuscript.
The authors also acknowledge the assistant editor of this book series, Maria Cristina Acocella, along with the editorial staff of Springer for their professional and proficient involvement in the production of this book.
The authors also gratefully acknowledge the Università degli Studi Internazionali di Roma (UNINT), which has made this study possible by providing financial support.
This study is part of a larger project on accounting information systems.
- Al-Hakim L (2007) Information quality management: theory and applications. IGI GlobalGoogle Scholar
- Berthold H, Rösch P, Zöller S, et al (2010) An architecture for ad-hoc and collaborative business intelligence. In: Proceedings of the 2010 EDBT/ICDT workshops. ACM, p 13Google Scholar
- Bharadwaj AS (2000) A resource-based perspective on information technology capability and firm performance: an empirical investigation. MIS Q 169–196Google Scholar
- Bingi P, Sharma MK, Godla JK (1999) Critical issues affecting an ERP implementation. Manag 16:7–14Google Scholar
- Blanco S, Lesca H (1998) Business intelligence: integrating knowledge into the selection of early warning signals. In: Workshop on knowledge managementGoogle Scholar
- Boyer J, Frank B, Green B, et al (2010) Business intelligence strategy: a practical guide for achieving BI excellence. Mc PressGoogle Scholar
- Brien JA, Marakas GM (2009) Management information system. Galgotia Pubn L994 3Google Scholar
- Burkhard RA, Meier M (2005) Tube map visualization: evaluation of a novel knowledge visualization application for the transfer of knowledge in long-term projects. J UCS 11:473–494Google Scholar
- Burstein F, Holsapple C (2008) Handbook on decision support systems 2: variations. Springer Science & Business MediaGoogle Scholar
- Corsi K, Trucco S (2016) The role of the CIOs on the IT management and firms’ performance: evidence in the Italian context. In: Strengthening information and control systems. Springer, pp 217–236Google Scholar
- da Costa RAG, Cugnasca CE (2010) Use of data warehouse to manage data from wireless sensors networks that monitor pollinators. In: 2010 Eleventh international conference on mobile data management (MDM). IEEE, pp 402–406Google Scholar
- Dell’Orco M, Giordano R (2003) Web community of agents for the integrated logistics of industrial districts. In: Proceedings of the 36th annual Hawaii international conference on system sciences, 2003. IEEE, p 10Google Scholar
- Eckerson WW (2005) The keys to enterprise business intelligence: critical success factors. TDWI RepGoogle Scholar
- Evans JR, Lindsay WM (2002) The management and control of quality. South-Western, Cincinnati, OHGoogle Scholar
- Ho J, Tang R (2001) Towards an optimal resolution to information overload: an infomediary approach. In Proceedings of the 2001 international ACM SIGGROUP conference on supporting group work, September, pp 91–96Google Scholar
- Imran M, Tanveer A (2015) Decision support systems: creating value for marketing decisions in the pharmaceutical industry. Eur J Bus Innov Res 3:46–65Google Scholar
- Juran JM (1992) Juran on quality by design: the new steps for planning quality into goods and services. Simon and SchusterGoogle Scholar
- Kelly D (2005) Business Intelligence: the smart way to track academic collections. Educ Q 28:48Google Scholar
- Lee MR, Lan Y (2007) From Web 2.0 to conversational knowledge management: towards collaborative intelligence. J Entrep Res 2:47–62Google Scholar
- Letsholo RG, Pretorius MP (2016) Investigating managerial practices for data and information overload in decision making. J Contemp Manag 13:767–792Google Scholar
- Li X, Qu H, Zhu Z, Han Y (2009) A systematic information collection method for business intelligence. In: International conference on electronic commerce and business intelligence, ECBI 2009. IEEE, pp 116–119Google Scholar
- Marchi L (1993) I sistemi informativi aziendali. GiuffrèGoogle Scholar
- McClave JT, Benson PG, Sincich T (1998) A first course in business statisticsGoogle Scholar
- Melinat P, Kreuzkam T, Stamer D (2014) Information overload: a systematic literature review. In: International conference on business informatics research. Springer, pp 72–86Google Scholar
- Nita B (2015) Methodological issues of management reporting systems design. Res Pap Wroclaw Univ Econ Nauk Uniw Ekon We WroclawiuGoogle Scholar
- O’Brien JA, Marakas GM (2006) Management information systems. McGraw-Hill, IrwinGoogle Scholar
- Piattini MG, Calero C, Genero MF (2012) Information and database quality. Springer Science & Business MediaGoogle Scholar
- Ranjan J (2009) Business intelligence: concepts, components, techniques and benefits. J Theor Appl Inf Technol 9:60–70Google Scholar
- Reeves CA, Bednar DA (1994) Defining quality: alternatives and implications. Acad Manage Rev 19:419–445Google Scholar
- Sangster A, Leech SA, Grabski S (2009) ERP implementations and their impact upon management accountants. JISTEM-J Inf Syst Technol Manag 6:125–142Google Scholar
- Shneiderman B (1996) The eyes have it: a task by data type taxonomy for information visualizations. In: Proceedings of IEEE symposium on visual languages, 1996. IEEE, pp 336–343Google Scholar
- Smith G, Ariyachandra T, Frolick M (2012) Business intelligence in the bayou: recovering costs in the wake. Organ Appl Bus Intell Manag Emerg Trends Emerg Trends 29Google Scholar
- Spira JB (2011) Overload! How too much information is hazardous to your organization. WileyGoogle Scholar
- Stvilia B, Twidale MB, Smith LC, Gasser L (2005) Assessing information quality of a community-based encyclopedia. In: IQGoogle Scholar
- Swain MR, Haka SF (2000) Effects of information load on capital budgeting decisions. Behav Res Account 12:171Google Scholar
- Zeithaml VA, Parasuraman A, Berry LL (1990) Delivering quality services. N Y Free Press Career Dev 11:63–64Google Scholar