Contents
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 A Brief Overview of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Theoretical Contributions of the Present Work . . . . . . . . . . . . . . . 3
1.3 Managerial Implications of the Present Work . . . . . . . . . . . . . . . . 5
1.4 Structure of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2 Enterprise Resource Planning Systems . . . . . . . . . . . . . . . . . . . . . . . 13
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 The Evolution of ERP Systems . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3 Information Quality and ERP . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.3.1 Information Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.3.2 ERP System for Information Quality. . . . . . . . . . . . . . . . . 21
2.4 Critical Success Factor for ERP Implementation . . . . . . . . . . . . . . 23
2.5 Critical Success Factors for ERP Post-implementation . . . . . . . . . 26
2.6 Advantages and Disadvantages of ERPs . . . . . . . . . . . . . . . . . . . 27
2.6.1 Potential Benefits of ERP Adoption . . . . . . . . . . . . . . . . . 27
2.6.2 A Framework for Classifying the Benefits of ERP Systems . . . . . . . . . . . 30
2.6.3 Potential Disadvantages of ERP Adoption . . . . . . . . . . . . . 31
2.7 ERP as a Driver of Alignment Between Management Accounting Information and Financial Accounting
Information . . . . . . . . 32
2.8 The Managerial Role of the Chief Information Officer . . . . . . . . . 33
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3 Business Intelligence Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.2 Business Intelligence and Companies Needs. . . . . . . . . . . . . . . . . 44
3.3 BI for Management Information Systems Needs . . . . . . . . . . . . . . 48
3.3.1 Alignment to Group Logics . . . . . . . . . . . . . . . . . . . . . . . 48
3.3.2 Coordination and Technical-Organizational Integration . . . . . . . . . . . . . 50
3.3.3 Improvement of Data Management and Decision Support Information . . . . . . . . . . . . . . 51
3.3.4 Improvement in Communications . . . . . . . . . . . . . . . . . . . 53
3.4 BI for Strategic Planning Needs . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.4.1 Monitoring of Environmental Signals . . . . . . . . . . . . . . . . 55
3.4.2 Planning and Control Requirements . . . . . . . . . . . . . . . . . 57
3.4.3 Innovative BI Tools for the Adaptation to Environmental Conditions . . . . . . . . 59
3.5 BI for Marketing Needs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.6 BI for Regulations and Fraud Detection Needs . . . . . . . . . . . . . . . 61
3.7 Critical Success Factors of BI Implementation and Adoption . . . . 62
3.8 BI Maturity Models and Lifecycle . . . . . . . . . . . . . . . . . . . . . . . . 65
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
4 ERP and BI as Tools to Improve Information Quality in the Italian Setting: The Research Design . . . . 75
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
4.2 Literature Review Supporting the Research Design . . . . . . . . . . . . 76
4.2.1 Literature Review on Information Overload and Information Underload. . . . . . . . . . . . . . 76
4.2.2 Links Between Information Overload/Underload and ERP Systems . . . . . . . . . . . . . 78
4.2.3 Links Between Features of Information Flow and ERP Systems . . . . . . . . . . . . . . . 79
4.2.4 Links Between Information Overload/Underload and Business Intelligence Systems . . . . . 80
4.2.5 Links Between Features of Information Flow and Business Intelligence Systems . . . . . . . . . 82
4.2.6 The Combined Use of ERP and Business Intelligence:
Information Overload/Underload and Features of Information Flow. . . . . . . . . . . . 83
4.2.7 Literature Review on Information Quality . . . . . . . . . . . . . 84
4.2.8 Links between Features of Information Flow and Information Quality . . . . . . . . . . . 87
4.3 Sample Selection and Data Collection . . . . . . . . . . . . . . . . . . . . . 89
4.4 Variable Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
4.4.1 Research Variable Measurement . . . . . . . . . . . . . . . . . . . . 90
4.4.2 Variable Measurement: Control Variables . . . . . . . . . . . . . 94
4.5 Factor Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
5 ERP and BI as Tools to Improve Information Quality in the Italian Setting: Empirical Analysis . . . . . 105
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
5.2 Descriptive Statistics and Correlation Analysis . . . . . . . . . . . . . . . 106
5.3 Research Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
5.3.1 T-Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
5.3.2 Regression Analysis for Research Variables . . . . . . . . . . . 109
5.4 Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
5.4.1 T-Test: Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . . 111
5.4.2 Empirical Results for Regression Analysis . . . . . . . . . . . . 117
5.5 Additional Analysis: Empirical Results on the Chief Information Officer Dataset . . . . . . . . . . 118
5.5.1 Regression Analysis for Chief Information Officers . . . . . . 118
5.5.2 Empirical Results of the Regression Analysis on Chief Information Officers . . . . . . . . . . . 118
5.5.3 T-Test: Empirical Results of the Analysis of Chief Information Officers . . . . . . . . . . . 124
5.6 Summary Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
5.6.1 Summary Results for the Entire Dataset of Respondents . . . . . . . . . . . . . . . . . . . . 127
5.6.2 Summary Results for Chief Information Officers . . . . . . . . 130
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
6 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
6.2 ERP, Information Overload/Underload and Features of Information Flow . . . . . . . . . . . . . 133
6.3 BI, Information Overload/Underload and Features of Information Flow . . . . . . . . . . . . . . 135
6.4 The Combination of ERP and BI for Information Overload/Underload and Features of Information Flow . . . . . . . . . 137
6.5 Information Quality and Features of Information Flow . . . . . . . . . 137
6.6 Limitations and Further Development . . . . . . . . . . . . . . . . . . . . . 139
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
Preface
Nowadays, Information Technology (IT) innovations, the advent of the Internet, and the ease of finding and sharing information are all elements that contribute to obtaining overwhelming amounts of data and information. On the one hand, managers can now easily find and store information, and on the other hand, this hyper-amount of data does not allow us to distinguish between “good” and “bad” information. Furthermore, the data and information stored in enterprise databases may be obsolete, inaccurate, irrelevant, or partial. In other words, companies do not find it difficult to acquire and store a huge “quantity” of data and information. Their problem instead is to obtain an adequate level of “quality” of data and information.
The point is that the increased volume of data and information can undermine the capacity of companies to discern quality from non-quality data and information, and this difficulty is even more crucial when we consider that we are living in an information economy where data, information, and knowledge become extremely strategic for companies. Therefore, the quality of information deserves particular attention.
Although IT has played a key role in bringing about information overload and underload, possible solutions to these phenomena are still being sought in the IT field. Integrated systems, data management systems, data warehousing, data mining, and knowledge discovery tools are some examples of IT solutions that companies are adopting to deal with information overload/underload. One of the most effective solutions seems to be the implementation of Enterprise Resource Planning (ERP) systems, which improve data quality, data integrity, and system integration.
In addition to improving data quality and system integration, companies also aim at improving their capacity to perform data analysis. As a matter of fact, in order to pursue the objective of improving the quality of information, companies need to pay attention both to the quality of incoming data and to the capacity to analyze it and deliver the resulting information to the right person, at the right time. Therefore, Business Intelligence (BI) systems are another important solution that companies use to improve their data analysis and processing capabilities and to recognize and select relevant data for a more effective decision-making process.
This manuscript will examine, through an empirical analysis, the role played by ERP and BI systems in reducing or managing information overload/underload and thus in improving the information quality perceived by the Italian manager. The research is based on the idea that the improvement of information systems, achievable by means of ERP and BI systems, may reduce or eliminate information overload/underload. We also investigate whether the combined adoption of ERP and BI systems is more effective in dealing with information overload/underload than would be the single adoption of ERP or BI systems. Furthermore, the research presented in this book examines the influence that ERP and BI systems may have on the features of information flow—such as information processing capacity, communication and reporting, the frequency of meetings, and information sharing —and, in turn, the influence of information flow features on information quality.
The research was made possible by the financial support of the Università degliStudi Internazionali di Roma (UNINT). This study is part of a larger project on accounting information systems.
Chapter 1
Introduction
The manuscript aims at analyzing the role played by ERP, BI systems and the combined adoption of ERP and BI in reducing or managing information overload/underload, and thus in improving the information quality perceived by Italian managers. Furthermore, the manuscript analyzes the effects of information flow on the perceived information quality. The analysis was carried out through a survey on a sample of 300 managers who work for Italian listed or non-listed companies of varying size. The participants—Chief Information Officers, Chief Technology Officers, Chief Executive Officers and Controllers—were randomly selected from the LinkedIn social network database, since some scholars have recently stressed the relevance and widespread use of this social media application.
We received back 79 answers, with a 26% rate of response. A set of regression and t-test analyses was performed. The main practical implication of our research is that it helps managers understand the impacts an investment in ERP or BI systems could have on information management and on the decision-making process. Other practical implications pertain to the methodology used in our study: in fact, managers may conduct an internal survey similar to that used for this study to assess the pre-conditions for investing in ERP and/or BI systems by (a) examining the information quality perceived by employees and managers, (b) analyzing the employees’ and managers’ perception of information overload/underload, and (c) investigating the perception of employees and managers regarding the current IT.
