Paul L. Bowen

Enhancing IT governance practices: A model and case study of an organization's efforts

For many organizations, Information Technology (IT) enabled business initiatives and IT infrastructure constitute major investments that, if not managed properly, may impair rather than enhance the organizations competitive position. Especially since the advent of Sarbanes–Oxley (SOX), both management and IT professionals are concerned with design, implementation, and assessment of IT governance strategies to ensure that technology truly serves the needs of the business. Via an in-depth study within one organisation, this research explores the factors influencing IT governance structures, processes, and outcome metrics. Interview responses to open-ended questions indicated that more effective IT governance performance outcomes are associated with a shared understanding of business and IT objectives; active involvement of IT steering committees; a balance of business and IT representatives in IT decisions; and comprehensive and well-communicated IT strategies and policies. IT governance also plays a prominent role in fostering project success and delivering business value.

The effects of information request ambiguity and construct incongruence on query development

This paper examines the effects of information request ambiguity and construct incongruence on end users ability to develop SQL queries with an interactive relational database query language. In this experiment, ambiguity in information requests adversely affected accuracy and efficiency. Incongruities among the information request, the query syntax, and the data representation adversely affected accuracy, efficiency, and confidence. The results for ambiguity suggest that organizations might elicit better query development if end users were sensitized to the nature of ambiguities that could arise in their business contexts. End users could translate natural language queries into pseudo-SQL that could be examined for precision before the queries were developed. The results for incongruence suggest that better query development might ensue if semantic distances could be reduced by giving users data representations and database views that maximize construct congruence for the kinds of queries in typical domains

An Empirical Investigation of End-User Query Development: The Effects of Improved Model Expressiveness vs. Complexity

Data models provide a map of the components of an information system. Prior research has indicated that more expressive conceptual data models (despite their increased size) result in better performance for problem solving tasks. An initial experiment using logical data models indicated that more expressive logical data models also enhanced end-user performance for information retrieval tasks. However, the principles of parsimony and bounded rationality imply that, past some point, increases in size lead to a level of complexity that results in impaired performance. The results of this study support these principles. For a logical data model of increased but still modest size, users composing queries for the more expressive logical data model did not perform as well as users composing queries for the corresponding less expressive but more parsimonious logical data model. These results indicate that, when constructing logical data models, data modelers should consider tradeoffs between parsimony and expressiveness.

Cognitive style factors affecting database query performance

Abstract As end-user computing becomes more pervasive, an organizations success increasingly depends on the ability of end-users, usually in managerial positions, to extract appropriate data from both internal and external sources. Many of these data sources include or are derived from the organizations accounting information systems. Managerial end-users with different personal characteristics and approaches are likely to compose queries of differing levels of accuracy when searching the data contained within these accounting information systems. This research investigates how cognitive style elements of personality influence managerial end-user performance in database querying tasks. A laboratory experiment was conducted in which participants generated queries to retrieve information from an accounting information system to satisfy typical information requirements. The experiment investigated the influence of personality on the accuracy of queries of varying degrees of complexity. Relying on the Myers–Briggs personality instrument, results show that perceiving individuals (as opposed to judging individuals) who rely on intuition (as opposed to sensing) composed queries more accurately. As expected, query complexity and academic performance also explain the success of data extraction tasks.

Modeling a Business Process and Querying the Resulting Database: Analyzing RFID Data to Develop Business Intelligence

ABSTRACT: This case supports the high‐level objective of learning to model business situations to design databases and query them to solve business problems. Students analyze Radio Frequency Identification (RFID) and related fulfillment data to develop business intelligence for a grower of bedding plants selling to big‐box retailers. Students prepare a business process diagram to understand the business process; identify objectives for detecting and correcting data errors and analyzing business performance; construct a Resource, Event, Agent (REA) diagram to specify a database design; implement the database and query it to satisfy business objectives; communicate results; document data error correction procedures; and prepare adjusting journal entries. The case is appropriate for students with moderate database querying proficiency. The cases managerial and financial accounting and internal control implications make it suitable for courses in Accounting Information Systems (AIS) and database systems for ...

The effects of normalization on end-user query errors: An experimental evaluation

As a result of the shortage of professional programmers to extract timely information from databases, end-users are increasingly developing their own database queries. Because end-user querying is error-prone, characterizing the sources of query errors and using that knowledge to improve the effectiveness of end-user query development can improve the quality of information used for decision making. This paper reports the results of an experiment that investigated the effect of normalization level on query errors. The results show that query errors vary with the normalization level of the database structure and confirm previous findings about query errors increasing with task complexity. End-users querying a first normal form data structure make fewer errors than end-users querying an unnormalized data structure or a third normal form data structure. Furthermore, end-users querying a third normal form data structure make fewer errors than end-users querying an unnormalized data structure.

Reliability factors in business software: volatility, requirements and end-users

Many business-oriented software applications are subject to frequent changes in requirements. This paper shows that, ceteris paribus, increases in the volatility of system requirements decrease the reliability of software. Further, systems that exhibit high volatility during the development phase are likely to have lower reliability during their operational phase. In addition to the typically higher volatility of requirements, end-users who specify the requirements of business-oriented systems are usually less technically oriented than people who specify the requirements of compilers, radar tracking systems or medical equipment. Hence, the characteristics of software reliability problems for business-oriented systems are likely to differ significantly from those of more technically oriented systems.

An experimental investigation of the effects of artificial intelligence systems on the training of novice auditors

The primary objective of this research is to investigate the impact of task‐technology fit on users’ performance when using artificial intelligence systems for auditing tasks. Four artificial intelligence auditing systems, two problem‐solving programs, and four questionnaires were developed. A laboratory experiment was performed with 292 undergraduate auditing students. The results suggested that the effect of task‐technology fit on accuracy in solving problems was marginal for case‐based reasoning with unstructured tasks. No significant effect was found on problem‐solving accuracy for rule‐based reasoning with structured tasks. The task‐technology fit, however, marginally increased users’ certainty of the correctness of their solutions.

The effects on end-user query performance of incorporating object-oriented abstractions in database accounting systems

Object‐oriented (OO) advocates assert that concepts such as generalization‐specialization hierarchies (GSHs) and abstract data types (ADTs) make information systems more usable by increasing the level of abstraction of the data structure. This study analyzes the effects of GSHs and ADTs on the performance of end‐users of accounting information systems. Two groups of experimental participants interactively developed Structured Query Language (SQL) queries to answer ten business questions. The control group (n = 28) used data stored in a traditional relational schema. The experimental group (n = 31) used the same data stored in an OO schema that included GSHs and ADTs. Both schemas implemented the same database accounting model of the sales cycle of a hypothetical company. Participants using the higher abstraction (OO) schema with GSHs and ADTs made fewer semantic errors than did participants using the traditional relational schema. The OO participants also required less time to formulate their queries. The...

Data quality initiatives: striving for continuous improvements

Although managers consider accurate, timely, and relevant information as critical to the quality of their decisions, evidence of large variations in Data Quality (DQ) abounds. Over a period of 12 months, the research project reported herein investigated and tracked DQ initiatives undertaken by the participating organisation. The results confirmed that, to ensure and maintain DQ, commitment to continuous DQ improvement is necessary. Most importantly, the research found that sustaining DQ gains requires mutual understanding by operations personnel, management, and funding sources as well as the provision of adequate incentives and modifications to institutional constraints.