IS enables organizations to improve their productivity, streamline their business processes, and better understand the challenges and opportunities facing their business. These benefits can further accrue to individuals and organizations when they adopt and use the systems. While the benefits of IS are multifarious, IS adoption remains challenging. The far-reaching consequences of IS motivate research examining the antecedents of successful IS adoption both at individual and organizational levels. To examine the complexity of IS adoption, we undertook an interpretive case study of SBR adoption in Australia. We contend that SBR’s context in Australia offers distinctive perspectives on the complexity of IS adoption. We found that IS adoption decisions can be based on both constructivist and ecological rationalities. Our findings can provide insight in improving understanding of the benefits of SBR and have implications for companies, regulators, standard setters, and the accounting profession, more generally.
Abstract Considerable research has been conducted into the relation between students' level of previous accounting knowledge and their subsequent performance in first year university‐level accounting. This study considers variables for academic performance and previous accounting knowledge in an attempt to quantify the advantage that high school accounting gives students entering tertiary business courses. The results indicate that for students entering tertiary courses with similar academic ability, i.e., obtained the same entrance score, the first year tertiary accounting result obtained by a student who studied accounting previously is between one and two grades higher than that of a student who did not study accounting at high school.
This paper investigates the effects of personality characteristics on individuals’ abilities to resolve ambiguity in an information retrieval environment. In particular, this research examines the effects on query performance of the interaction of personality characteristics (as measured using the NEO PI-R) with information requests that contained extraneous, syntactic, or both extraneous and syntactic ambiguities. The results indicate that ambiguity affected performance. The results also show that various personality dimensions significantly affect end-users’ abilities to compose accurate queries. Neuroticism, agreeableness, openness to experience, and conscientiousness affected the number of errors made in the query formulations. Conscientiousness affected the length of time taken to compose the queries and neuroticism affected the confidence end users had in the accuracy of their queries. In addition, the results indicated that, while the personality dimensions affected performance, there was no interaction between the personality dimensions and ambiguity.
In the past few years service computing has been gradually shaping a new Information Technology (IT) branch and shifting conventional paradigms of business practices and research. Such a transformation involving both technology and management issues, is triggering a hot discussion in the Information Systems (IS) community. Currently, however, little research is available to explore and identify the topics related to service research that IS researchers can undertake. As such, this paper proposes a systematic framework of IT-enabled service research with the purpose of discovering in what scenarios IS researchers can be involved. The developed framework, represented by a hierarchical model along with a relational model, is systematically derived from extant MIS literature using General System Theory (GST). In addition, how to deploy this framework in actual research is discussed from the perspective of formulating research questions. Validation of the framework is achieved by mapping the extant literature into the framework.
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.
The data structure of an information system can significantly impact the ability of end users to efficiently and effectively retrieve the information they need. This research develops a methodology for evaluating, ex ante, the relative desirability of alternative data structures for end user queries. This research theorizes that the data structure that yields the lowest weighted average complexity for a representative sample of information requests is the most desirable data structure for end user queries. The theory was tested in an experiment that compared queries from two different relational database schemas. As theorized, end users querying the data structure associated with the less complex queries performed better. Complexity was measured using three different Halstead metrics. Each of the three metrics provided excellent predictions of end user performance. This research supplies strong evidence that organizations can use complexity metrics to evaluate, ex ante, the desirability of alternate data structures. Organizations can use these evaluations to enhance the efficient and effective retrieval of information by creating data structures that minimize end user query complexity.
With the proliferation of relational database programs for PC's and other platforms, many business end-users are creating, maintaining, and querying their own databases. More importantly, business end-users use the output of these queries as the basis for operational, tactical, and strategic decisions. Inaccurate data reduce the expected quality of these decisions. Implementing various input validation controls, including higher levels of normalisation, can reduce the number of data anomalies entering the databases. Even in well-maintained databases, however, data anomalies will still accumulate. To improve the quality of data, databases can be queried periodically to locate and correct anomalies. This paper reports the results of two experiments that investigated the effects of different data structures on business end-users' abilities to detect data anomalies in a relational database. The results demonstrate that both unnormalised and higher levels of normalisation lower the effectiveness and efficiency of queries relative to the first normal form. First normal form databases appear to provide the most effective and efficient data structure for business end-users formulating queries to detect data anomalies.
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