EJIS Special Issue on Quantitative Research Methodology (January 2012) Volume 21, Issue 1
Special Issue on Quantitative Research Methodology, European Journal of Information Systems, 21(1), 1-112. #EJIS #AIS6
Introduction to the special issue: Chin, W., Junglas, I., & Roldán, J.. (2012). Some considerations for articles introducing new and/or novel quantitative methods to IS researchers. European Journal of Information Systems: Special Issue: Quantitative Research Methodology, 21(1), 1-5. Retrieved January 3, 2012, from ABI/INFORM Global. (Document ID: 2549639441).
1. Seddon, P., & Scheepers, R.. (2012). Towards the improved treatment of generalization of knowledge claims in IS research: drawing general conclusions from samples. European Journal of Information Systems: Special Issue: Quantitative Research Methodology, 21(1), 6-21. Retrieved January 3, 2012, from ABI/INFORM Global. (Document ID: 2549639421).
This paper presents a framework for justifying generalization in information systems (IS) research. First, using evidence from an analysis of two leading IS journals, we show that the treatment of generalization in many empirical papers in leading IS research journals is unsatisfactory. Many quantitative studies need clearer definition of populations and more discussion of the extent to which 'significant' statistics and use of non-probability sampling affect support for their knowledge claims. Many qualitative studies need more discussion of boundary conditions for their sample-based general knowledge claims. Second, the proposed new framework is presented. It defines eight alternative logical pathways for justifying generalizations in IS research. Three key concepts underpinning the framework are the need for researcher judgment when making any claim about the likely truth of sample-based knowledge claims in other settings; the importance of sample representativeness and its assessment in terms of the knowledge claim of interest; and the desirability of integrating a study's general knowledge claims with those from prior research. Finally, we show how the framework may be applied by researchers and reviewers. Observing the pathways in the framework has potential to improve both research rigour and practical relevance for IS research. [PUBLICATION ABSTRACT]
3. Indulska, M., Hovorka, D., & Recker, J.. (2012). Quantitative approaches to content analysis: identifying conceptual drift across publication outlets. European Journal of Information Systems: Special Issue: Quantitative Research Methodology, 21(1), 49-69. Retrieved January 3, 2012, from ABI/INFORM Global. (Document ID: 2549639451).
Unstructured text data, such as emails, blogs, contracts, academic publications, organizational documents, transcribed interviews, and even tweets, are important sources of data in Information Systems research. Various forms of qualitative analysis of the content of these data exist and have revealed important insights. Yet, to date, these analyses have been hampered by limitations of human coding of large data sets, and by bias due to human interpretation. In this paper, we compare and combine two quantitative analysis techniques to demonstrate the capabilities of computational analysis for content analysis of unstructured text. Specifically, we seek to demonstrate how two quantitative analytic methods, viz., Latent Semantic Analysis and data mining, can aid researchers in revealing core content topic areas in large (or small) data sets, and in visualizing how these concepts evolve, migrate, converge or diverge over time. We exemplify the complementary application of these techniques through an examination of a 25-year sample of abstracts from selected journals in Information Systems, Management, and Accounting disciplines. Through this work, we explore the capabilities of two computational techniques, and show how these techniques can be used to gather insights from a large corpus of unstructured text. [PUBLICATION ABSTRACT]
4. Evangelopoulos, N., Zhang, X., & Prybutok, V.. (2012). Latent Semantic Analysis: five methodological recommendations. European Journal of Information Systems: Special Issue: Quantitative Research Methodology, 21(1), 70-86. Retrieved January 3, 2012, from ABI/INFORM Global. (Document ID: 2549639411).
The recent influx in generation, storage, and availability of textual data presents researchers with the challenge of developing suitable methods for their analysis. Latent Semantic Analysis (LSA), a member of a family of methodological approaches that offers an opportunity to address this gap by describing the semantic content in textual data as a set of vectors, was pioneered by researchers in psychology, information retrieval, and bibliometrics. LSA involves a matrix operation called singular value decomposition, an extension of principal component analysis. LSA generates latent semantic dimensions that are either interpreted, if the researcher's primary interest lies with the understanding of the thematic structure in the textual data, or used for purposes of clustering, categorization, and predictive modeling, if the interest lies with the conversion of raw text into numerical data, as a precursor to subsequent analysis. This paper reviews five methodological issues that need to be addressed by the researcher who will embark on LSA. We examine the dilemmas, present the choices, and discuss the considerations under which good methodological decisions are made. We illustrate these issues with the help of four small studies, involving the analysis of abstracts for papers published in the European Journal of Information Systems. [PUBLICATION ABSTRACT]
5. Richard, P., Coltman, T., & Keating, B.. (2012). Designing IS service strategy: an information acceleration approach. European Journal of Information Systems: Special Issue: Quantitative Research Methodology, 21(1), 87-98. Retrieved January 3, 2012, from ABI/INFORM Global. (Document ID: 2549639431).
Information technology-based innovation involves considerable risk requiring foresight; yet our understanding of the way in which managers develop the insight to support new breakthrough applications is limited and remains obscured by high levels of technical and market uncertainty. This paper applies discrete choice analysis to support improved empirical explanation of how and why decisions are made in information systems (IS). A new experimental method based on information acceleration (IA) is also applied to improve prediction of future IS service strategies. Both explanation and prediction are important to IS research and these two behaviourally sound methods complement each other. Specifically, the combination of IA and discrete choice analysis removes misspecification artefacts from response variability and generates more accurate parameter estimates that better explain IS decision making. [PUBLICATION ABSTRACT]
6. Henseler, J., Fassott, G., Dijkstra, T., & Wilson, B.. (2012). Analysing quadratic effects of formative constructs by means of variance-based structural equation modelling. European Journal of Information Systems: Special Issue: Quantitative Research Methodology, 21(1), 99-112. Retrieved January 3, 2012, from ABI/INFORM Global. (Document ID: 2549639401).
Together with the development of information systems research, there has also been increased interest in non-linear relationships between focal constructs. This article presents six Partial Least Squares-based approaches for estimating formative constructs' quadratic effects. In addition, these approaches' performance is tested by means of a complex Monte Carlo experiment. The experiment reveals significant and substantial differences between the approaches. In general, the performance of the hybrid approach as suggested by Wold (1982) is most convincing in terms of point estimate accuracy, statistical power, and prediction accuracy. The two-stage approach suggested by Chin et al (1996) showed almost the same performance; differences between it and the hybrid approach - although statistically significant - were unsubstantial. Based on these results, the article provides guidelines for the analysis of non-linear effects by means of variance-based structural equation modelling. [PUBLICATION ABSTRACT]
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