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The purpose of this paper is to identify potential reasons for inconsistent results of the economic value of information technology (IT) investments. Furthermore, the study aims to develop framework and propositions to explore future opportunities and directions for research that examine the returns on IT investments.

This study conducted a longitudinal analysis of the literature review concerning the impact of IT investments on firm performance to identify the reasons to the so-called “IT productivity paradox” and to explore future opportunities and directions for future research.

The study provides and discusses the reasons for the inconsistent results in the prior research that examines IT investments payoff and suggested a framework and propositions for future research. Results of prior studies should be interpreted in the context of research questions raised, data used, level of analysis, IT investment measures, firm performance measures, time horizon and industry characteristics.

IT managers and researchers should align IT investments with the environment in which a firm operates and competes and with firm’s business strategies as important determinants of the return on IT investments.

Understanding the link between firm performance and IT investments assists researchers and practitioners to understand why firms continue to pour enormous resources into IT and, more importantly, specifies the conditions under which firms are likely to achieve competitive advantages from their IT investments.

This chapter describes three active learning activities developed for use in the introductory financial accounting class: an interview with a financial statement user, an internal control paper, and a financial statement project where students analyze two competing businesses. We gathered student surveys and direct assessment data to see if these activities add value to the introductory accounting course.

The learning activities were originally developed using Fink’s (2003) Taxonomy of Significant Learning, aligning the activities with Fink’s learning dimensions, which also support the higher order learning skills in Bloom’s revised taxonomy. Students completed surveys by comparing how well traditional class activities (i.e., homework and tests) and the new activities support the core competencies of the American Institute for Certified Public Accountants (AICPA). We also asked students open-ended questions on how they felt about these new activities. Researchers then compared pre- and postadoption assessment data to investigate the impact of the new learning activities on class completion rates and grades.

Based on faculty comments and student survey results, the three active learning assignments appear to be more effective in developing many of the AICPA’s core competencies and real world skill sets valued by professionals, providing more value than traditional teaching methods. In addition, the passing rates in the course at the Youngstown State University increased by 12% after adopting the learning innovations.

This study aims to explore the safe-haven properties of sukuk and analyze the co-movement and interdependence between sukuk and conventional bond indices to provide insights into the potential role of diversification.

The study uses the data set from 2012 to 2022, retrieved from the Eikon Reuter database. Different machine learning tools such as decision trees, random forests, gradient boosting and deep neural networks have been applied to capture the non-linear relationship and co-movement among the variables. Furthermore, K-clustering captures the hidden patterns and periods of high and low co-movements.

The results state that the sukuk and conventional bond indices exhibit various degrees of co-movement influenced by regional and global market sentiments. The clustering analysis shows strong positive and negative correlations. The sukuk shows some instances of zero co-movement, but the results are inconsistent across all scenarios. Moreover, investors need to do their research first before investing in sukuk.

This study uniquely applies K-clustering and advanced machine learning tools to understand the nonlinear relationship among variables better. In contrast, the previous studies mainly focused on linear relationships. It is critical to understand that financial variables tend to have nonlinear relationships, and these techniques best suit those needs.