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Micro, small, and medium enterprises (MSMEs) can play a significant role in achieving sustainable development goals (SDGs) as they have the ability to reduce unemployment. Digitalization helps MSMEs in a number of ways, including lowering transaction costs, quickening access to information, and bettering communication with extended supply chain members. This chapter aims to understand the level of digitalization in MSMEs in an emerging economy such as Bangladesh. MSMEs in Bangladesh account for 25% of the gross domestic product and employ 87% of civilians. This chapter builds on qualitative data from 60 MSMEs from various manufacturing and service sectors such as textile, retail, food delivery, IT companies, etc. The interviews were semi-structured and followed an interview protocol. The length of interviews varied between 40 and 50 minutes. Content analysis was used to analyze the data. Findings suggest that counterintuitively the level of digitization in MSMEs is not low in Bangladesh. Many micro and small enterprises use MS Excel to help them manage customer and product data. Medium Enterprises use Enterprise Resource Planning (ERP) software for planning enterprise-wide resources. Some medium enterprises also use powerful data analytics software such as Oracle, Power BI, Google Analytics, Python, and SPSS. Results also reveal barriers to digitization in MSMEs, which include a lack of employee awareness, training, and motivation of top management. This chapter maps the digitalization levels in MSMEs in Bangladesh and provides implications for SGDs. The chapter also presents policy recommendations for improving the digitalization level in emerging economies.

Supplier selection is a complex and strategic activity needed in every organization, involving many stakeholders and different attributes as price, delivery performance, and product quality. Globalization, in the last decades, increased the competitiveness between vendors, enhancing the use of decision models to support the best choice based on optimizations and bidding variations due to specific needs. This chapter presents three models of multi-dimensional auctions to improve an international humanitarian NGO process procurement efficiency by reducing procurement costs and the decision-making process time. These models have the advantage to be easily implementable in typically complex environments where there is a large number of categories, suppliers, and other features.

The first proposed model uses combinatorial auctions and is suited for procurement, where suppliers can benefit from cost complementarity. The second one uses volume discount auctions and is suited for volumetric purchases, where discounts for large quantities are common. The third one is a multi-attribute model, which computes the best possible solution considering several criteria and can be used in case of complex purchases that involve various categories and trade-offs and are subject to spot prices.

Several design considerations for this type of auctions are reviewed, as well as the mathematical formulation to determine the best alternative (i.e., winner) that can be solved using simple tools like Microsoft Excel. The models are optimized by a mixed-integer programming, and the multi-attribute one is developed using multi-criteria decision analysis (MCDA). All three models developed in this research showed superior results compared to the baseline, being between 9% and 20% more efficient than a regular supplier selection (singly choosing the lowest price) and improving the bidding compliance.

In this chapter, we applied Data Envelopment Analysis (DEA) to a group of property and casualty insurance companies' data from 2018 to 2020. The calculated relative efficiencies were compared with selected traditionally used financial measures. We conclude that DEA and its relative efficiency calculation provide a consistent measure with selected IRIS ratios. The result and method can be used for situations when multiple ratios and change-based financial metrics provide inconsistent conclusions.

This paper makes a significant contribution to the growing field of digital inequality research by developing an operational definition of emotional costs. To examine this understudied aspect of digital inequalities, we build on Van Dijk’s concept of mental access. We define emotional costs as anxiety toward using information and communication technologies instigated by a lack of prior technology experience and limited computer access.

We examined the influence of emotional costs on lower-income students’ technology efficacy, academic efficacy, and computer application proficiency in the context of a computing intervention. Specifically, we examined the relationship between home and school computer usage with self-perceived technology efficacy, computer application proficiency, and academic efficacy. Data from surveys of 972 students were analyzed in order to better understand the importance of technology access on our outcome variables. We also investigated the possible mediation effects of emotional costs on our outcome variables.

The results revealed that home computer usage was a determinant of students’ self-perceived technology efficacy while shared school access was not. After conducting mediation tests, the results further indicated that emotional costs mediate the effects of home computer usage on technology efficacy.

We conclude that emotional costs might help explain why access inequalities lead to skill inequalities in the context of computing interventions and offer a replicable operational definition for future studies.

Knowledge workers labor to meet their business goals with the support of practical information technology (IT) tools. IT advances can be organizational enablers, when aligned with business goals, and when selectively applied. Workplace leaders and their workers often experience a productivity paradox. This paradox forms an operational limit for current knowledge workers and organizational success. Performance management steps within a Balanced Scorecard (BSC) framework can help overcome workplace productivity paradoxes. The BSC frames and tabulates lagging and leading indicators of IT tools’ usage and soft skill engagements. These adaptive measures dashboard workplace progress and success for organizations of all sizes and in public and private sectors. Lessons can be learned from BSC deployment successes in several business sectors. Valued practices exist to pick / monitor / adapt organizational capability objectives, measures and HR initiatives. Can right IT tool(s) or application(s) help achieve aligned business goals? Yes. Certain IT applications can favorably frame learning and development (L&D) efforts and metrics for knowledge workers as most valuable players, or MVPs.How do knowledge workers and their business leaders manage and leverage these IT applications for employee L&D to improve organizational capabilities? How do they address and adapt to complex and chaotic business conditions, and manage disruptive technologies: a. Artificial Intelligence (AI), b. The Internet of Things (IoT), and c. Data Analytics? Prudent managers and workers can accommodate these conditions and disruptions with agile, productive BSC approaches to generate productivity-ware and to attain their aligned business goals.

Professionals who carry out the forensic accounting profession must have an extensive knowledge of accounting, as well as an effective knowledge of law, auditing, internal audit, business management, psychology, crime science, and, in particular, computer technologies. In today’s digital business environment, it has become difficult to identify fraudulent transactions with traditional methods. Developments in information (data) and information technology have helped increase anti-fraud control programs and fraud research opportunities. In particular, fraudulent financial reporting disrupts the reliability, accuracy, and efficiency of financial markets in terms of existence and continuity. The forensic accounting profession has been able to improve the effectiveness of inspections by using big data techniques, data analytics, and algorithms (Rezaee, Lo, Ha, & Suen, 2016; Seda & Kramer, 2014; Singleton & Singleton, 2010).

The aim of the author, in this chapter, is to evaluate the contribution of using big data techniques in forensic accounting applications and the skills that will be provided to students while integrating these techniques in forensic accounting trainings. For this purpose, studies on forensic accounting education and their applications were reviewed. In addition, opinions were evaluated by considering the relevant literature about the importance of big data, benefits of big data, use of big data techniques, and interest shown of them.

The balanced scorecard (BSC) allows firms to place importance on both financial and nonfinancial performance measures in four perspectives for developing and implementing corporate strategy and performance evaluation. The BSC literature however provides minimal insight on how to set targets, how to weigh measures when evaluating managers and the firm, and how to resolve conflicts that arise in the BSC process. Researchers have attempted to fill these gaps using two contending approaches. In particular, Datar et al. (2001) uses an agency model to select the optimal set of weights and more recently Herath et al. (2009) develop a mathematical programming–based collaborative decision model to find the optimal (or approximately optimal) set of target and weights considering inputs from two parties. In this article, we apply the Herath et al. (2009) model to a detailed BSC example. We demonstrate how the collaborative BSC model can be implemented in Microsoft Excel by practitioners to minimize BSC conflicts. Finally, we discuss how the model facilitates alignment and a culture of open reporting (information sharing) around the BSC that is necessary for its effective implementation.