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This chapter provides an overview of entrepreneurship clubs. It charts the development of these organisations, as a form of extracurricular activity. It introduces different forms of entrepreneurship clubs, such as Junior Achievement (JA) and Enactus, and explains how they grew from 1919 to the present. It also illustrates the differences between self-organised clubs, organised programs using clubs as a learning method, structured societies and nationally organised cooperative societies. The second part introduces research on student clubs in entrepreneurship education. It explores the benefits of clubs. It shows that clubs assist student learning, enable the acquisition of practical skills and improve college attendance, employment opportunities and career attainment. We argue that entrepreneurship clubs have improved student learning outcomes in entrepreneurship and simulated entrepreneurial learning, while impacting student self-efficacy and intentionality as well as improving employability and social learning. The final part of the chapter provides advice and tips for educators advising student-run entrepreneurship clubs. Ultimately, the chapter explains how student clubs have developed, why they are important for student learning and how advisors can support them.

Mergers and acquisitions (M&As) are important strategic actions undertaken by firms to access resources and markets. However, firms face substantial challenges in M&As during deal completion. While prior literature reviews synthesize the studies on the post-merger consequences of M&As, the literature on deal completion is largely fragmented. In this paper, the authors synthesize prior literature on deal completion into the antecedents and consequences framework and map various studies across the international business and management, finance and accounting literature at the macro-, meso- and micro-levels.

The authors adopt a content analysis-based methodology to conduct the review. First, the authors identify existing literature on deal completion based on keyword searches. Next, the authors propose a framework that integrates the extant literature from a multi-theoretic perspective across four broad themes: concepts, antecedents, implications and moderators. In this study, the authors consider not only empirical but also conceptual papers to strengthen the theoretical foundations of M&A literature. Finally, after synthesizing various studies, the authors highlight a future research agenda on deal completion.

Based on the review, this study provides important avenues for future research on M&A deal completion.

This study theoretically integrates multi-disciplinary and multi-country research on acquisition completion.

The main goal of this study is to determine the role of employee commitment in the relations between the compensation system and turnover intentions of employees in the Republic of Serbia, as well as to investigate whether there is a mediating effect of employee commitment in this relation.

The primary methodology implemented in the research was data gathering, obtaining theoretical research works on the proposed relations and empirical studies based on the PLS-SEM, analysed by IBM SPSS Statistics and SmartPLS data processing software. The data for the analysis was obtained from a total sample of 764 employees, collected in the Republic of Serbia via an online questionnaire.

The results indicated a positive statistically significant relationship between the formative construct (compensation system) and reflective construct (commitment), as well as a negative statistically significant relationship between the compensation system and reflective construct (turnover intentions). Employee commitment partially mediates the relationship between the compensation system and turnover intentions of employees.

The study was conducted in Serbia and is thus rooted in the specific national context which is characterized by high power distance and high uncertainty avoidance and more collectivistic society with feminine values more expressed. Most of the previous investigations related to the mentioned constructs were performed in companies from more developed countries, including Western Europe and the United States of America, whereas there has been no such research conducted in Serbia to date. The results portrayed a mismatch between the expected relations regarding the attitudes of employees to the rewards and the proposed national context. Modern companies in Serbia need to follow a modern reward mechanism to build stronger commitment and decrease turnover intentions. Moreover, in most earlier research works, compensation was examined in terms of satisfaction with rewards, while this study was based on questions related to perceptions of employees toward HR compensation practices (“The organization offers me”-type questions), not related to their satisfaction. Further, in the majority of previous research works, the compensation system was examined as a variable in combination with other HR processes (staffing, training and development, career development, employee relations, HR planning, communication, etc.), as a HPWP, while in this case the authors used only the practice of compensation (reward elements and employee performance evaluation) to investigate relations with commitment and turnover intentions.

Accounting information systems are mainly rule-based, and data are usually available and well-structured. However, many accounting systems are yet to catch up with current technological developments. Thus, artificial intelligence (AI) in financial accounting is often applied only in pilot projects. Using AI-based forecasts in accounting enables proactive management and detailed analysis. However, thus far, there is little knowledge about which prediction models have already been evaluated for accounting problems. Given this lack of research, our study aims to summarize existing findings on how AI is used for forecasting purposes in financial accounting. Therefore, the authors aim to provide a comprehensive overview and agenda for future researchers to gain more generalizable knowledge.

The authors identify existing research on AI-based forecasting in financial accounting by conducting a systematic literature review. For this purpose, the authors used Scopus and Web of Science as scientific databases. The data collection resulted in a final sample size of 47 studies. These studies were analyzed regarding their forecasting purpose, sample size, period and applied machine learning algorithms.

The authors identified three application areas and presented details regarding the accuracy and AI methods used. Our findings show that sociotechnical and generalizable knowledge is still missing. Therefore, the authors also develop an open research agenda that future researchers can address to enable the more frequent and efficient use of AI-based forecasts in financial accounting.

Owing to the rapid development of AI algorithms, our results can only provide an overview of the current state of research. Therefore, it is likely that new AI algorithms will be applied, which have not yet been covered in existing research. However, interested researchers can use our findings and future research agenda to develop this field further.

Given the high relevance of AI in financial accounting, our results have several implications and potential benefits for practitioners. First, the authors provide an overview of AI algorithms used in different accounting use cases. Based on this overview, companies can evaluate the AI algorithms that are most suitable for their practical needs. Second, practitioners can use our results as a benchmark of what prediction accuracy is achievable and should strive for. Finally, our study identified several blind spots in the research, such as ensuring employee acceptance of machine learning algorithms in companies. However, companies should consider this to implement AI in financial accounting successfully.

To the best of our knowledge, no study has yet been conducted that provided a comprehensive overview of AI-based forecasting in financial accounting. Given the high potential of AI in accounting, the authors aimed to bridge this research gap. Moreover, our cross-application view provides general insights into the superiority of specific algorithms.

The purpose of this study is to fill the research gap by investigating the impact of brand social initiatives on both the warmth and competence dimensions of the brand, as well as examining the brand's potential to induce pro-environmental behavioral change among consumers.

The study employed a scenario-based survey design and surveyed 415 consumers in India. The data were analyzed using structural equation modeling (SEM) with SmartPLS 4.0.

The results show that social initiatives enhance both dimensions of social perception and increase cooperative intentions. Brand competence is more crucial than warmth in predicting behavioral intentions. Brand-cause fit, CSR commitment and initiatives' impact all influence the brand image. A brand's commitment to the cause is more significant than brand-cause fit in determining how consumers evaluate a brand.

This research provides insights into how social initiatives can enhance brand image and encourage helping intentions through effective communication traits. It can aid brands in building consumer trust, benefiting society and boosting finances. The study offers a framework for effective cause communication strategies, highlighting the importance of commitment to the cause over fitting with the brand's image to elicit desired responses.

This study is a novel attempt that uses the stereotype content model and costly signaling theory to explore how social initiatives influence a brand's warmth and competence and impact consumer behavior. It provides critical insights into the psychological mechanisms behind social initiatives' impact on consumer behavioral intentions.

The case is written based on publicly available data from primary sources such as the company’s annual reports, company website and the company’s presentations, as well as from secondary sources comprising newspaper articles, research papers, research magazines, magazine articles, industry reports, research reports, etc. as indicated in the references. The company’s financials and peer data are sourced from the Thomson Reuters Eikon database.

The case examines the financial position of Macy’s, Inc., America’s largest and one of the oldest premier departmental stores, with a consolidated annual turnover of US$18,097m in the fiscal year 2020/2021 (FY, 2021). Over the previous few years, the company had been struggling with decreasing market share and profitability mainly due to increasing competition from online retailers and deep discounters, which was affecting the company’s share price. With the appointment of a new chief executive officer (CEO) in fiscal year (FY) 2017, Macy’s, Inc. undertook several changes to revive its financial health and improve its market share. However, it still registered heavy losses of US$3,944m in the FY 2020/2021, the company’s first time in the past decade. With many retailers filing for bankruptcy, was there more that Macy’s could do to improve the company’s position and regain lost investor confidence? Will its entry into emerging markets play a crucial role in its turnaround?

The case can be used in undergraduate and postgraduate courses such as accounting for managers, financial statement analysis, management accounting, introduction to accounting and advanced financial statement analysis. The case can also be effectively used to understand the primary fundamental analysis of the company that involves understanding the company’s positioning and strengths, weaknesses, opportunities and threats analysis. The case would also help business management and entrepreneurship students to get a preliminary idea about the change management process. Finally, the case can be used to familiarize students with using Microsoft Excel to build financial analysis worksheets.

Teaching notes are available for educators only.

This study investigates how asset specificity, relational governance and firm adaptability relate with supply chain integration (SCI), considering selected food processing firms (FPFs) in Uganda.

This study applies a quantitative research methodology. This research draws on a sample of 103 FPFs that have been selected from a population of 345 FPFs located in Kampala district. Hypothesis testing was done using Smart PLS version 3.

Asset specificity has a significant positive relationship with SCI, and firm adaptability partially mediates this relationship. Also, there is a full mediation impact of firm adaptability on the relationship between relational governance and SCI.

This study focused on perceptual measures to get responses from managers on the level of integration with key suppliers and customers, yet firms deal with a number of suppliers and customers.

This study contributes to existing literature on SCI by applying the transaction cost theory. The study focuses on the influence of asset specificity, relational governance and firm adaptability on SCI in the food processing sector. Literature on relational governance in supply chain using the transaction cost theory remains scanty. Few studies have also focused on firm adaptability as a mediator in the FPS with specific focus on Uganda, yet the sector is highly faced with uncertain events. The uncertain events in the sector and in developing countries call for adaptive strategies. Additionally, this study is the first to use firm adaptability to mediate the influence of asset specificity and relational governance on SCI more so in a developing country like Uganda where the FPS is one of the most important in the economy.

The present study was designed to explore the major challenges being faced by the Indian nurses' pre-post pandemic period affecting their well-being (WB) and identify factors that motivated them to perform their service wholeheartedly during the pandemic. The study also tries to bridge the gap in the study area by providing various ways that can help maintain the WB of health care professionals.

A descriptive exploratory qualitative design involving semi-structured interviews was conducted during December–January 2021 with 30 nurses from hospitals in Punjab Qualitative and thematic data analysis technique were adopted. In addition, a literature review was also conducted to study the various factors that affect the WB of health care professionals.

There are various themes and subthemes that were identified by the health care professionals, such as (1) psychological WB, (2) social WB and (3) workplace WB and (4) key motivators. This research work has identified various managerial implications that can play a huge rolein strengthening the healthcare sector of the entire world economy, paving the way toward the better WB of healthcare professionals (HCPs).

Firstly, it is probably the only study that is performed on nursing staff to evaluate their personal experiences during crucial times. It has successfully compared the factors affecting WB pre- and post-pandemic, leading to the emergence of many new factors that have originated due to the pandemic and are the cause of the poor WB of HCPs (Figures 2, 4). Secondly, it is the only study that targeted only those nurses who have provided their services in both scenarios. Finally, the study has been a pioneer in identifying the importance of maintaining the WB of HCPs at hospitals.

With big data (BD), traditional supply chain is shifting to digital supply chain. This study aims to address the issues and challenges in the way toward the implementation of big data analytics (BDA) in sustainable supply chain management (SSCM).

The factors that affect the implementation of BDA in SSCM are identified through a widespread literature review. The PESTEL framework is used for this purpose as it covers all the political, economic, social, technological, environmental and legal factors. These factors are then finalized by means of experts' opinion and analyzed using structural equation modeling (SEM).

A total of 10 factors are finalized with 31 sub-factors, of which sustainable performance, competitive advantage, stakeholders' involvement and capabilities, lean and green practices and improvement in environmental performance are found to be the critical factors for the implementation of BDA in SSCM.

This research has taken up the case of Indian manufacturing industry. It can be diversified to other geographical areas and industry sectors. Further, the quantitative analysis may be undertaken with structured or semi-structured interviews for validation of the proposed model.

This research provides an insight to managers regarding the implementation of BDA in SSCM by identifying and examining the influencing factors. The results may be useful for managers for the implementation of BDA and budget allocation for BDA project.

The result includes green practices and environmental performance as critical factors for the implementation of BDA in SSCM. Thus the research establishes a positive relationship between BDA and sustainable manufacturing that ultimately benefits the environment and society.

This research addresses the challenges in the implementation of BDA in SSCM in Indian manufacturing sector, where such application is at its nascent stage. The use of PESTEL framework for identifying and categorizing the factors makes the study more worthwhile, as it covers full spectrum of the various factors that affect the strategic business decisions.

The advancements in internet technologies and the use of sophisticated digital devices in supply chain operations incessantly generate enormous amounts of data, which is termed as big data (BD). The BD technologies have brought about a paradigm shift in the supply chain decision-making towards profitability and sustainability. The aim of this work is to address the issue of implementation of the big data analytics (BDA) in sustainable supply chain management (SSCM) by identifying the relevant factors and developing a structural model for this purpose.

Through a comprehensive literature review and experts’ opinion, the crucial factors are found using the PESTEL framework, which covers political, economic, social, technological, environmental and legal factors. The structural model is developed based on the results of the total interpretive structural modelling (TISM) procedure and MICMAC analysis.

The policy support regarding IT, culture of data-based decision-making, inappropriate selection of BDA technologies and the laws related to data security and privacy are found to affect most of the other factors. Also, the company’s vision towards environmental performance and willingness for material and energy optimization are found to be crucial for the environmental and social sustainability of the supply chain.

The study is focused on the manufacturing supply chain in emerging economies. It may be extended to other industry sectors and geographical areas. Also, additional factors may be included to make the model more robust.

The proposed model imparts an understanding of the relative importance and interrelationship of factors. This may be useful to managers to assess their strengths and weaknesses and ascertain their priorities in the context of their organization for developing a suitable investment plan.

The study establishes the importance of BDA for conservation and management of energy and material. This is crucial to develop strategies for enhancing eco-efficiency of the supply chain, which in turn enhances the economic returns for the society.

This study addresses the implementation of BDA in SSCM in the context of emerging economies. It uses the PESTEL framework for identifying the factors, which is a comprehensive framework for strategic planning and decision-making. This study makes use of the TISM methodology for model development and deliberates on the social and environmental implications too, apart from theoretical and managerial implications.