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Organizational scholars studying compensation often place an emphasis on certain employee groups (e.g., executives). Missing from this discussion is research on the compensation systems for low-wage jobs. In this review, the authors argue that workers in low-wage jobs represent a unique employment group in their understanding of rent allocation in organizations. The authors address the design of compensation strategies in organizations that lead to different outcomes for workers in low-wage jobs versus other workers. Drawing on and integrating human resource management (HRM), inequality, and worker literatures with compensation literature, the authors describe and explain compensation systems for low-wage work. The authors start by examining workers in low-wage work to identify aspects of these workers’ jobs and lives that can influence their health, performance, and other organizationally relevant outcomes. Next, the authors explore the compensation systems common for this type of work, building on the compensation literature, by identifying the low-wage work compensation designs, proposing the likely explanations for why organizations craft these designs, and describing the worker and organizational outcomes of these designs. The authors conclude with suggestions for future research in this growing field and explore how organizations may benefit by rethinking their approach to compensation for low-wage work. In sum, the authors hope that this review will be a foundational work for those interested in investigating organizational compensation issues at the intersection of inequality and worker and organizational outcomes.

This chapter uses data from the Scopus database to present a comprehensive bibliometric analysis of fintech research, focusing on publication trends, citation patterns, and thematic clusters within the field. The analysis reveals notable trends, including influential publications, prolific authors, and their affiliations. It identifies and explores publications categorized into 23 distinct themes, representing key areas of inquiry in fintech, such as technological advancements, financial inclusion, innovation, data analytics, sustainability, and regulatory compliance. The study also identifies research gaps, indicating areas within fintech that have received limited scholarly attention. These findings provide valuable insights for researchers, policymakers, and industry practitioners. Researchers can better understand the fintech landscape, identify research gaps, and guide future inquiries. Policymakers can develop effective regulations and policies based on identified trends and challenges. Industry practitioners can leverage fintech developments to enhance their strategies and operations.

Past research is mixed on family small and medium-sized enterprises’ (SMEs) use of external advisors and the limited empirical evidence is confined to developed markets. Drawing on the knowledge-based view of the firm, this research focuses on the “familiness” characteristic of SMEs and their use of external accountants as advisors in an emerging marketplace. Using internal resources for basic tasks is proposed to strengthen this relationship from a managerial cognition lens. Focusing also on SME internalization, this research probes the performance ramifications of using external accountants as advisors.

Hierarchical regression is used to test the hypotheses. The mediation hypothesis is tested by bootstrapping the indirect effect. The interaction hypothesis is visualized with simple slope analysis.

The results indicate that the familiness of SMEs is positively associated with the use of external advisors, and thereby, with high performance. SMEs with higher international exposure also use these external advisors to a greater degree. Family SMEs that have a focused use of internal resources for basic tasks benefit more from the use of external accountants for advising tasks.

This research sheds light on how family involvement in management influences firm performance, showing the moderating role of the use of internal advisors for basic tasks and the mediating role of the use of external accountants for advising. We add to the knowledge-based view by describing how family SMEs can utilize internal and external knowledge resources simultaneously.

This paper examines the factors influencing the adoption of Quality 4.0 technologies by quality professionals. The study evaluates perceived usefulness, perceived ease of use, attitude towards use, and intention to use new technologies.

The research involves a literature review, identification of latent variables derived from the Technology Acceptance Model (TAM), and a survey conducted among 200 quality professionals in the high-tech sector using computer-assisted web interviews.

The study elucidates the attitudes and intentions of high-tech industry employees towards adopting Quality 4.0 technologies. The primary conclusion drawn is that the predominant factor shaping the attitude of quality professionals towards new technologies is their confidence in their ability to effectively engage with these technologies rather than solely the perceived usefulness of such technologies to themselves or their organization.

This study is subject to certain limitations. Firstly, it focuses on five variables identified in the TAM model, potentially overlooking other pertinent factors that could provide a more comprehensive understanding. Secondly, the analysis of Quality 4.0 technologies is presented in a generalized manner, possibly resulting in nuanced differences if each specific technology were examined individually.

This article fills a gap in the literature by identifying the factors influencing quality professionals' adoption of Quality 4.0 technologies and delineating the relationships between these factors.

This study aims to understand client food preferences and how pantry offerings can be optimized by those preferences.

This study develops and administers customized surveys to study three food pantries within the Second Harvest Food Bank of Northwestern North Carolina network. This study then categorizes food items by client preferences, identifies the key predictors of those preferences and obtains preference scores by fitting the data to a predictive model. The preference scores are subsequently used in an optimization model that suggests an ideal mix of food items to stock based upon client preferences and the item and weight limits imposed by the pantry.

This study found that food pantry clients prefer fresh and frozen foods over shelf-friendly options and that gender, age and religion were the primary predictors. The optimization model incorporates these preferences, yielding an optimal stocking strategy for the pantry.

This research is based on a specific food bank network, and therefore, the client preferences may not be generalizable to other food banks. However, the framework and corresponding optimization model is generalizable to other food aid supply chains.

This study provides insights for food pantry managers to make informed decisions about stocking the pantry shelves based on the client’s preferences.

An emerging topic within the humanitarian food aid community is better matching of food availability with food that is desired in a way that minimizes food waste. This is achieved by providing more choice to food pantry users. This work shows how pantries can incorporate client preferences in inventory stocking decisions.

This study contributes to the literature on food pantry operations by providing a novel decision support system for pantry managers to aid in stocking their shelves according to client preferences.

In 2021, more than half a million Indian students traveled abroad to pursue Master’s programs. Against this background, we explore why Indian students do not enroll in Master’s programs in Indian higher education (IHE) institutions.

Semi-structured, in-depth online interviews of 30 Indian international students pursuing post-graduation studies in four major host countries were conducted to elicit the required information. The interviews were recorded, transcribed and analyzed using a thematic approach. The attribution theory was used as a theoretical lens to discuss the findings.

The results indicate that the primary reasons for students not choosing Indian institutions are wide-ranging. The need to study abroad, decision influencers and why they did not consider IHE institutes were three significant categories encompassing nine themes. The study revealed that besides other factors, the rigor of admission in premier Indian universities is much higher when compared with other international universities. Also, the perceived rate of return in investing in those developed countries' universities is better when compared with those of premier Indian universities.

The insights will act as marketing intelligence for supporting the business and university administrators of Indian universities in formulating effective tactics to admit more students from this in-demand Indian subgroup.

Most of the existing research papers discuss the motivation and challenges of Indian international students for studying abroad, There is negligible research on their demotivation to join IHE institutions. The present study tries to focus on the aspects that most of them considered but did not join IHE. Also, the attribution theory is a new perspective for understanding their behavior in selecting higher education institutions.

This study proposes a machine learning framework to predict customer complaints from production line tests in an automotive company's lot-release process, enhancing Quality 4.0. It aims to design and implement the framework, compare different machine learning (ML) models and evaluate a non-sampling threshold-moving approach for adjusting prediction capabilities based on product requirements.

This study applies the Cross-Industry Standard Process for Data Mining (CRISP-DM) and four ML models to predict customer complaints from automotive production tests. It employs cost-sensitive and threshold-moving techniques to address data imbalance, with the F1-Score and Matthews correlation coefficient assessing model performance.

The framework effectively predicts customer complaint-related tests. XGBoost outperformed the other models with an F1-Score of 72.4% and a Matthews correlation coefficient of 75%. It improves the lot-release process and cost efficiency over heuristic methods.

The framework has been tested on real-world data and shows promising results in improving lot-release decisions and reducing complaints and costs. It enables companies to adjust predictive models by changing only the threshold, eliminating the need for retraining.

To the best of our knowledge, there is limited literature on using ML to predict customer complaints for the lot-release process in an automotive company. Our proposed framework integrates ML with a non-sampling approach, demonstrating its effectiveness in predicting complaints and reducing costs, fostering Quality 4.0.

This paper aims to explore the impact of fourth-party logistics (4PL) adoption on the agility, adaptability and alignment (AAA) capabilities within humanitarian supply chains (HSCs).

Semi-structured interviews with individuals from a large non-government organisation were combined with secondary data to assess the influence of 4PL adoption on AAA capabilities in HSCs.

The results indicate that HSCs exhibit some of the AAA antecedents but not all are fully realised. While 4PL positively affects the AAA capabilities of HSCs, its adoption faces challenges such as the funding environment, data security/confidentiality and alignment with humanitarian principles. The study suggests an AAA antecedent realignment, positioning alignment as a precursor to agility and adaptability. It also identifies three core antecedents in HSCs: flexibility, speed and environmental uncertainty.

The study shows the positive impact 4PL adoption can have on the AAA capabilities of HSCs. The findings have practical relevance for those wishing to optimise HSC performance through 4PL adoption, by identifying the inhibiting factors to its adoption as well as strategies to address them.

This research empirically explores 4PL’s impact on AAA capabilities in HSCs, highlighting the facilitating and hindering factors of 4PL adoption in this environment as well as endorsing a realignment of AAA antecedents. It also contributes to the growing research on SC operations in volatile settings.