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The purpose of this paper, a critical reflection, is twofold. First, by comprehensively reviewing scale development procedures in hospitality research, a concerning lack of nomological validity testing is demonstrated. Second, the need for nomological validity testing is discussed and both conceptually and empirically reasoned.

This research systematically reviews scale development studies in three leading hospitality journals, including Cornell Hospitality Quarterly, International Journal of Contemporary Hospitality Management and International Journal of Hospitality Management over ten years (2012–2021) to analyze the completeness of scale development procedures. Specifically, the authors evaluate whether the reviewed studies engage in testing the nomological and predictive validity of the newly developed measures.

The results indicate a concerning gap in the current practices in hospitality research. Specifically, only 33.3% of the examined studies assess nomological validity. These findings collectively underscore the need for improving the comprehensiveness of scale development processes in hospitality research.

The study offers important implications for hospitality researchers. The paper provides an extensive discussion on the importance and benefits of testing for nomological validity in scale development studies, contributing to the completeness and consistency of scale development procedures in the hospitality discipline.

This research critically assesses prevalent, and widely accepted, scale development procedures in hospitality research. This research empirically demonstrates the neglect of nomological validity issues in scale development practices in hospitality research. Scale development is an essential scientific practice used to create a research instrument in a field of study, improving our understanding of a specific phenomenon and contributing to knowledge creation. Considering the significance of scale development in advancing the field of hospitality research, the validation procedures involved in the scale development processes are of utmost importance and should be thoroughly applied.

This study aims to develop and validate a multidimensional scale to measure the motivating factors that lead to opportunity recognition in social entrepreneurship among higher education institute (HEI) students.

The scale was developed through two phases; in phase 1, semi-structured interviews with social entrepreneurs and aspiring students were conducted to explore themes for item generation. Phase 2 included developing and validating the scale using exploratory (EFA) and confirmatory factor analysis (CFA). The sample included HEI students (n = 300 for EFA, n = 300 for CFA) with either academic background or volunteering experiences in social entrepreneurship.

A 24-item scale is developed in the study, with six factors measuring the motivating factors influencing opportunity recognition in social entrepreneurship: life experiences, social awareness, social inclination, community development, institutional voids and natural option for a meaningful career.

The scale facilitates the development of theories and models in social entrepreneurship. The scale also enables policymakers and social entrepreneurship educators to understand the motivating factors that lead to opportunity recognition among students. It would help them to provide target-specific support to students.

To the best of the authors’ knowledge, this study is the first attempt to develop a scale that measures opportunity recognition in social entrepreneurship based on specific motivating factors. The study used the model by Yitshaki and Kropp (2016) as the conceptual framework. This study is the first attempt to triangulate the model’s findings using a quantitative methodology and through the development of a measurement scale. Besides, the scale adds value to social entrepreneurship research, which lacks empirical research on HEI students.

This study investigates the impact of large-scale teaching in higher education on students’ preparedness for the workforce within the context of evolving labour market demands, the expansion of higher education and the application of high-impact teaching strategies. It synthesizes perspectives on employer work readiness, the challenges and opportunities of large-scale teaching and strategies for fostering a dynamic academia-industry feedback loop. This multifaceted approach ensures the relevance of curricula and graduates’ preparedness while addressing the skills gap through practical recommendations for aligning teaching methodologies with employer expectations.

The research methodically examines the multifaceted challenges and opportunities inherent in large-scale teaching. It focuses on sustaining student engagement, maintaining educational quality, personalizing learning experiences and cultivating essential soft skills in extensive student cohorts.

This study highlights the critical role of transversal skills in work readiness. It also uncovers that despite its challenges, large-scale teaching presents unique opportunities. The diversity of large student groups mirrors modern workplace complexities, and technological tools aid in personalizing learning experiences. Approaches like peer networking, innovative teaching methods, real-world simulations and collaborative resource utilization enrich education. The importance of experiential learning for augmenting large-scale teaching in honing soft skills is emphasized.

This manuscript contributes to the discourse on large-scale teaching, aligning it with employer expectations and the dynamic requirements of the job market. It offers a nuanced perspective on the challenges and opportunities this educational approach presents, providing insights for crafting engaging and effective learning experiences in large cohorts. The study uniquely integrates experiential learning, co-creation in education and industry-academia feedback loops, underscoring their importance in enhancing student work readiness in large-scale teaching.

This chapter describes a research study in which data about the uses of Facebook by higher education students were gathered simultaneously with measurements of a set of psychological dimensions (personal and collective self-esteem, self-concept, general self-efficacy, satisfaction with social support and with academic life, and several aspects of academic experiences: interpersonal, career, institutional, personal and course satisfaction). The final result of the study is a path model inspired on the structural model proposed by Mazman and Usluel (2010) in which the psychological variables that have a significant influence on the academic use of Facebook were incorporated. A positive total effect of identity collective self-esteem in the educational use of Facebook was found and a negative total effect was found for public collective self-esteem in the educational usage of Facebook (EUF). Institutional adaptation proved to have a significant positive total effect on students’ willingness to use Facebook for educational purposes. Satisfaction with life was not a direct predictor of the educational use of Facebook. However, it was a direct predictor of the use of Facebook for work-related purposes, which was the strongest predictor of educational use of Facebook. Therefore, although the effect of satisfaction with life in the educational use of Facebook was only indirect, it was nevertheless positive and statistically significant.

Scalar terms, such as “local” and “global,” “big” and “small” are fundamental in how academics and practitioners make sense of and respond to grand challenges. Yet, scale is so taken-for-granted that we rarely question or critically reflect on the concept and how it is used. The aim of this paper is to identify scale as an important concept in research on grand challenges and to point out why taking scale for granted can be problematic. In particular, I suggest that to date most research on grand challenges sees scale as a fundamental ontological feature of the world. Yet, scalar categories and hierarchies are not as self-evident and given as they may seem. Moreover, taking scale as an ontological fixed category limits our ability to make sense of, theorize and respond to grand challenges. As an alternative, I suggest seeing scale as an epistemological frame that participants employ in their everyday practices to make sense of, navigate and develop solutions to grand challenges. The chapter concludes with a research agenda for studying scale as socially constructed in practice.

Purpose: This chapter aims to enlighten the stakeholders on the role and contribution and the issues and challenges of large-scale industries in the wake of the globally unified economies.

Need for the study: Large-scale industries are one of the pillars of any nation and can exercise an immense impact on the numerous facets of the economy of any country. Their role and contribution can benefit all the stakeholders, especially in today’s integrated and interdependent world economies. Hence, there is an absolute need to highlight the issues and challenges and suggest measures to overcome them to promote a resilient global economy.

Methodology: The study gathered data from secondary sources like textbooks, articles, and the internet.

Findings: The findings of the study state that large-scale industries are enormous contributors to employment creation, development of the economy, growth of revenue, research and development (R&D) and innovation, export promotion, and infrastructure. The significant challenges include regulatory compliance, workforce management, economic volatility, political instability, supply chain management, environmental compliance, and technology and infrastructure.

Protectionism, deregulation, public–private partnership, privatisation, and environmental regulation are significant government decisions that affect large-scale industries. The study identifies tax incentives, easy access to financing, and domestic and international trade policies to safeguard large-scale industries’ interests.

Practical implications: Large-scale industries contribute towards the growth of global economic resilience in terms of employment generation, technological advancements, and innovation, fostering international trade in today’s interconnected world.

Large-scale farm management in China has developed rapidly in recent years. Large-scale farmers face substantial operating risks, requiring extensive price risk management. However, the agricultural insurance and futures markets in China are incomplete. This study aims to analyze the price-risk-management behaviors of large-scale farmers under incomplete market conditions, with a focus on the interconnections between large scale farmers' subjective preferences (risk preferences, time preferences), liquidity constraints and their price risk management.

The authors construct an analysis framework to reveal the impact of large-scale farmers' risk preferences, time preferences and liquidity conditions on their price-risk-management behaviors under incomplete market conditions. Using data from field surveys and subjective preference experiments involving 409 large-scale grain farmers in China, an empirical analysis was conducted using the bivariate probit model.

The results show that risk-averse farmers will use risk transfer (such as contract farming) and risk diversification (such as multi-period sales) to avoid price risk. However, farmers subject to liquidity constraints and strong time preferences will not choose risk diversification, and the interaction between time preferences and liquidity constraints will strengthen this decision. The larger the farm-management scale, the greater the impact.

The authors focus on rapidly developed large-scale farm management in China. Appropriate price risk management is required by large-scale farmers due to their substantial operating risks. Considering the incomplete conditions of agricultural insurance and futures markets, the results of this study will help identify behavioral characteristics of large-scale farmers and optimize their price-risk-management strategies, further stabilizing large-scale farm management.

Purpose: This literature review aims to answer the calls for further exploration of scaling challenges and opportunities for social entrepreneurs (SEs). We address the scaling issue of social entrepreneurship through the theoretical lens of sustainable business models. Methodology: This paper investigates, on a multilevel approach, 340 journal articles published in one of the 20 peer-reviewed journals in management, entrepreneurship, CSR, organizational behavior, and nonprofit. It also considers influential articles due to their relatively high citation count (i.e., more than 150 times) outside of those selected journals. This paper furthermore analyses in-depth 32 scaling articles. Findings: This study positions the topics of social entrepreneurship over the last decades, together with the six types of scaling strategies: scaling up, scaling down, scaling across, scaling deep, scaling out, and diversification. It also discusses 15 challenges related to the scaling efforts by SEs. It furthermore elaborates on potential leads for research and practice regarding scaling social impact. Social Implications: There are many pathways for SEs to increase their impact on society, even though it remains quite challenging to achieve for most. Understanding what possibilities or limitations apply to individual SEs is but a first step in developing the full potential of social entrepreneurship. Originality: This paper approaches scaling from three complementary levels of analysis, i.e., individual, organizational, and institutional. Thus we provide more clarity and a nuanced perspective on past and future research regarding scaling challenges and opportunities.

Digital business models are often designed for rapid growth, and some relatively young companies have indeed achieved global scale. However, despite the visibility and importance of this phenomenon, analysis of scale and scalability remains underdeveloped in management literature. When it is addressed, analysis of this phenomenon is often over-influenced by arguments about economies of scale in production and distribution. To redress this omission, this paper draws on economic, organization, and technology management literature to provide a detailed examination of the sources of scaling in digital businesses. We propose three mechanisms by which digital business models attempt to gain scale: engaging both non-paying users and paying customers; organizing customer engagement to allow self-customization; and orchestrating networked value chains, such as platforms or multi-sided business models. Scaling conditions are discussed, and propositions developed and illustrated with examples of big data entrepreneurial firms.

This paper examines the impact on German personal income distribution of income-dependent (variable) equivalence scales. The use of variable equivalence scales causes distinctive increases in income inequality compared with income-independent, constant equivalence scales. The narrowing of income limits between the upper and lower income regions also leads to an increase in income inequality.