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This study investigates the optimum size for manufacturing firms and the impact of subcontracting on firms' likelihood of achieving their optimal scale in Vietnam.

Using data from the enterprise census in 2017 and 2021, the paper first estimates the production function to identify the optimum firm size for manufacturing firms and then, applies the logit model to investigate factors associated with the optimal firm size.

The study reveals that medium-sized firms exhibit the highest level of productivity. Nevertheless, a consistent trend emerges, indicating that nearly 90% of manufacturing firms in Vietnam operated below their optimal scale in both 2017 and 2021. An analysis of the impact of subcontracting on firms' likelihood to achieve their optimal scale emphasizes its crucial role, especially for foreign firms, exerting an influence nearly five times greater than that of the judiciary system.

The paper's findings offer crucial policy implications, suggesting that initiatives aimed at enhancing the overall productivity of the manufacturing sector should prioritise facilitating contract arrangements to encourage firms to reach their optimal size. These insights are also valuable for other countries with comparable firm size distributions.

This paper provides the first empirical evidence on the relationship between firm size and productivity as well as the role of subcontracting in firms' ability to reach their optimal scale in a country with a right-skewed distribution of firm sizes.

In keeping with recent body image social trends, consumer demand for the adoption of plus-size models is increasing, although the use of thin models remains prevalent. The current study explores how consumers process information about fashion products displayed on different sizes of models in advertisements, focusing on model and consumer body sizes and both genders. As an underlying mechanism explaining how the relationship between model and consumer body sizes shapes consumer purchase intention, this study explores the role of guilt, shame and mental imagery.

The current study uses a text analytics technique to identify female consumers' general opinions of thin models in advertising. Employing a 3 (consumer body size: normal, overweight, obese) × 2 (model body size: thin, plus-size) × 2 (gender: male, female) between-subjects online experiment (n = 718), the main study comparatively analyzes the influences of plus-size and thin models on consumer responses.

The results reveal that, despite body positivity movements, thin models still generate negative emotions among female consumers. For obese female consumers, advertisements featuring plus-size models produce fewer negative emotions but not more mental imagery than advertisements featuring thin models. Conversely, for obese male consumers, advertisements featuring plus-size models generate more mental imagery but not more negative emotions than advertisements featuring thin models. The results also reveal that the relationship between consumer body size and guilt is moderated by perceived model size, which is also moderated by gender in generating mental imagery. While guilt plays a mediating role in enhancing mental imagery, resulting in purchase intention, shame does not take on this role.

This study is the first to present an integrated model that elucidates how consumers with varying body sizes respond to different sizes of models in advertising and how these responses impact purchase intentions.

Our findings only apply to contexts where consumers purchase fashion clothing in response to advertisements featuring thin versus plus-size models.

Exposing normal-size consumers to plus-size models generates less mental imagery, and thus, practitioners should seek to match the body sizes of the models featured in advertising to the body sizes of their target audience or ad campaigns that include both plus-size and thin models may help improve message persuasiveness in fashion advertising. Moreover, guilt-appeal advertising campaigns using thin models would appeal more to thin consumers of both genders than shame-appeal advertising.

How does the family influence individual entrepreneurial entry? The literature does not provide a satisfactory answer. In this paper, we develop an individual entrepreneurial capital perspective to systematically evaluate the impact of current households on entrepreneurship at both theoretical and empirical levels.

Based on the Global Entrepreneurship Monitor (GEM) database from 2010 to 2018, we used logit regression models to examine the relationship between household size and opportunity- and necessity-motivated entrepreneurship.

The empirical results show that with the expansion of household size, the possibility of opportunity-motivated entrepreneurship shows a trend that first declines and then rises, and the possibility of necessity-motivated entrepreneurship shows an upward trend, suggesting that there are two types of opposing mechanisms that affect the relationship between household and entrepreneurial entry.

Family households are the starting point of individual growth and provide initial cognitive and social resources for decision-making. For entrepreneurs, it is necessary to make full use of household-based capital and networks to promote entrepreneurial activities. For the government, it is necessary to explore the development and implementation of household-based entrepreneurial policies.

Through analyses of the U-shaped relationship between household size and opportunity-motivated entrepreneurship and the positive relationship with necessity-motivated entrepreneurship, we reveal the relationship between household and entrepreneurship, reconcile the contradictions in the literature and contribute to the two fields of family studies and entrepreneurship.

This study examines the impact of university science parks’ (USPs) capabilities on revenue generation and introduces regional innovation as a moderating variable. This study aims to provide insights into enhancing revenue generation and fully leveraging the role of USPs in promoting revenue generation.

This study employs system generalized method of moments (GMM) estimation for 116 universities in China from 2008 to 2020, using hierarchical regression analysis to examine the relationships between variables.

The findings suggest that USPs play a beneficial role in fostering revenue generation. Specifically, the provision of incubation funding demonstrates a positive correlation, while USPs size exhibits an inverted U-shaped pattern, with a threshold at 3.037 and a mean value of 3.712, highlighting the prevalent issue of suboptimal personnel allocation in the majority of USPs. Moreover, the analysis underscores the critical moderating influence of regional innovation, affecting the intricate interplay between USPs size, incubation funding and revenue generation.

The single country (China) analysis relied solely on the use of secondary data. Future studies could expand the scope to include other countries and employ primary data collection. For instance, future research can further examine how regional development and USPs strategic plan impact revenue generation.

The study recommends that USPs managers and policymakers recognize the importance of incubation funding and determine the optimal quantity of USPs size to effectively foster revenue generation in USPs. Policymakers can use regional innovation as a moderating variable to reinforce the relationship between USPs size and incubation funding on revenue generation.

The study’s findings can contribute to the strategic industry growth and economic development of nations by promoting revenue generation. Leveraging the role of USPs and implementing the study’s recommendations can strengthen innovation and technology capabilities, driving strategic industry growth and economic development. This can enhance global competitiveness and promote sustainable economic growth.

This study introduces regional innovation as a moderating variable and provides empirical evidence of its influence on the relationship between USPs size and incubation funding on revenue generation. This adds value to research to the existing literature on USPs and revenue generation by showcasing the importance of examining the regional impact in research and innovation.

As the application of crowdsourcing contests grows, leveraging the participation of superstars (i.e. solvers who have outstanding performance records in a crowdsourcing platform) becomes an emergent approach for managers to solve crowdsourced problems. Although much is known about superstars’ performance implications, it remains unclear whether and how their participation affects the size of a contest crowd for a crowdsourcing contest. Based on social contagion theory, this paper aims to examine the impact of superstars’ participation on the crowd size and studies how this impact varies across solvers with different heterogeneity in terms of skills, exposure and cultural proximity with superstars in crowdsourcing contests.

This paper uses secondary data from one crowdsourcing platform that includes 6,587 innovation contests to examine superstars’ main and contextual effects on the crowd size of a contest.

Our results reveal that superstars’ participation positively affects the crowd size of a contest in general. This finding suggests that social contagion is a fundamental mechanism underlying crowd formation in crowdsourcing contests. Our results also indicate that in contests that involve multiple superstars, superstars’ effect on crowd size becomes negative when we simultaneously consider other solvers’ heterogeneity in terms of skills, exposure and cultural background, and this negative effect will be intensified by increases in the skill gap, extent of exposure and cultural proximity between superstars and other solvers in the same contest.

Our research enhances the understanding of the influence of superstars and the mechanism underlying the emergence of contest crowds in crowdsourcing contests and contributes knowledge to better understand social contagion in a competitive setting. The results are meaningful for sourcing managers and platform supervisors to design contests and supervise crowd size in crowdsourcing contests.

Currently, the state of practice in experimental design centres on orthogonal designs (Alpizar et al., 2003), which are suitable when applied to surveys with a large sample size. In a stated choice experiment involving interdependent freight stakeholders in Sydney (see Hensher & Puckett, 2007; Puckett et al., 2007; Puckett & Hensher, 2008), one significant empirical constraint was difficult in recruiting unique decision-making groups to participate. The expected relatively small sample size led us to seek an alternative experimental design. That is, we decided to construct an optimal design that utilised extant information regarding the preferences and experiences of respondents, to achieve statistically significant parameter estimates under a relatively low sample size (see Bliemer & Rose, 2006).

The D-efficient experimental design developed for the study is unique, in that it centred on the choices of interdependent respondents. Hence, the generation of the design had to account for the preferences of two distinct classes of decision makers: buyers and sellers of road freight transport. This paper discusses the process by which these (non-coincident) preferences were used to seed the generation of the experimental design, and then examines the relative power of the design through an extensive bootstrap analysis of increasingly restricted sample sizes for both decision-making classes in the sample. We demonstrate the strong potential for efficient designs to achieve empirical goals under sampling constraints, whilst identifying limitations to their power as sample size decreases.

This paper aims to investigate the relationship between the impact of COVID-19 on the performance of financial managers of medium and large companies.

This research used the data of 173 listed large and over-the-counter as medium-size companies from 2018 to 2021. The results of these tests have been analyzed using panel data and STATA 15 software.

The results showed that COVID-19 has no significant relationship with the return on equity in large and medium-size companies. This variable does not significantly affect Tobin’s Q index in medium-size companies either. Other financial indicators examined in this research have decreased considerably in all companies under the influence of COVID-19. Still, the intensity of this effect is different in large and medium-size companies. Funds from borrowings and Tobin’s Q ratios in medium-size companies compared with large companies have been more severely affected by the COVID-19 disease; the return on assets, book value to market value and large companies compared with medium-size companies have been more severely and significantly affected by COVID-19; and financing funds through the issuance of shares in large companies and medium-size companies have been affected by COVID-19 almost equally.

Despite the studies related to financial crises and their effect on the performance of companies, no research has examined the financial performance indicators during the outbreak of COVID-19 in large and small companies. Therefore, the results of this research can affect different groups: financial managers and the board of directors of companies to better understand the impact of the corona disease on the company’s performance; investors benefit from research results in line with investment decisions; developing theory and educational topics for the benefit of students and studying and conducting more experimental research in this regard; and the stock exchange organization and regulatory and support institutions need to find out the depth of the disaster and the effect of COVID-19 on the performance of companies.

Over the past 50 years, air travel in the United States has increased from approximately 33 million passengers in 1960 to over 607 million passengers in 2007 (National Transportation Statistics, 2011, Table 1–40). This is over an 18-fold increase in air travel in the past five decades. Over that same time period, the number of airports increased modestly, from 15,161 in 1980 to 19,750 in 2009. The number of those airports serving public commercial traffic is even smaller, and has declined from 730 airports in 1980 to 559 in 2009 (National Transportation Statistics, 2011, Table 1–3). Together, these two facts point to phenomenal growth among airports (measured by the number of passenger trips).

Despite the relevance of firm size in the analysis of corporate social responsibility (CSR) engagement, there is still much to know about the specific impact of firm size on CSR formalisation. Moreover, in order to better understand such a relation, the interaction effects of development strategies on which companies may base its growth, namely diversification and internationalisation, will be also taken into account. Specifically, this work contributes to shed light on these issues by combining theories related to external and internal drivers of CSR. Using a sample of Spanish listed firms, the results show that firm size affects positively CSR formalisation, and that this effect is stronger in the case of adopting a diversification strategy, while no evidence was found for the moderating effect of internationalisation strategy.