European Research on Management and Business Economics最新文献

Push and pull driving factors are important motivational antecedents of entrepreneurial intention. Perceptual variables, such as perceived risk in venture creation and opportunity recognition, also play a significant role in this question. However, the existing research has not analyzed all these factors in conjunction, which would enable better identification of entrepreneurial intention. This study advances the understanding of the relationship between push–pull factors and entrepreneurial intention through an analysis of the mediating effects of perceived risk and opportunity recognition. The results of a structural equation model (partial least squares, PLS) applied to a sample of 616 Spanish undergraduate students reveal that the influence of pull factors on entrepreneurial intention is positive, and partially mediated by opportunity recognition. However, the influence of push factors on entrepreneurial intention is indirect and negative. Push factors have a negative impact on entrepreneurial intention, increasing individuals’ perceptions of risk in venture creation and undermining their opportunity recognition. The paper extends the current knowledge on how entrepreneurial intention is formed, integrating the Push-Pull Theory into Krueger's 1993 Model of Entrepreneurial Intention, thus incorporating motivational and perceptual variables into a unified model. The results suggest practical implications for forming entrepreneurial intention in individuals from three perspectives: entrepreneurship education, public policy and practitioners. Specifically, these implications mainly focus on the importance of designing programs and policies aimed at favoring pull-related motivations (i.e., self-realization, independence), as well as helping develop perceptions that venture creation entails low risk and that an interesting high-value added business opportunity is recognized.

Life settlements (LSs) can be considered a novel and innovative financial asset in countries where they are not yet established. This paper aims to assess the attitude (ATT) of policyholders towards participating in LSs in such countries by evaluating various variables: performance expectancy (PE), expected easiness (EE), social influence (SI), perceived ethical problems (EP), and bad feelings (BF) that may arise from this type of transaction. To achieve this goal, a questionnaire was administered to 89 individuals in Spain who possessed extensive knowledge of financial and insurance matters. The data analysis employed fuzzy set qualitative comparative analysis (fsQCA) as the basis, supplemented by partial least squares-structural equation modelling (PLS-SEM). The fsQCA results enabled the identification of policyholder profiles associated with the acceptance or rejection of LSs. Meanwhile, PLS-SEM provided insights into the net strength and statistical significance of the impact of each variable on ATT. Methodologically, this study demonstrates that fsQCA is valuable in constructing a reliable and concise framework for subsequent PLS-SEM estimation. A significant practical implication of this research is the importance of the interaction between PE and SI in the successful development of LS markets. A positive perception of financial advisors regarding these agreements emerges as a crucial factor in market growth. Moreover, the study reveals that EP and BF may significantly influence resistance towards LSs.

Logistics startups gradually rely on digital marketing strategies to acquire a competitive advantage. The main aim of Logistics startups is to increase their digital brand name and user engagement in order to acquire a competitive advantage. To the completion of this target, various digital marketing strategies could be implemented to ensure a differentiating factor. A three-stage data-driven methodology was adopted to evaluate the contribution between the parameters and to reflect strategies that can be presented to improve the website's user engagement and digital brand name. The first part of the study collects data from nine logistics startups’ websites over a period of 180 days. The second part of the study employs Fuzzy Cognitive Mapping (FCM) to develop an exploratory diagnostic model that visually depicts the cause-and-effect relationships between the metrics under consideration. In the last part of the study, a predictive simulation model has been created to present the intercorrelation between the examined metrics and to present possible optimization strategies. According to the findings of this study, Logistics startups’ websites must be developed with fewer web pages and need to be focused on the customers’ target. Additionally, in contradiction with other industries’ websites, logistics startups must maintain a steady flow of digital advertisements to optimize brand name and profit.

The shift to remote work mode significantly impacts the way people work, especially middle managers, who play a fundamental role in organizations. There is a broad consensus that they have a key responsibility in deploying strategies, and in terms of operations and organizational culture. Based on the responses of 200 managers from five Latin American countries working in several economic sectors, the effects of remote work demands on their levels of perceived work stress and general well-being were examined in this study. Through partial least squares structural equation modeling, the positive effect of remote work demands on work stress was verified. The analysis also suggested a competitive mediating effect of role transitions on the relationship between work stress and well-being. The study also provides theoretical and practical implications for human resource management and a design of flexible labor policies from a sustainability perspective.

Non-financial information and its contribution to the achievement of each Sustainable Development Goal (SDG) are assuming great relevance in the business world, in which it is not enough to be economically sustainable without also being sustainable from ethical, environmental, and social points of view. An analysis of how the financial sector contributes to the achievement of the SDGs is crucial in two ways. Firstly, due to the relevance and the magnitude of this sector itself; secondly, and more importantly in our view, because of the financial leverage of the banking sector that has a mandate to facilitate the transition of all economic sectors towards sustainability, guided by the 2030 Agenda. However, despite the expectations placed on banking entities, there is a research gap on their disclosure practices and on the SDG-related information that they report. In addition, the academic literature centered on the analysis of SDG-related disclosures through artificial intelligence is very scarce. To fill this gap, the objective of our study is, on the one hand, to analyze whether there is greater homogeneity in the disclosure of non-financial information in the Spanish banking sector following the transposition of Directive 2014/95/EU into Spanish Law. On the other hand, it is to evaluate the contribution of banking entities to the SDGs. To do so, the non-financial information reports of 12 Spanish banks are analyzed, completing a comparative evaluation and using artificial intelligence to identify mentions of each SDG and its targets. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was also used to rank the banking entities in accordance with their contribution to each SDG. The results reflected the plurality, in both breadth and quality, in the disclosure of non-financial information and in the contribution to the SDGs. The only point in common between all the entities that were studied was the use of the GRI disclosure framework and the identification of the priority SDGs, positioning SDGs 8, 13, and 4 in priority positions. The banks with higher bank capitalization levels occupied the top of the ranking of contributions to the SDGs. Differences were presented for all other aspects, even to the point of highlighting that some entities or independent verifiers had not offered all the information. In conclusion, greater efforts to improve the quality of non-financial reporting and further development of the common regulatory framework will be fundamental for better comparability between the reports from banking entities. Furthermore, this study shows that natural language processing can be applied to better measure companies' alignment with the SDGs based on the text of their non-financial reports.

Nowadays, investors seek beyond the financial performance of their investments, including Environmental, Social, and Governance (ESG) criteria in the decision-making process. To this effect, there are currently suppliers who offer different methodologies to evaluate the ESG factors that companies apply in their businesses. In 2016, Morningstar launched the Morningstar Sustainability Rating for mutual funds, which provides information on how well mutual funds incorporate sustainable assets. This rating is obtained from the Morningstar Portfolio Sustainability Score, the calculation system of which was modified and improved in 2018. However, an important drawback remains to be overcome because the score is only calculated if at least 67% of the portfolio assets have an ESG score and it does not take assets without an ESG score into account. This paper aims to provide a more reliable rating for investors because it proposes the inclusion of all the assets in the fund valuation, regardless of whether they have an ESG score, using fuzzy set theory and specifically triangular fuzzy numbers.

Today, people live in the age of new diseases, such as stress and anxiety, and these diseases can affect users’ decision making regarding different products and services. Another important variable that can affect decision making is the use of mobile applications for marketing purposes. However, despite the importance of the aforementioned factors, relevant academic research on the impact of these variables on decision making has been scarce. The present study fills this gap in the literature using the technology acceptance model and integrating stress and anxiety variables. To this end, a sample of 672 customers of Spanish restaurants is analyzed. After carrying out a descriptive analysis of the sample and the variables, the structural equation model is used. In particular, the partial least squares model is used to test the hypotheses. The paper concludes with a discussion of how the results of the present study contribute to previous research on stress and anxiety, along with technology acceptance model variables, on decision making. In particular, the results have shown that there is no direct relationship between stress and perceived usefulness, and perceived ease of use, and attitude towards using. Although the relationship between stress and anxiety, perceived usefulness and attitude towards using, perceived usefulness and stress, and other relationships raised in the hypotheses have been confirmed.

Corporate reputation has deserved attention in recent years from firms and researchers given its impact on creating a competitive advantage and on keeping a sustained superior performance. However, the impact of corporate reputation on risk, in addition to being less studied, still presents controversial results. Thus, the purpose of this study is to, simultaneously, analyze the effect of corporate reputation on stock return and risk. A model based on firms' financial market data was assessed through a panel data analysis which included 84,745 firm-year observations, which occurred between January 6^th, 2009, and December 31^th, 2019, from a full sample of 156 United States firms listed in the New York Stock Exchange (NYSE) and National Association of Securities Dealers Automated Quotations (NASDAQ), 82 of which were listed in Reputation Quotient (RQ).

The results show that there are no significant differences between listed and not listed firms in Reputation Quotient concerning firms' abnormal returns and firms' systematic risk. This can be justified because stock prices adjusted instantly to the corporate reputation, which supports the market efficiency hypothesis.

This study may provide important insights into the literature: firstly, although the impact of reputation on performance has gained attention in recent years, the firsts studies essentially analyze the reverse impact; secondly, this work aims to, simultaneously, study the effect of corporate reputation on return and risk, being the impact on the risk an area still little explored and with controversial results; thirdly, this study distinguishes itself by using a set of firms listed in a reputation ranking and a set of firms not listed, but with similar characteristics in terms of market capitalization, highlighting the impact of reputation communication through media rankings.

The purpose of this article is to analyze the evolution of the domestic market—recessive, stable, and expansive—on export volume in family firms. Although globalization has hastened family firms toward internationalization, little is known of the influence of market evolution on export strategies. We propose a theoretical model that evaluates the influence of domestic market evolution on the percentage of export sales and the mediating role of innovation in this relationship. This model views innovation as a process that may impact export propensity when influenced by the market situation.

We perform a study with panel data for a five-year period (2012–2016) from 788 family firms to identify family firms’ behavior in export volume and innovation under different market conditions.

The results reveal that family firms have significant results for export sales under recessive and expansive market situations. They are less prone, however, to export when markets are stable. Contrary to the predictions of the literature, our results indicate that innovation has little relevance to export volume in any of the market situations analyzed.