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Given the general consensus that creativity is a crucial driving force for innovation and progress, understanding how to promote it would benefit individuals, companies, society and academia. Therefore, this paper aims to analyze the independent and contingent impact of individuals’ personality traits, team-member exchange (TMX) and gender on stimulating creativity.

The study uses a survey-sample of 639 university students (51.96% women) between the ages of 17 and 50. Confirmatory factor analysis (CFA) demonstrated reliability and validity of its measures. To test hypotheses, using structural equation modeling, hierarchical regression analyses were performed.

Results show that four of the five personality traits (extraversion, agreeableness, conscientiousness and openness to experience) and TMX positively influence creativity. There are no significant differences between men’s and women’s creativity. High TMX reinforces the influence of extraversion on creativity, while low TMX harms this relationship. High extravert women are more creative than high extravert men, while low extravert men are more creative than low extravert women. Low emotionally stable women are more creative than low emotionally stable men, while high emotionally stable men are more creative than high emotionally stable women. There are differences in how women and men take advantage of their openness to experience when TMX is considered. That is, while women take advantage of openness to experience for any value of TMX, men only increase their creativity as openness to experience increases for low values of TMX.

Like all studies, ours has some limitations that provide opportunities for future research. First, care should be taken when generalizing these findings to other contexts. We use data from Spanish individuals, specifically university students. While they are suitable for testing our hypotheses, future studies should establish whether the general tendencies that we observe hold true for other kinds of people from Spain and other countries. Even more, this paper’s perspective might be biased by the authors’ country of origin (south Europe) in terms of gender. According to Hofstede (2001) south European and South American countries are more masculine than other cultures (Mensa and Grow, 2022). Therefore, analyzing these questions in different cultures (countries and settings) would facilitate the generalization of the results. Second, the data we use is mainly cross-sectional so strict causality cannot be inferred. The theory we use assumes specific causal directions, but alternative causal relationships cannot be ruled out. Finally, ideally, we should have controlled for additional variables that might influence the relationships in our model.

This paper has practical implications, as it demonstrates that neither gender is more creative than the other. It goes a step further, explaining how men and women can leverage their personality traits to be more creative. Moreover, since TMX could reinforce the impact of personality traits on creativity, this paper could help managers better organize teams and companies that want to be more innovative by taking into account the personality traits of their employees and how to get the best out of women and men.

Traditionally, women have considered creativity to be a man’s thing. The results of this work favor society, demonstrating that women are just as creative as men and that through personality traits and TMX, both men and women can be more creative. These results help to reduce the gender gap and may favor women’s place in today’s society.

This work offers academic and practical implications. The main contributions to the creativity and gender literatures are the following: (1) Women are as creative as men. (2) High extravert and low emotionally stable women are more creative than men. (3) High open to experience women with high TMX are more creative than men at any level of TMX. For practitioners, the understanding of what personality traits have higher impact on creativity depending on the levels of TMX for women and men could help companies and politicians in hiring the most suitable people, especially for those positions where creativity is needed. This would increase the quality of their human capital, allowing them to get the most out of their human resources, from the very beginning of the employment relationship.

This paper aims to investigate whether loan officers' risk taking in credit decisions are associated with their personal financial risk preference and personality traits or solely with bank-contextual and loan-relevant factors.

An online survey administered in six large Swedish banks to 163 loan officers responsible for assessing credit risk and approval of loan applications. The loan officers rated their likelihood of approving fictitious loan applications from business companies.

The loan officers' credit risk taking is associated with bank-contextual factors, directly with perceived organizational credit risk norms and indirectly with self-confidence in assessing credit risks through attitude to credit risk taking. A direct association is also found with personal financial risk preference but not with personality traits.

Increased awareness of that loan officers' personal financial risk preference is associated with their credit risk taking in loan decisions but that the banks' risk policy has a stronger association. Banks' managements and boards should therefore assure that their credit risk policy is implemented, followed and being aligned with their performance incentives.

Increased awareness of that loan officers' credit risk taking is associated with personal financial risk preference but more strongly with the banks' risk policy that motivate banks' managements and boards to assure that their credit risk policy is implemented, followed and being aligned with their performance incentives.

The first study which directly compare the associations of loan officers' risk taking in credit approvals with personal risk preference and personality traits versus bank-contextual factors and loan-relevant information.

This study investigates theoretical relationships among personality characteristics, preferences for different types of rewards and the propensity to choose a job in auditing by management-related higher education students. Specifically, the authors consider motivation, locus of control (internal and external) and self-efficacy (SE) as personality characteristics and financial, extrinsic, support and intrinsic as types of rewards.

Data were collected through a questionnaire targeted at management-related higher education students in Portugal. Partial least squares structural equation modelling was used to analyse the data.

The full sample results show that different types of motivation, locus of control and SE are related to different reward preferences. The authors also find a positive association between a preference for extrinsic rewards and the propensity to choose a job in auditing. Moreover, when the authors consider the role of working experience in the model, the authors find that the reward preferences that drive the choice of an auditing job differ according to that experience.

This study enriches the literature by assessing preferences for different types of rewards, considering multiple personality characteristics and a comprehensive set of rewards. Furthermore, the authors identify the reward preferences that drive the choice of an auditing career. This knowledge empowers auditing firms to devise recruitment strategies that resonate with candidates’ preferences, which boosts the capacity of these companies to attract new auditors.

We investigated the relationship between personality and managerial assessment center (AC) dimensions, emphasizing age’s moderating role within volatility, uncertainty, complexity, ambiguity (VUCA) simulations.

We analyzed 327 managers and applied the AC method, examining areas like social skills, problem-solving, management and goal striving, openness to change, employee development using the VUCA framework.

We assessed personality metatraits through a questionnaire based on the circumplex model (CPM; Strus, Cieciuch, & Rowinski, 2014), identifying four bipolar metatraits. Results highlighted passiveness and disharmony as negatively correlated with all managerial AC dimensions, with passiveness adversely affecting social skills and problem-solving.

Age’s moderating role emerged as pivotal in the relationship between personality and managerial AC dimensions, especially in specific VUCA contexts. This underscores age’s influence on the interplay between personality and managerial efficacy, suggesting varying predictive capabilities across age groups. The research illuminates the complexities of these relationships, spotlighting age’s nuanced impact.

The purpose of this paper is to provide an overview of the literature regarding the academic online learning experience under the lens of broad personality traits, in the transition from traditional to online learning due to global coronavirus pandemic (COVID-19).

The systematic literature review is based on preferred reporting items for systematic reviews and meta-analyses (PRISMA) method and includes indexed empirical studies in academic institutes during the period of COVID-19 outbreak.

Electronic sources identified 103 references; while after the elimination of duplicates and irrelevant titles, 42 papers were forwarded for abstract screening and later full-text assessment. Of these, 14 met the eligibility criteria. Finally, nine studies were included in the literature review profiling and in the qualitative analysis.

The research insights provided in this study are useful in terms of enhancing the view that link broad personality traits and various learning outcomes, during the necessitated transition to online learning by the public health emergency of the COVID-19 pandemic.

The objective of this study is to examine current business and management research on “Industry 4.0 base technologies” and “business models” to shed light on this vast literature and to point out future research agenda.

The authors conducted a bibliometric analysis of scientific publications based on 482 documents collected from the Scopus database and a co-citation analysis to provide an overview of business model studies related to Industry 4.0 base technologies. After that a qualitative analysis of the articles was also conducted to identify research trends and trajectories.

The results reveal the existence of five research themes: smart products (cluster 1); business model innovation (cluster 2); technological platforms (cluster 3); value creation and appropriation (cluster 4); and digital business models (cluster 5). A qualitative analysis of the articles was also conducted to identify research trends and trajectories.

First, the dataset was collected through Scopus. The authors are aware that other databases, such as Web of Science, can be used to deepen the focus of quantitative bibliometric analysis. Second, the authors based this analysis on the Industry 4.0 base technologies identified by Frank et al. (2019). The authors recognize that Industry 4.0 comprises other technologies beyond IoT, cloud computing, big data and analytics.

Drawing on these analyses, the authors submit a useful baseline for developing Industry 4.0 base technologies and considering their implications for business models.

In this paper, the authors focus their attention on the relationship between technologies underlying the fourth industrial revolution, identified by Frank et al. (2019), and the business model, with a particular focus on the developments that have occurred over the last decade and the authors performed a bibliometric analysis to consider all the burgeoning literature on the topic.

Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.

Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.

The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.

One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.

This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

Digitalization accelerates the need of tourism and hospitality ecosystems to reframe business models in line with a data-driven orientation that can foster value creation and innovation. Since the question of data-driven business models (DDBMs) in hospitality remains underexplored, this paper aims at (1) revealing the key dimensions of the data-driven redefinition of business models in smart hospitality ecosystems and (2) conceptualizing the key drivers underlying the emergence of innovation in these ecosystems.

The empirical research is based on semi-structured interviews collected from a sample of hospitality managers, employed in three different accommodation services, i.e. hotels, bed and breakfast (B&Bs) and guesthouses, to explore data-driven strategies and practices employed on site.

The findings allow to devise a conceptual framework that classifies the enabling dimensions of DDBMs in smart hospitality ecosystems. Here, the centrality of strategy conducive to the development of data-driven innovation is stressed.

The study thus developed a conceptual framework that will serve as a tool to examine the impact of digitalization in other service industries. This study will also be useful for small and medium-sized enterprises (SMEs) managers, who seek to understand the possibilities data-driven management strategies offer in view of stimulating innovation in the managers' companies.

The paper reinterprets value creation practices in business models through the lens of data-driven approaches. In this way, this paper offers a new (conceptual and empirical) perspective to investigate how the hospitality sector at large can use the massive amounts of data available to foster innovation in the sector.

The purpose of this study is to examine how five different multi-level governance (MLG) models affect place branding (PB) performance in Saudi Arabia.

In hierarchical administrative systems, central governments exert control on PB, influencing its effectiveness. While PB as such is widely studied, the effect of MLG on PB performance in centralized administrative systems remains understudied. The study is approached as a multiple case study of nine cities.

The study reveals that different MLG models indeed affect PB performance differently. Direct access to central leadership and resources boosts branding performance, while privatization promotes flexibility with similarly positive effects. Study findings, furthermore, show that some cities are considered too big to fail. Cities such as Riyadh and Neom are of prime importance and receive plenty of resources and leadership attention, while others are considered peripheral, are under-resourced and branding performance suffers accordingly. Emerging differences in PB performance associated with different MLG models are thus likely to deepen the gap between urban economic winners and losers.

This paper introduces five MLG models based on the actors involved in PB, their interactions and their access to resources. For each model, this paper assesses other factors which may influence the effectiveness of PB as well, such as access to the national leadership and staff capacity. This research thereby adds to the literature by identifying specific factors within MLG models influencing PB performance in hierarchical administrative systems.

This paper aims to examine the impact of problem-solving activities, emotional experiences and contextual and personal factors on learning from dealing with software-related problems in everyday office work.

To measure the use of problem-solving activities, emotional experiences and the contextual factors of problem characteristics and learning in situ, a research diary was used. To measure team psychological safety (contextual factor) and personal factors, including the Big Five personality traits, occupational self-efficacy and technology self-efficacy, the authors administered a self-report questionnaire. In sum, 48 students from a software company in Germany recorded 240 diary entries during five working days. The data was analysed using multilevel analysis.

Results revealed that asking others and using information from the internet are positive predictors of self-perceived learning from a software-related problem, while experimenting, which was the most common activity, had a negative effect on learning. Guilt about the problem was positively related to learning while working in the office (as opposed to remote work), and feeling irritated/annoyed/angry showed a negative effect. Surprisingly, psychological safety had a negative effect on perceived learning.

Major limitations of the study concern the convenience sample and the disregard for the sequence of the activities.

This study contributes to the limited empirical evidence on employees’ problem-solving activities and informal workplace learning in the software context. To overcome the shortcomings of previous studies using retrospective assessments and in-lab observations, this study uses the diary method to investigate in situ.