Search results

Food and beverage (F&B) small and medium-sized enterprises (SMEs) must diversify their markets and obtain predictable sources of revenues to withstand difficult and volatile periods such as the post-pandemic geopolitical scenario, recently burdened by the Russian-Ukrainian conflict. On the other hand, another strand of the literature suggests that public procurement could be considered a great source of income, enabling solid contracts, revenues and cash-flow stability. Therefore, this paper aims to explore the role of public procurement, the adoption of e-commerce platforms and their interactions in affecting the exporting performances of SMEs operating in the F&B sector.

The study retrieves data from 2,186 Italian F&B manufacturing SMEs relying on Margò by Cribis database. Therefore, it conducts a structured equational model (SEM) to test the developed hypotheses empirically.

The findings reveal that digital selling platforms positively affect exports, whereas public procurement negatively affects F&B SMEs exports. Nonetheless, findings underline that the interaction between public procurement and the adoption of digital selling platforms dampens public procurement's negative effects on exports.

This study brings an original contribution to the F&B literature by conducting empirical research on an extensive sample of firms from one of the most influential countries in the F&B vertical, Italy, with officially registered data. More importantly, to the best of the authors' knowledge, this study pioneers the investigation of the relationship between public procurement and e-commerce platforms in affecting F&B SMEs' export performances.

This paper aims to examine the effects of socio-emotional wealth importance (SEWi) in family firms and family firm-specific HR practices, namely professionalization and bifurcation bias, on their entrepreneurial orientation (EO).

The paper surveyed 133 small and medium-sized family firms in the USA. The respondents were recruited through Prolific Academic.

When SEWi is low, a family firm becomes more similar to a non-family firm, thereby enjoying the benefits associated with EO. When SEWi is high, a family firm leverages the unique resources and capabilities specific to family firms. Moderate SEWi levels are associated with lower EO levels. Additionally, the results support the argument that professionalization (involving non-family managers, formalization and decentralization) fosters EO, while bifurcation bias hinders its development.

Unlike previous studies, this paper posits a non-linear, U-shaped relationship between SEWi and EO. It contributes to the field by empirically investigating the effects of professionalization and bifurcation bias on EO in family firms.

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.

The objective of the study is to understand the relationship between entrepreneurial orientation (EO), employers' organizational commitment to their employees (OCE), employees' perception of the adequacy of budgetary resources (BRs), and firm performance in Malaysian and Chilean firms. A survey of 165 managers finds that EO has a significant positive relationship with performance. Additionally, the perceptions about the adequacy of the BR have a significant positive effect on performance. And in the Chilean subsample, the OCE interacts positively with perceptions of budget resource adequacy to affect the firm performance. The total effect of EO on performance is similar across both countries despite the EO being higher in Malaysia and perceptions of budget resource adequacy being higher in Chile. The study highlights the contribution to the literature on the EO – performance relationship – and emphasizes the importance of providing adequate resources to enhance the psychological safety of employees working in less developed countries.

We wanted to find out how infant multinationals originating from Poland enact opportunities in Sub-Saharan African (SSA) countries.

We conducted a comparative case study of four Polish firms operating in SSA.

We found that when entering SSA, studied firms employed effectual decision-making logic. Thus, their internationalization was means-driven, serendipitous, partnership-oriented, based on the “affordable loss” principle and focused on shaping opportunities in SSA, rather than predicting, analyzing and planning any firm-specific assets or capabilities.

We illuminated the nature of the means employed in effectual internationalization and the role of partners (“effectual stakeholders”) in this process. Thus, we contribute to a deeper understanding of how infant multinationals navigate extreme uncertainty in the emerging SSA markets.

This study aims to use regression Least-Square Support Vector Machine (LS-SVM) as a probabilistic model to determine the factor of safety (FS) and probability of failure (PF) of anisotropic soil slopes.

This research uses machine learning (ML) techniques to predict soil slope failure. Due to the lack of analytical solutions for measuring FS and PF, it is more convenient to use surrogate models like probabilistic modeling, which is suitable for performing repetitive calculations to compute the effect of uncertainty on the anisotropic soil slope stability. The study first uses the Limit Equilibrium Method (LEM) based on a probabilistic evaluation over the Latin Hypercube Sampling (LHS) technique for two anisotropic soil slope profiles to assess FS and PF. Then, using one of the supervised methods of ML named LS-SVM, the outcomes (FS and PF) were compared to evaluate the efficiency of the LS-SVM method in predicting the stability of such complex soil slope profiles.

This method increases the computational performance of low-probability analysis significantly. The compared results by FS-PF plots show that the proposed method is valuable for analyzing complex slopes under different probabilistic distributions. Accordingly, to obtain a precise estimate of slope stability, all layers must be included in the probabilistic modeling in the LS-SVM method.

Combining LS-SVM and LEM offers a unique and innovative approach to address the anisotropic behavior of soil slope stability analysis. The initiative part of this paper is to evaluate the stability of an anisotropic soil slope based on one ML method, the Least-Square Support Vector Machine (LS-SVM). The soil slope is defined as complex because there are uncertainties in the slope profile characteristics transformed to LS-SVM. Consequently, several input parameters are effective in finding FS and PF as output parameters.

The study examines the impact of behavioral biases, such as herd behavior, overconfidence and reactions to ESG News, on Socially Responsible Investing (SRI) decisions in the Indian context. Additionally, it explores gender differences in SRI decisions, thereby deepening the understanding of the factors shaping SRI choices and their implications for sustainable finance and gender-inclusive investment strategies.

The study employs Bayesian linear regression to analyze the impact of behavioral biases on SRI decisions among Indian investors since it accommodates uncertainties and integrates prior knowledge into the analysis. Posterior distributions are determined using the Markov chain Monte Carlo technique, ensuring robust and reliable results.

The presence of behavioral biases presents challenges and opportunities in the financial sector, hindering investors’ SRI engagement but offering valuable opportunities for targeted interventions. Peer advice and hot stocks strongly predict SRI engagement, indicating external influences. Investors reacting to extreme ESG events increasingly integrate sustainability into investment decisions. Gender differences reveal a greater inclination of women towards SRI in India.

The sample size was relatively small and restricted to a specific geographic region, which may limit the generalizability of the findings to other areas. While efforts were made to select a diverse sample, the results may represent something different than the broader population. The research focused solely on individual investors and did not consider the perspectives of institutional investors or other stakeholders in the SRI industry.

The study's practical implications are twofold. First, knowing how behavioral biases, such as herd behavior, overconfidence, and reactions to ESG news, affect SRI decisions can help investors and managers make better and more sustainable investment decisions. To reduce biases and encourage responsible investing, strategies might be created. In addition, the discovery of gender differences in SRI decisions, with women showing a stronger propensity, emphasizes the need for targeted marketing and communication strategies to promote more engagement in sustainable finance. These implications provide valuable insights for investors, managers, and policymakers seeking to advance sustainable investment practices.

The study has important social implications. It offers insights into the factors influencing individuals' SRI decisions, contributing to greater awareness and responsible investment practices. The gender disparities found in the study serve as a reminder of the importance of inclusivity in sustainable finance to promote balanced and equitable participation. Addressing these disparities can empower individuals of both genders to contribute to positive social and environmental change. Overall, the study encourages responsible investing and has a beneficial social impact by working towards a more sustainable and socially conscious financial system.

This study addresses a significant research gap by employing Bayesian linear regression method to examine the impact of behavioral biases on SRI decisions thereby offering more meaningful results compared to conventional frequentist estimation. Furthermore, the integration of behavioral finance with sustainable finance offers novel perspectives, contributing to the understanding of investors, investment managers, and policymakers, therefore, catalyzing responsible capital allocation. The study's exploration of gender dynamics adds a new dimension to the existing research on SRI and behavioral finance.

This paper aims to examine the multiplicity of corporate social responsibility (CSR) standards, explaining its nature, dynamics and implications for multinational enterprises (MNEs) and international business (IB), especially in the context of CSR and global value chain (GVC) governance.

This paper leverages insights from the literature in political science, policy, regulation, governance and IB; from the own earlier work; and from an inventory of CSR standards across a range of sectors and products.

This analysis’ more nuanced approach to CSR standard multiplicity helps distinguish the different categories of standards; uncovers the existence of different types of standard multiplicity; and highlights complex trends in their evolution over time, discussing implications for the various firms targeted by, or involved in, these initiatives, and for CSR and GVC governance research.

This paper opens many avenues for future research on CSR multiplicity and its consequences; on lead firms governing GVCs from an IB perspective; and on institutional and market complexity.

By providing overviews and classifications, this paper helps clarify CSR standards as “new regulators” and “instruments” for actors in business, society and government.

This paper contributes by filling gaps in different existing literatures concerning standard multiplicity. It also specifically adds a new perspective to the IB literature, which thus far has not fully incorporated the complexity and dynamics of CSR standard multiplicity in examining GVCs and MNE strategy and policy.

Cybersecurity vulnerabilities are often due to human users acting according to their own ethical priorities. With the goal of providing tailored training to cybersecurity professionals, the authors conducted a study to uncover profiles of human factors that influence which ethical principles are valued highest following exposure to ethical dilemmas presented in a cybersecurity game.

The authors’ game first sensitises players (cybersecurity trainees) to five cybersecurity ethical principles (beneficence, non-maleficence, justice, autonomy and explicability) and then allows the player to explore their application in multiple cybersecurity scenarios. After playing the game, players rank the five ethical principles in terms of importance. A total of 250 first-year cybersecurity students played the game. To develop profiles, the authors collected players' demographics, knowledge about ethics, personality, moral stance and values.

The authors built models to predict the importance of each of the five ethical principles. The analyses show that, generally, the main driver influencing the priority given to specific ethical principles is cultural background, followed by the personality traits of extraversion and conscientiousness. The importance of the ingroup was also a prominent factor.

Cybersecurity professionals need to understand the impact of users' ethical choices. To provide ethics training, the profiles uncovered will be used to build artificially intelligent (AI) non-player characters (NPCs) to expose the player to multiple viewpoints. The NPCs will adapt their training according to the predicted players’ viewpoint.