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The purpose of this study was to examine if team composition based on adaption-innovation (A-I) problem-solving styles is related to the teamwork quality and team effectiveness (TE) of student project teams participating in a [state-gifted program (SGP)].

A correlational design was conducted with a sample of 72 (SGP) participants, consisting of 15 project teams (n = 15), which formed three groups: (1) the homogeneous adaptive group, which consists of five homogeneous adaptive teams (n = 5); (2) the homogeneous innovative group, which consists of five homogeneous innovative teams (n = 5), and (3) the heterogeneous group (i.e. a mix of innovative and adaptive individuals), which consists of five heterogeneous teams (n = 5).

A one-way ANOVA and post-hoc test revealed that team composition based on problem-solving styles is related to teamwork quality and TE. Regarding TE, both homogeneous groups (i.e. all adaptive or all innovative individuals) were more effective than the heterogeneous group. However, regarding teamwork quality, only the adaptive group had significantly higher teamwork quality than the heterogeneous group.

We recommend that leadership educators utilize Kirton’s adaption-innovation inventory (KAI) as a tool for building effective student project teams. KAI can be used by leadership educators in two major ways: to assign students to groups (as done in the current study) or for team building, where team members share their KAI scores to better understand their problem-solving preferences.

The findings add to the literature by specifying the type of homogeneous groups (i.e. homogeneous adaptive), which may offer an advantage over heterogeneous groups regarding teamwork quality.

In this study, the authors argue that the private information obtained and transmitted by institutions during the corporate visits can alleviate the degree of information asymmetry between firms and investors, so institutional visits may influence investors' heterogeneous beliefs. Therefore, the authors investigated whether and how institutional investors' corporate visits affect investors' heterogeneous beliefs.

This study examines whether and how institutional investors' corporate visits affect investors' heterogeneous beliefs using the data of A-share companies from the Shenzhen Stock Exchange (SZSE) during 2013–2019. Using empirical research method, this study designs and conducts an empirical research according to empirical research's basic norms.

The authors find that institutional visits effectively decrease investors' heterogeneous beliefs, especially institutional investors. Meanwhile, institutional site visits and sell-side institutional visits have a more significant negative effect on investors' heterogeneous beliefs. The findings remain after robustness tests with the alternative variable, instrumental variable, propensity score matching and quantile regression methods.

The development of China's capital market is imperfect, resulting in a strong speculative atmosphere. So, investors' irrational investment behaviors occur from time to time, leading to sizeable heterogeneous beliefs in China's capital market, which increases the risk of investment and is not conducive to the discovery of corporate value and the efficient allocation of resources. Therefore, exploring the factors influencing heterogeneous beliefs and finding ways to alleviate heterogeneous beliefs can reduce the proportion of speculative investors and promote the healthy development of China's capital market.

Heterogeneous risk disclosure has been proven to improve the efficiency of new stock issuance, but excessive risk disclosure during the IPO may lead to irrational underestimation of the company, which is different from the original intention of management's detailed disclosure. Therefore, this study aims to examine the impact of IPO heterogeneous risk disclosure on earnings management motivations from the information transfer perspective of earnings management.

The sample includes 2,000 listed companies listed firms on Shanghai and Shenzhen Stock Exchanges from 2007 to 2022. This study uses the pretrained ERNIE model to measure text similarity in the prospectus to measure the heterogeneity of IPO risk disclosure.

This study empirically finds that heterogeneous IPO risk disclosure suppresses the opportunistic motivation of earnings management because managers tend to use earnings management to leverage information transmission functions. Such an effect is more pronounced in firms with higher analyst attention, lower marketization levels and non-state-owned. And heterogeneous risk disclosure may inhibit management’s over-investment behavior, thereby reducing the possibility of management engaging in opportunistic earnings management. Besides, price discounts are used to distinguish opportunistic and non-opportunistic earnings management and carry out a quasi-natural experimental design to demonstrate that marketization can enhance the relationship between heterogeneous risk disclosure and earnings management.

This study contributes evidence regarding the economic consequences of managerial earnings management behavior related to heterogeneous IPO risk disclosure. It supports highlighted firms in the IPO risk information disclosure to mitigate potential adverse outcomes through earnings management. This contributes to the literature and enhances information transparency in the capital market, fostering the healthy development of China’s capital market.

The purpose of this paper is to improve the existing paradigm of edge computing to maintain a balanced energy usage.

The new greedy algorithm is proposed to balance the energy consumption in edge computing.

The new greedy algorithm can balance energy more efficiently than the random approach by an average of 66.59 percent.

The results are shown in this paper which are better as compared to existing algorithms.

Customers will develop a stronger desire to purchase when more people are waiting in line for service due to the herding effect. However, this also leads to longer queue times, causing customers to experience a waiting patience time. This study examines these two psychological aspects of delay-sensitive customers in service systems, considering both homogeneous and heterogeneous customer scenarios to explore the optimal pricing strategy for service providers.

Using queueing theory, we construct and optimally solve the customer's service utility function and the service provider's service revenue function. Further, the model is extended to account for heterogeneous customers, solving the utility and revenue functions accordingly.

Results show that service revenue increases with the intensity of herding behavior and the length of patience time. If customers have low herding intensity and short patience time, the service provider only needs to serve a portion of the customers. For heterogeneous customers, if a large proportion exhibits high herding intensity, the service provider should focus on serving them. Otherwise, the service provider should serve all high-intensity herding customers while striving to attract low-intensity herding customers.

This paper considers the combined utility of multiple customer psychology and examines homogeneous and heterogeneous customers. The findings provide valuable managerial insights for service providers' pricing and service strategies.

This study investigates how federated learning (FL) and human–robot collaboration (HRC) can be used to manage diverse industrial environments effectively. We aim to demonstrate how these technologies not only improve cooperation between humans and robots but also significantly enhance productivity and innovation within industrial settings. Our research proposes a new framework that integrates these advancements, paving the way for smarter and more efficient factories.

This paper looks into the difficulties of handling diverse industrial setups and explores how combining FL and HRC in the mark of Industry 5.0 paradigm could help. A literature review is conducted to explore the theoretical insights, methods and applications of these technologies that justify our proposal. Based on this, a conceptual framework is proposed that integrates these technologies to manage heterogeneous industrial environments.

The findings drawn from the literature review performed, demonstrate that personalized FL can empower robots to evolve into intelligent collaborators capable of seamlessly aligning their actions and responses with the intricacies of factory environments and the preferences of human workers. This enhanced adaptability results in more efficient, harmonious and context-sensitive collaborations, ultimately enhancing productivity and adaptability in industrial operations.

This research underscores the innovative potential of personalized FL in reshaping the HRC landscape for manage heterogeneous industrial environments, marking a transformative shift from traditional automation to intelligent collaboration. It lays the foundation for a future where human–robot interactions are not only more efficient but also more harmonious and contextually aware, offering significant value to the industrial sector.

This paper aims to develop a framework for the bioaccounting measurement of environmental assets based on natural wealth sustainability. Specifically, this paper proposes a theoretical structure for qualitative and quantitative organization-level assessments of the existence and circulation of water, air, wildlife, flora, soil and subsoil resources.

This research used an inductive method with a qualitative and quantitative approach. The authors postulate a systemic and comprehensive bioaccounting measurement of environmental assets, including heterogeneous and homogeneous methods and quantitative and qualitative valuations of the resources that comprise environmental assets.

The authors describe a theoretical structure for the bioaccounting measurement of environmental assets based on the sustainability of natural wealth through heterogeneous and homogeneous measurement methods and show how to integrate these assets through an homogeneous method.

The development of this general theoretical structure will require the integration of theoretical, conceptual and technical developments from multiple disciplines. The authors hope that the scientific community will evaluate and study this proposal for faster progress towards its practical implementation in organizations.

The authors structured the bioaccounting measurements, which are presented individually for each class of environmental assets. Each of these assets requires subcategories (accounts, subaccounts and resources) and recognition/measurement units. Environmental value units (EVUs) are used to standardize the plurality of measurement units.

This paper examines the relationship between heterogeneous political connections and corporate overinvestment.

Based on a comprehensive Malaysian dataset of 834 publicly listed companies from 2000 to 2022, the authors employed multivariate ordinary least squares regression to test the relationship.

Despite different types of political connections, the findings demonstrate a positive relationship between political connections and corporate overinvestment. In particular, the association is more profound in government-linked companies (GLCs) but weaker in firms that developed political ties through family members of ruling elites. Further analysis reveals that the “helping hand” effect is only observed in GLCs and firms with politically connected directors and businessmen, whereas the “grabbing hand” effect is observed among firms connected through board, businessmen, and family ties. Moreover, the relationship is more persistent among firms with politically connected directors and businessmen around the regime change.

Regardless of the types of political connections, the findings show that politically connected firms tend to engage in rent-seeking through political patronage networks and high levels of government interference in resource allocation. Therefore, a more sophisticated monitoring system should be developed within the political patronage networks to reduce the likelihood of different types of political-business collusion. In terms of research limitations, the research design does not consider the influence of financial constraints and management efficiency. Future research could explore these facets to comprehensively understand the dynamics between political connections and corporate investment decisions.

The evidence informs market participants about the relationship between heterogeneous political connections and corporate overinvestment, reinforcing previous findings that crony capitalism, political patronage, agency problems, and weak governance are well-entrenched in Malaysia’s emerging economy. The government should acknowledge these concerns by enacting anti-corruption campaigns and promoting a fair business environment. In the meantime, policymakers might redesign regulations and revise corporate governance frameworks to substantially reduce the value of political connections, thereby diminishing the bargaining power of politicians.

As corporate investment efficiency has a considerable impact on firm value, investment decisions that enhance firm value will increase share price and maximise shareholder value. Conversely, firms may damage shareholder value if they overinvest or undertake projects that do not yield sufficient. Hence, the findings of this study may assist investors in making more informed judgements, particularly by understanding different types of business-government relations, as political connections are one of the determinants of corporate overinvestment.

This study reveals that the degree to which overinvestment issues manifest within firms is influenced by the nature of the political connections those firms possess. This indicates that politically connected firms should not be regarded as a homogenous group of firms.

How to embed artificial intelligence (AI) in human resource management (HRM) is one of the core challenges of digital HRM. Despite regulations demanding humans in the loop to ensure human oversight of AI-based decisions, it is still unknown how much decision-makers rely on information provided by AI and how this affects (personnel) selection quality.

This paper presents an experimental study using vignettes of dashboard prototypes to investigate the effect of AI on decision-makers’ overreliance in personnel selection, particularly the impact of decision-makers’ information search behavior on selection quality.

Our study revealed decision-makers’ tendency towards status quo bias when using an AI-based ranking system, meaning that they paid more attention to applicants that were ranked higher than those ranked lower. We identified three information search strategies that have different effects on selection quality: (1) homogeneous search coverage, (2) heterogeneous search coverage, and (3) no information search. The more applicants were searched equally often (i.e. homogeneous) as when certain applicants received more search views than others (i.e. heterogeneous) the higher the search intensity was, resulting in higher selection quality. No information search is characterized by low search intensity and low selection quality. Priming decision-makers towards carrying responsibility for their decisions or explaining potential AI shortcomings had no moderating effect on the relationship between search coverage and selection quality.

Our study highlights the presence of status quo bias in personnel selection given AI-based applicant rankings, emphasizing the danger that decision-makers over-rely on AI-based recommendations.

This paper aims to analyze the heterogeneous effect of prudential regulation on the stability of banks in the West African Economic and Monetary Union (WAEMU).

The author uses in this study individual bank data from balance sheets, income statements of banks in the WAEMU space and annual reports of the banking commission formed into a three-year panel from the period 2017 to 2019. First, this study uses hierarchical clustering based on specific banking characteristics to determine whether the WAEMU region’s banking markets are heterogeneous or not. Second, this study uses quantile regression approach with fixed effects to explore how that prudential regulation affects the conditional distribution of WAEMU bank stability.

The analysis reveals heterogeneity resulting in two distinct groups. Using the quantile regression approach, this study demonstrates that prudential regulation has a significantly more substantial and positive effect on the upper quantiles than on the lower quantiles of the conditional distribution of WAEMU bank stability. Furthermore, the effect of banking regulation also varies among pan-African cross-border banks, national banks and foreign banks. Among these types of banks, pan-African cross-border banks remain the most stable by adopting prudential regulation. The results remain robust and vary across different WAEMU countries.

The contribution of this study to the literature is multifaceted. First, this study uses individual bank-level constituted in panel data from the WAEMU region to assess the effect of prudential regulation on the stability of the WAEMU’s banking sector. This approach allows for a more granular analysis as this study considers individual regional banks’ specific characteristics and behaviors. Second, this study considers the heterogeneous effect of regulation on the stability of banks within the WAEMU space. This means that this study acknowledges that not all banks are affected similarly by prudential regulations, and this research aims to identify and quantify these differences.