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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.

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.

The purpose of this study is to provide new insights into the relationship between fiscal policy and total factor productivity (TFP) while accounting for several economic and econometric issues of the phenomenon like non-stationarity, fiscal feedback effects, persistence in productivity, country heterogeneity and unobserved global shocks and local spillovers affecting heterogeneously the countries in the sample.

The paper is empirical. It builds an Error Correction Model (ECM) specification within a dynamic heterogeneous framework with common correlated effects and models both reverse causality and feedback effects.

The results of this study highlight some new findings relative to the existing related literature. The outcomes suggest some relevant evidence at both the academic and policy levels: (1) the causal effects going from fiscal deficit/surplus to TFP are heterogeneous across countries; (2) the effects depend on the time horizon considered; (3) the long-run dynamics of TFP are positively impacted by improvements in fiscal budget, but only if the austerity measures do not exert slowdowns in aggregate growth.

The main originality of this study is methodological, with possible extensions to related phenomena. Relative to the existing literature, the gains of this study rely on the way econometric techniques, recently proposed in the literature, are adapted to the economic relationship of interest. The endogeneity due to the existence of reverse causality is modelled without implying relevant performance losses of the models. Moreover, this is the first article that questions whether the effects of fiscal budget on productivity depend on the impact of the former on aggregate output growth, thus emphasising the importance of the quality of fiscal adjustments.

The study attempts to examine the impact of the US-China trade war on Vietnamese exports to the United States, which has consistently served as a key market for Vietnamese goods and services in recent decades. The heterogeneous effects of the trade war on different export sectors are also evaluated.

The secondary data on Vietnamese exports to the US at a 6-digit level is collected from UN Comtrade. Besides, the difference-in-differences (DiD) method is employed to analyze the impact of the trade war on exports from Vietnam to the United States.

The findings revealed a 14% increase in total Vietnamese exports to the United States due to the trade war. Examining heterogeneous effects, certain industries, such as plastics, iron or steel articles, textiles and garments, and machinery and mechanical appliances, experience significant benefits. However, the study did not identify statistically significant effects on other sectors, such as electrical machinery products, agricultural and forestry, and furniture.

The paper is one among limited studies considering the causal effects of the trade war on a developing country, accounting for the heterogeneous effects on different export sectors.

This paper aims to investigate the heterogeneous effects of macroeconomic and financial factors across various distributions of financial deepening in 22 African countries over the past two decades (2000–2019).

The paper uses a recent method of moments quantile regression, which accounts for the often overlooked heterogeneity effects. The analysis focuses on the banking sector, which is predominant in Africa, using a broad range of macroeconomic and financial indicators.

The findings show that gross domestic product per capita positively and significantly impacts financing deepening with an increasing marginal benefit as depth increases. Trade openness positively and substantially affects only high financial deepening. Real interest rate, real exchange rate and inflations negatively and significantly affect financial deepening, especially at higher than lower levels. Financial stability positively and substantially influences financial deepening with an increasing marginal benefit as the depth increases. Bank lending interest rate, bank lending–deposit rate spread, bank concentration and return on equity negatively and substantially impact higher levels of financial deepening than lower levels.

These findings are crucial to policymakers and development partners, as promoting a favourable financial environment and stable macroeconomic policies based on the heterogeneity of financial depths can increase debt financing in Africa.

To the best of the authors’ knowledge, this paper is one of the first attempts to analyse the heterogeneous effects of macroeconomic and financial determinants on varying levels of financial depth in Africa.

The purpose of this paper is to understand the distributional impact of house price increases on consumption in the context of the energy transition.

This study draws from two micro cross-sectional datasets, the English Housing Survey (EHS) and the Living Costs and Food Survey (LCFS) to study the Marginal Propensity to Consume (MPC) out of changes in house prices. By employing pseudo-panel regressions, the paper examines the impact of house price changes on consumption among diverse household types.

This paper finds varying consumption responses to house price changes across age and tenure groups. Older homeowners tend to increase consumption when house prices rise. In contrast, middle-aged individuals, often renters or mortgage holders, reduce consumption in response to price increases. The youngest age group also experiences increased consumption but to a lesser degree than the oldest group. Energy-efficient homes are related to lower consumption across all tenure levels. However, when interacted with house prices and age, the estimates are positive, pointing to an unequal accrual of property premiums depending on housing market positions.

The main limitations stem from data constraints. First, using a pseudo-panel approach hinders control for unobservable selection bias. Additionally, while robust under cross-validation and specifications tests, the energy efficiency variable imputation results in a low number of energy-efficient homes. Due to heterogeneous responses to rising house prices, this paper contends that an energy transition model that subsidises homeowners’ renovation is likely to produce a negative impact on consumption among younger and middle-aged households.

This paper contributes to the MPC literature by incorporating energy efficiency as a key variable. It draws from recent data to obtain new estimates. By highlighting shifts in consumption patterns the paper contributes to a well-established body of literature with renewed policy relevance regarding housing retrofit.

This article explores the heterogenous social mechanisms that drive responsible environmental behaviours by investigating differences in the mean effect of the psychosocial determinants of the intention to buy organic foods.

Using data for a representative sample of the Spanish population, we estimated the mean effect of the constructs represented in the responsible environmental behaviour (REB) theory that affect sustainable food consumption, and examined the social mechanisms that may explain heterogeneity in the mean effect of those constructs. Confirmatory factor analysis, linear regression, and latent class regression were used in the analysis.

We found that the effect of REB’s psychosocial constructs varied significantly, demonstrating social heterogeneity in the estimated average effect. We identified different social mechanisms that explain variations in organic food purchase intentions: environmental attitudes and social norms shape these intentions among socioeconomically privileged consumers, whereas personal norms shape these intentions among less socially advantaged consumers.

Our research contributes to the literature by highlighting the existence of differing social mechanisms explaining organic food purchase intentions. The uncovering of three social mechanisms explaining differences in the mean effect of factors driving those intentions provides valuable insights with regard to both further developing a holistic framework for responsible environmental behaviours and developing new public policies and marketing strategies aimed at improving sustainable food consumption.

Status distinctions matter among heterogeneous organizations within a socio-environmental field. This is exemplified in the food waste field, where six types of organizations employ different excess strategies to address the issue. Theoretically, we propose that status is constructed internally through advice relationships and externally through evaluations. We posit that organizations conducting evaluations and advocating legitimate principles based on expertise (i.e., Others) are status winners. Our mixed-method study confirms that Others hold privileged positions and identifies status inconsistencies. By critically illuminating these status dynamics, we contribute to a better understanding of the roles of organizations and status in tackling socio-environmental issues.

The purpose of this paper is to introduce a three-echelon multimodal transportation problem applied to a humanitarian logistic case study that occurred in Mexico.

This study develops a methodology combining a transshipment problem and an adaptation of the multidepot heterogeneous fleet vehicle routing problem to construct a mathematical model that incorporates the use of land-based vehicles and drones. The model was applied to the case study of the Earthquake on September 19, 2017, in Mexico, using the Gurobi optimization solver.

The results ratified the relevance of the study, showing an inverse relationship between transportation costs and delivery time; on the flip side, the model performed in a shorter CPU time with medium and small instances than with large instances.

While the size of the instances limits the use of the model for big-scale problems, this approach manages to provide a good representation of a transportation network during a natural disaster using drones in the last-mile deliveries.

The present study contributes to a model that combines a vehicle routing problem with transshipment, multiple depots and a heterogeneous fleet including land-based vehicles and drones. There are multiple models present in the literature for these types of problems that incorporate the use of these transportation modes; however, to the best of the authors’ knowledge, there are still no proposals similar to this study.

We expand the recent literature on the dynamics of capital structure decisions by investigating the impact of national culture on firms' optimal debt ratios and their dynamic re-adjustment process. To this end, we aim at estimating firm-specific speeds of leverage adjustment, allowing for heterogeneous dynamics in firms' capital structure.

We use dynamic panel data estimators to analyze the impact of cultural factors on the dynamics of debt ratios.

We show that national culture affects the optimal level of leverage and the dynamic rebalancing of debt ratios, both directly and indirectly, by altering the effect of firm characteristics and macroeconomic factors on firms' financing behavior. Firms converge faster towards the optimal leverage in countries with a stronger attitude to conform with the norm, while they are slower where there is a higher propensity to intellectual autonomy. A higher risk aversion and long-run propensity induce over-levered firms to reduce leverage faster, making the adjustment process strongly asymmetric. Moreover, national culture also produces indirect effects by mitigating the impact of asymmetric information on capital structure decisions. Indeed, firms in more individualistic countries display a lower speed of adjustment and a stronger effect of firm characteristics associated with higher agency costs. On the contrary, firms in countries with a higher tendency to conform to social norms, less individualistic and more long-term oriented have a higher adjustment speed and appear to suffer less from agency issues. Our results therefore highlight how national culture affects agency problems within firms, thus suggesting the adoption of country-specific corporate governance provisions accounting for the effects of local cultural traits on managers' behavior.

We expand the capital structure and governance literature by showing how cultural traits impact on the dynamics of debt ratios. In particular, we show how cultural traits may mitigate or exacerbate the role of agency issues on firms' behavior, hence suggesting that cultural factors may interact with governance rules in shaping firms' decisions. Therefore, our work highlights how policy-makers should include cultural aspects when defining regulation concerning corporate governance.