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The current study used the social exchange theory to examine the dimensional impact of total quality management (TQM) on the organisational citizenship behaviour for the environment (OCBE) of employees through the mediatory role of environmental corporate social responsibility (ECSR).

The data were collected from 311 employees of ISO-14001-certified manufacturing firms in Pakistan. Both symmetrical partial least squares structural equation modelling (PLS-SEM) and asymmetrical fuzzy-set qualitative comparative analysis (fsQCA) methods were applied to test the proposed hypotheses to enhance the robustness of the results.

PLS-SEM results discovered that all dimensions of TQM, like process management, leadership, human resource management, customer focus, information and analysis and strategic planning, foster the OCBE through the mediation role of ECSR. The robustness of the findings was manifold when fsQCA results complemented the results by discovering that all six dimensions have been identified as sufficient conditions and some as necessary conditions to drive the OCBE.

The theoretical contribution of this study sheds light on TQM's function in boosting OCBE through the mediation of ECSR. Practically, the business managers may utilise TQM as a strategy to foster the OCBE in order to mitigate environmental damages of their organisations’ operations by instilling OCBE among the employees.

This study aims to analyze how restaurants' collaboration with mobile food delivery applications (MFDAs) affects product development efficiency and argues that technological capabilities moderate relational ties impact the joint decision-making and development efficiency of restaurant products.

A product development efficiency model was formulated using a resource-based view and real options theory. In all, 472 samples were collected from restaurants collaborating with MFDAs, and partial least squares structural equation modeling was applied to the proposed model.

The findings of this study indicate three factors are critical to the product development efficiency between restaurants and MFDAs; restaurants must develop a strong connection with the latter to ensure meals are consistently served promptly. Developers of MFDAs should use artificial intelligence analysis, such as order records of different genders and ages or various consumption attributes, to collaborate with restaurants.

To the best of the authors’ knowledge, this study is one of the few that considers the role of MFDAs as a product strategy for restaurant operations, and the factors the authors found can enhance restaurants’ product development efficiency. Second, as strategic artificial intelligence adaptation changes, collaborating firms and restaurants use such applications for product development to help consumers identify products.

The demand for healthcare innovation is increasing, and not much is known about how entrepreneurial firms search for and sell to customers in the highly regulated and complex healthcare market. Drawing on effectuation perspectives, we explore how entrepreneurial digital healthcare firms with disruptive innovations search for early customers in the healthcare sector.

This study uses a qualitative, longitudinal multiple-case design of four entrepreneurial Nordic telehealth firms. In-depth interviews were conducted with founders and senior managers over a period of 27 months.

We find that when customer buying conditions are highly flexible, case firms use effectual logic to generate customer demand for disruptive innovations. However, under constrained buying conditions firms adopt a more causal approach to customer search.

Managers need to gain a deep understanding of target buying environments when searching for customers. In healthcare sector markets, the degree of flexibility customers have over buying can constrain them from engaging in demand co-creation. In particular, healthcare customer access to funding streams can be a key determinant of customer flexibility.

We contribute to effectuation literature by illustrating how customer buying conditions influence decision-making logics of entrepreneurial firms searching for customers in the healthcare sector. We contribute to entrepreneurial resource search literature by illustrating how entrepreneurial firms search for customers beyond their networks in the institutionally complex healthcare sector.

This study aims to examine the impact of board characteristics on firm performance while also exploring the influential mechanisms that help Chinese listed companies establish effective boards of directors and strengthen their corporate governance mechanisms.

This paper uses machine learning methods to investigate the predictive ability of the board of directors' characteristics on firm performance based on the data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges in China during 2008–2021. This study further analyzes board characteristics with relatively strong predictive ability and their predictive models on firm performance.

The results show that nonlinear machine learning methods are more effective than traditional linear models in analyzing the impact of board characteristics on Chinese firm performance. Among the series characteristics of the board of directors, the contribution ratio in prediction from directors compensation, director shareholding ratio, the average age of directors and directors' educational level are significant, and these characteristics have a roughly nonlinear correlation to the prediction of firm performance; the improvement of the predictive ability of board characteristics on firm performance in state-owned enterprises in China performs better than that in private enterprises.

The findings of this study provide valuable suggestions for enriching the theory of board governance, strengthening board construction and optimizing the effectiveness of board governance. Furthermore, these impacts can serve as a valuable reference for board construction and selection, aiding in the rational selection of boards to establish an efficient and high-performing board of directors.

The study findings unequivocally demonstrate the superiority of nonlinear machine learning approaches over traditional linear models in examining the relationship between board characteristics and firm performance in China. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. The study reveals that the predictive performance of board attributes is generally more robust for state-owned enterprises in China in comparison to their counterparts in the private sector.

Although the effects of both news sentiment and expectations on price in financial markets have now been extensively demonstrated, the jointness that these predictors can have in their effects on price has not been well-defined. Investigating causal ordering in their effects on price can further our understanding of both direct and indirect effects in their relationship to market price.

We use autoregressive distributed lag (ARDL) methodology to examine the relationship between agent expectations and news sentiment in predicting price in a financial market. The ARDL estimation is supplemented by Grainger causality testing.

In the ARDL models we implement, measures of expectations and news sentiment and their lags were confirmed to be significantly related to market price in separate estimates. Our results further indicate that in models of relationships between these predictors, news sentiment is a significant predictor of agent expectations, but agent expectations are not significant predictors of news sentiment. Granger-causality estimates confirmed the causal inferences from ARDL results.

Taken together, the results extend our understanding of the dynamics of expectations and sentiment as exogenous information sources that relate to price in financial markets. They suggest that the extensively cited predictor of news sentiment can have both a direct effect on market price and an indirect effect on price through agent expectations.

Even traditional financial management firms now commonly track behavioral measures of expectations and market sentiment. More complete understanding of the relationship between these predictors of market price can further their representation in predictive models.

This article extends the frequently reported bivariate relationship of expectations and sentiment to market price to examine jointness in the relationship between these variables in predicting price. Inference from ARDL estimates is supported by Grainger-causality estimates.

This paper examines the relationship between entrepreneurs' internality of causal attributions and firm growth during an economic crisis. We propose a U-shaped relationship between the two variables, arguing that the highest-growth entrepreneurs are those with either the highest or lowest levels of internal attribution (IA) during such periods.

To test our hypothesis, we analyze a database of 804 interviews with entrepreneurs in Brazil during a period of economic stress. Due to the existence of endogeneity, we estimate a model of simultaneous equations in two stages.

We find evidence of a U-shaped relationship. This means that during economic stress, the fastest-growing entrepreneurs are those who rely more on their own effort (high IA) and those who attribute their success to the economic crisis (low IA).

Tailoring interventions based on attribution patterns and recognizing the U-shaped relationship ensures effective support during economic stress. Entrepreneurial support programs should align with internality levels, emphasizing external awareness or skill development accordingly. Policymakers should take attributions into account when promoting financial resilience. Entrepreneurs would benefit from awareness programs on attributions for reflective decision-making. Ecosystems should foster collaboration by recognizing diverse attributions, enhancing a collective understanding of entrepreneurial responses in crises.

Our results have important implications for understanding the role of entrepreneurs in economic crises. Our results are relevant because they challenge the usual claim that entrepreneurs with high IA are the ones who perform better in situations where external economic conditions are adverse.

The purposes of this paper are to delve into the “liability of foreignness” among immigrants and to explore factors that may enhance or moderate such liability while obtaining jobs in host countries. We explore the competition for jobs in a host country among foreign-born individuals from various backgrounds and local residents, by examining such factors as their human capital, as well as, for the foreign-born, their duration of residence in the host country.

Applying configurational theorizing, we propose that the presence of specific human capital can help reduce the challenges associated with the “liability of foreignness” for migrants who have shorter durations of stay in the host country, and, to a lesser extent, for female migrants. Our study draws upon extensive career data spanning several decades and involving 249 employees within a Danish multinational enterprise.

We find that specific human capital helps established immigrants in general, although female immigrants are more vulnerable. We furthermore find a strong “gender liability” in the industry even for local females, including returnees in the host countries. Our findings suggest that for immigrants, including returnees, career building requires a mix of right human capital and tenure in the host country, and that career building is especially challenging for female immigrants.

While the concept of “liability of foreignness” – focussing on discrimination faced by immigrants in the labour market – has been brought to the fore, a notable gap exists in empirical research pertaining to studies aiming at disentangling potential means to overcome such liability, as well as in studies seeking to explore this issue from a stance of gendered experience.

The study aims to identify the possible risk factors for electricity grids operational disruptions and to determine the most critical and influential risk indicators.

A multi-criteria decision-making best-worst method (BWM) is employed to quantitatively identify the most critical risk factors. The grey causal modeling (GCM) technique is employed to identify the causal and consequence factors and to effectively quantify them. The data used in this study consisted of two types – quantitative periodical data of critical factors taken from their respective government departments (e.g. Indian Meteorological Department, The Central Water Commission etc.) and the expert responses collected from professionals working in the Indian electric power sector.

The results of analysis for a case application in the Indian context shows that temperature dominates as the critical risk factor for electrical power grids, followed by humidity and crop production.

The study helps to understand the contribution of factors in electricity grids operational disruptions. Considering the cause consequences from the GCM causal analysis, rainfall, temperature and dam water levels are identified as the causal factors, while the crop production, stock prices, commodity prices are classified as the consequence factors. In practice, these causal factors can be controlled to reduce the overall effects.

From the results of the analysis, managers can use these outputs and compare the risk factors in electrical power grids for prioritization and subsequent considerations. It can assist the managers in efficient allocation of funds and manpower for building safeguards and creating risk management protocols based on the severity of the critical factor.

The research comprehensively analyses the risk factors of electrical power grids in India. Moreover, the study apprehends the cause-consequence pair of factors, which are having the maximum effect. Previous studies have been focused on identification of risk factors and preliminary analysis of their criticality using autoregression. This research paper takes it forward by using decision-making methods and causal analysis of the risk factors with blend of quantitative and expert response based data analysis to focus on the determination of the criticality of the risk factors for the Indian electric power grid.

Taking advantage of distinctive text-based measures of climate policy uncertainty and firm-specific exposure to climate change, this study aims to examine the impact of firm-specific vulnerability on dividend policy.

To mitigate endogeneity, the authors apply an instrumental-variable analysis based on climate policy uncertainty as well as use additional analysis using propensity score matching and entropy balancing.

The authors show that an increase in climate policy uncertainty exacerbates firm-specific exposure considerably. Exploiting climate policy uncertainty to generate exogenous variation in firm-specific exposure, the authors demonstrate that companies more susceptible to climate change are significantly less likely to pay dividends and those that do pay dividends pay significantly smaller dividends. For instance, a rise in firm-specific exposure by one standard deviation weakens the propensity to pay dividends by 5.11%. Climate policy uncertainty originates at the national level, beyond the control of individual firms and is thus plausibly exogenous, making endogeneity less likely.

To the best of the authors’ knowledge, this study is the first attempt in the literature to investigate the effect of firm-specific exposure on dividend policy using a rigorous empirical framework that is less vulnerable to endogeneity and is more likely to show a causal influence, rather than a mere correlation.

Despite the burgeoning popularity of virtual gifting in live streaming, research lacks an in-depth understanding of the drivers behind this behavior. Using para-social relationship (PSR), this study aims to capture viewers’ lively social feelings toward the streamer as the key factor leading to the purchase behavior of virtual gifts. It also aims to establish a theoretical link between PSR and viewers’ holistic experience in live streaming as captured by cognitive absorption and aims to investigates the role of technological features (i.e. viewer–streamer and viewer–viewer interactivity, streamer-level and viewer-level deep profiling and design aesthetics) in shaping viewers’ experience.

Based on 433 survey responses, this study employs a combination of structural equation modeling and neural networks to offer valuable insights into the relationships between the technological environment, viewer experience and viewer behavior.

Our results highlight the salience of PSR in promoting the purchase of virtual gifts through cognitive absorption and the importance of the technological environment in eliciting the viewer experience. This study sheds light on the development of PSR in a technological environment and its relationship with cognitive absorption.

By applying PSR to conceptualize viewers’ perceived connection with the streamer, this study extends the research on purchase behavior in the non-shopping context by providing an enlightened understanding of virtual gift purchase behavior in live streaming. Moreover, by theoretically linking PSR with cognitive absorption, virtual gift purchase and technological features of live streaming, it enriches the theory of PSR and bridges the gap between the design practice of supporting the IT infrastructure of live streaming and research.