Search results

This study aims to investigate the impact of job loss on the mental health of individuals in Tunisia during the COVID-19 crisis.

In this research, the authors use the counterfactual decomposition technique and the potential outcome approach. In the first part, the authors calculated mental health indicators for all individuals included in the sample based on the World Health Organization-5 items. The individuals were then grouped into two subpopulations: the first group included those who had lost their jobs and the second group included individuals whose status in the labor market had remained unchanged. In the second part, the authors used the Blinder and Oaxaca decomposition to explain the mean difference in the mental health scores between the two groups and determine the factors contributing to this difference.

The empirical results identified symptoms of depressed mood, decreased energy and loss of interest in several individuals. Based on these three symptoms, the authors were able to classify individuals into three types of depression: mild, moderate and severe. In addition, it appeared that job loss had significantly contributed to the worsening mental health of the individuals.

Although the psychological impact of the COVID-19 outbreak among health-care professionals has been the subject of other studies in health literature on Tunisia, to the best of the authors’ knowledge, no research has addressed the impact of job loss on the mental health of Tunisian workers. Thus, this study fills this gap in the literature.

To answer the questions: what roles windows of opportunity act in the catchup process of latecomers, what strategies latecomer enterprises should adopt to size windows of opportunity to catch-up with incumbents even going beyond?

This paper studies the catch-up history of the Chinese mobile phone industry and proposes a sectoral innovation system under scenario of technology paradigm shifts. Then a history-friendly simulation model and counterfactual analysis are conducted to learn how different windows of opportunity and catch-up strategies influence the catch-up performance of latecomers.

Results show latecomers can catch up with technology ability by utilizing technology window and path-creating strategy. However, catching up with the market is not guaranteed. Demand window can help latecomers to catch up with market as it increases their survival rates, different sized windows benefit different strategies. However, it also enlarges incumbents' scale effect. Without technology window technology catch up is not guaranteed. Two windows have combination effects. Demand window affects the “degree” of change in survival rates, while the technology window affects the “speed” of change. Demand window provides security; technology window provides the possibility of a breakthrough for technology ability.

The findings of this paper provide theoretical guidance for latecomer enterprises to choose appropriate catch-up strategies to seize different opportunity windows.

This paper emphasizes the abrupt change of industrial innovation system caused by technology paradigm shifts, which makes up for the shortcomings of previous researches on industrial innovation system which either studied the influence of static factors or based on the influence of continuous changes.

Organizations are faced with increasing pressure to engage in sustainable development. There is an ongoing discussion on how to incorporate green thinking into lean management systems. This study aims to investigate configurations of lean and green supply chain management (GSCM) practices associated with high environmental performance.

The study uses survey data from a sample of Portuguese manufacturing firms and apply fuzzy-set qualitative comparative analysis to examine the data. This configurational technique allows to capture the synergetic effect of lean and GSCM practices and identify distinct combinations leading to the outcome of interest.

Seven configurations of lean and green practices are associated with high environmental performance. The implementation of lean practices is required in all configurations. Analysis of alternative combinations reveals trade-offs between lean initiatives and environmental goals. Four configurations require low level of implementation of pull production. In one configuration, high environmental performance is achieved with low implementation of statistical process control and without lean employee involvement.

This study expands the literature on lean management by identifying different strategies to integrate lean and GSCM practices to achieve high environmental performance.

The findings suggest different strategies to achieve high environmental performance. Managers need to selectively implement lean and green supply chain practices to achieve the desired combinatorial effect, which may require not to implement specific lean practices.

The study demonstrates the synergetic effects of lean and green practices on environmental performance using a configurational perspective. In addition, it identifies combinations that require a low level of implementation of specific lean practices.

This paper finds the returns from soil conservation practices and examines whether the welfare implications of adopting the conservation practices are heterogeneous across the farming groups in Indian agriculture.

The study uses an endogenous switching regression (ESR) method on the data collected from the 77th round of National Sample Survey (2019–21) to quantify the returns from adopting soil conservation practices.

It finds that farmers adopting soil health conservation practices would have reduced their crop yield by 13% if they did not implement them. Similarly, smallholders who have not adopted soil health management practices would have increased crop yield by 16% if they had adopted the practices. The authors also observed that the returns from adopting soil health management practices vary across farming groups, where marginal and large farms tend to gain higher yields. Finally, the authors find that regardless of farm size, smallholders who did not adopt soil health management practices would benefit from adopting these with increased crop yields of 29%–31%.

More data could have been better for drawing policy implications, since the number of soil card users are relatively less.

This research work uses nationally representative data, which is first in nature on this very aspect.

The purpose of this study is to analyze the combined effect of eight factors – performance expectancy (PE), effort expectancy (EE), hedonic motivation (HM), system quality (SQ), information quality (IQ), service quality (SEQ), digital literacy (DL) and computer anxiety (CA) on learners’ behavioral intention (BI) toward the adoption of e-learning in higher education institutions (HEIs) in India.

The study used factors from two theoretical models, the extended Unified Theory of Acceptance and Use of Technology and the DeLone and McLean Information Systems Success model. The study also considered DL and CA as additional factors because they could affect a learner’s intention in a developing country like India. Data were collected from three HEIs in Southern India and analyzed using fuzzy qualitative and comparative analysis (fsQCA).

The results of the study emphasize the importance of considering both individual and technological factors in e-learning adoption and provide evidence for the significance of integrating multiple theories in understanding the complex relationship between factors and learners’ BI. Four different configurations of the eight factors: EE*HM*SQ*IQ*SEQ*DL*∼CA; PE*EE*HM*SQ*IQ*DL*CA; PE*EE*HM*IQ*SEQ*DL*CA; and PE*EE*SQ*IQ*SEQ*DL*CA found to be sufficient to cause learners’ BI to use e-learning.

This study explores the complex relationship between different factors and learners’ intention to adopt e-learning using the fsQCA method. These findings may need further validation in HEIs across different geographical locations.

This study provides practical insights for HEIs in India and other developing countries on how different factors combine and interact to determine e-learning adoption in multiple contexts.

Using fsQCA as a novel and rigorous method, this study uncovers the complex and nonlinear causal relationships among various factors that affect e-learning adoption. This study provides a holistic and contextualized understanding of e-learning adoption in a developing country’s scenario. This study can inform educators and policymakers on how to design and implement effective e-learning strategies that suit different learner profiles and contexts.

Smartphone apps collect users' personal information, which triggers privacy concerns for app users. Consequently, app users restrict apps from accessing their personal information. This may impact the effectiveness of in-app advertising. However, research has not yet demonstrated what factors impact app users' decisions to use apps with restricted permissions. This study is aimed to bridge this gap.

Using a quantitative research method, the authors collected the data from 384 app users via a structured questionnaire. The data were analysed using AMOS and fuzzy-set qualitative comparative analysis (fsQCA).

The findings suggest privacy concerns and risks have a significant positive effect on app usage with restricted permissions, whilst reputation, trust and perceived benefits have significant negative impact on it. Some app-related factors, such as the number of apps installed and type of apps, also impact app usage with restricted permissions.

Based on the findings, the authors provided several implications for app stores, app developers and app marketers.

This study examines the factors that influence smartphone users' decisions to use apps with restricted permission requests. By doing this, the authors' study contributes to the consumer behaviour literature in the context of smartphone app usage. Also, by explaining the underlying mechanisms through which the principles of communication privacy management theory operate in smartphone app context, the authors' research contributes to the communication privacy management theory.

This study aims to investigate the configurational effects of five rules – artificial intelligence (AI)-based hiring decision transparency, consistency, voice, explainability and human involvement – on applicants' procedural justice perception (APJP) and applicants' interactional justice perception (AIJP). In addition, this study examines whether the identified configurations could further enhance applicants' organisational commitment (OC).

Drawing on the justice model of applicants' reactions, the authors conducted a longitudinal survey of 254 newly recruited employees from 36 Chinese companies that utilise AI in their hiring. The authors employed fuzzy-set qualitative comparative analysis (fsQCA) to determine which configurations could improve APJP and AIJP, and the authors used propensity score matching (PSM) to analyse the effects of these configurations on OC.

The fsQCA generates three patterns involving five configurations that could improve APJP and AIJP. For pattern 1, when AI-based recruitment with high interpersonal rule (AI human involvement) aims for applicants' justice perception (AJP) through the combination of high informational rule (AI explainability) and high procedural rule (AI voice), there must be high levels of AI consistency and AI voice to complement AI explainability, and only this pattern of configurations can further enhance OC. In pattern 2, for the combination of high informational rule (AI explainability) and low procedural rule (absent AI voice), AI recruitment with high interpersonal rule (AI human involvement) should focus on AI transparency and AI explainability rather than the implementation of AI voice. In pattern 3, a mere combination of procedural rules could sufficiently improve AIJP.

This study, which involved real applicants, is one of the few empirical studies to explore the mechanisms behind the impact of AI hiring decisions on AJP and OC, and the findings may inform researchers and managers on how to best utilise AI to make hiring decisions.

The purpose of this study is to empirically explore how firms configure centrifugal and centripetal forces in promoting breakthrough innovation (BI), thus improving their strategic performance (SP) in the artificial intelligence (AI) context.

This study applies the centrifugal and centripetal forces model to a survey sample of 285 Chinese AI firms. Fuzzy-set qualitative comparative analysis (fsQCA) and propensity score matching (PSM) are integrated to explore the configurational effects of three centrifugal forces—the autonomy of technical experts, knowledge search and alliance network—and two centripetal forces—strictness of organisational institutions (SOI) and human–human–AI collaboration (HHAC)—on BI, examining whether the configurations that enhance BI can further improve SP.

The results indicate that the strictness of innovation institutions (SII) and strictness of ethical institutions (SEI) are equally important for determining SOI. Three configurations can improve BI when SOI and HHAC are the core conditions; only one of three configurations can further improve SP significantly.

By introducing SOI composed of equally important levels of SII and SEI and HHAC, this research is one of the few empirical studies to explore the mechanisms behind the impact of centrifugal and centripetal forces on BI and SP, which may help researchers and managers address innovation challenges in the AI context.

This paper evaluates the level of the digital economy in Chinese cities based on digital industrialization and industrial digitalization. The research focuses on the effects of spatial mechanism of the urban digital economy on the quality of firms’ exported products.

The authors use the principal component analysis method to evaluate the level of China’s urban digital economy, and spatial metrology to measure the spatial effects of the digital economy on product quality.

The findings suggest that the urban digital economy can expand the quality of firms’ exports. The digital economy has spatial dependence, spatial spillover and spatial heterogeneity on product quality. At the same time, the spatial effect has a significant nonlinear effect and threshold effect. Further decomposition shows that industrial digitalization is the core factor of enterprises’ export products quality, and the micro-mechanism of this impact is mainly manifested in optimization of resource allocation.

The innovation of this paper is reflected explicitly in exploring the quality upgrading of export products from the background of the digital economy, providing a reference for the improvement of China’s export trade competitiveness and the cultivation of a trade power. The authors studied two different mechanisms (specialization division of labor and optimization of resource allocation) to explain the spatial imbalance of export product quality to provide empirical support for enterprises and government departments to formulate quality upgrading policies accurately.

This paper aims to explain the causal complexity between ecosystems of doing business and living standards based on the theoretical model of the ecosystem of doing business proposed by Li (2019) and Du et al. (2020). By integrating ecological theory, transaction cost theory and institutional logics theory, this study explored effective ecosystems of doing business that achieve high living standards and explained the interpretive mechanisms behind different ecosystems of doing business. Moreover, this study also analyzed whether there were any necessary elements that lead to high living standards and discussed how the interactions between these elements influence carrying capacity and transaction costs from government logic and market logic, thus affecting living standards.

In this study, fuzzy set qualitative comparative analysis (fsQCA) and necessary condition analysis (NCA) were combined to analyze the data from the 2020 China City Statistical Yearbook, covering the main socioeconomic statistical data of cities at all levels in 2019.

This study found that no individual factor of the ecosystems of doing business was necessary to achieve high living standards, but the high level of human capital, innovation capacity, financial access and market demand play a significant role in achieving high living standards. Furthermore, two effective types of ecosystems of doing business lead to high living standards, namely, market dominance (government’s “invisible hand” or “nudging hand”) and government–market logic mutualism/symbiosis (government’s “helping hand”).

First, this work found that individual elements were not a necessary condition for high living standards, not only in kind but also in degree, complementing fsQCA with NCA, which indicates that environmental elements can be substituted by others. Second, this study considered the complex effects and explained the mechanisms behind different ecosystems of doing business, drawing on ecological theory, transaction cost theory and institutional logics theory from a configurational perspective. This study deepened the theories’ applications in the field of living standards and further discussed the elements interactions. Third, this study introduced configurational perspective and QCA into living standards research and adopted a mixed method that combines fsQCA and NCA to analyze the causal complexity between ecosystems of doing business and people’s living standards.