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This study explores the impact of green supply chain integration (GSCI) on enterprise performance (EP) from an organizational capability perspective. Additionally, this study investigated the mediating effect of ambidextrous green innovation (AMGI) and the moderating effect of green legitimacy (GL).

This study followed a five-step systematic review of the literature to ensure the auditability and repeatability of the concept development process: (1) formulation of the question, (2) research area orientation, (3) selection and evaluation of research literature, (4) data analysis and synthesis and (5) reporting and application of results.

This study clarified the concepts and dimensions of four relevant variables and, based on the organizational capability theory (OCT), ambidextrous innovation theory (AIT) and new institutional theory (NIT), explained the interactions among these variables and proposed a conceptual framework. In addition, an agenda for future research has been suggested.

This study provides a new direction for future GSCI research and practice in emerging economies. Enterprises should focus on developing GSCI capabilities to promote its positive impact on enterprise performance through AMGI adoption. Moreover, they must emphasize the acquisition of GL, which provides a certain degree of security, to realize the benefits of AMGI.

Forecasting demand of emergency supplies under major epidemics plays a vital role in improving rescue efficiency. Few studies have combined intuitionistic fuzzy set with grey-Markov method and applied it to the prediction of emergency supplies demand. Therefore, this article aims to establish a novel method for emergency supplies demand forecasting under major epidemics.

Emergency supplies demand is correlated with the number of infected cases in need of relief services. First, a novel method called the Intuitionistic Fuzzy TPGM(1,1)-Markov Method (IFTPGMM) is proposed, and it is utilized for the purpose of forecasting the number of people. Then, the prediction of demand for emergency supplies is calculated using a method based on the safety inventory theory, according to numbers predicted by IFTPGMM. Finally, to demonstrate the effectiveness of the proposed method, a comparative analysis is conducted between IFTPGMM and four other methods.

The results show that IFTPGMM demonstrates superior predictive performance compared to four other methods. The integration of the grey method and intuitionistic fuzzy set has been shown to effectively handle uncertain information and enhance the accuracy of predictions.

The main contribution of this article is to propose a novel method for emergency supplies demand forecasting under major epidemics. The benefits of utilizing the grey method for handling small sample sizes and intuitionistic fuzzy set for handling uncertain information are considered in this proposed method. This method not only enhances existing grey method but also expands the methodologies used for forecasting demand for emergency supplies.

1. An intuitionistic fuzzy TPGM(1,1)-Markov method (IFTPGMM) is proposed.

2. The safety inventory theory is combined with IFTPGMM to construct a prediction method.

3. Asymptomatic infected cases are taken to forecast the demand for emergency supplies.

An intuitionistic fuzzy TPGM(1,1)-Markov method (IFTPGMM) is proposed.

The safety inventory theory is combined with IFTPGMM to construct a prediction method.

Asymptomatic infected cases are taken to forecast the demand for emergency supplies.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.

Although the innovative behaviour of public employees is critical for the creation of public value and meeting of public interests, the authors are uncertain about the role of the human resource (HR) system in affecting individual behaviour as past studies tended to discuss innovation at the organisational level of analysis. Based on corporate human resource management (HRM) literature, the authors draw from the ability-motivation-opportunity (AMO) model to examine the influence of innovation-based HR practices on work-related risk propensity and innovative behaviour and the moderating role of perceived error tolerance of public sector organisations.

Dyadic data were collected from supervisors and their subordinates in various public sector organisations in the UAE. The authors collected valid responses from 100 managers and 200 employees.

This study's findings demonstrate that the HR system in the public sector shapes employees' behaviour at the individual level of analysis, consistent with the corporate HRM literature. The authors reveal that innovation-based HR practices significantly promote employees' innovative work behaviour because they trigger their inclination and disposition to take risks. Furthermore, the authors provide evidence that such risk-taking propensity at work is heightened under the conditions of a high level of error tolerance by the organisational management.

This study's findings point out the importance of implementing innovation-based HR practices, such as recruitment, reward and training, to drive public sector employees' innovative work behaviour as they could galvanise their risk-taking propensity and, subsequently, innovative behaviour. Public sector managers also need to develop an innovation culture tolerant toward employees' mistakes to further foster employees' work innovativeness. Policy wise, this study's findings could be integrated into the national innovation strategy to drive the national growth in the UAE.

This study sheds light on the drivers behind innovative behaviour among public employees, which is a less researched area, especially in a non-Western context.

Non-fungible tokens (NFTs) are gaining popularity as investments and personal indulgences, prompting brands to integrate them into marketing campaigns. Thus, understanding consumer personality traits toward NFTs is essential for success. This study presents a model that explores how social comparison orientation (SCO) influences perceived exclusivity and financial benefits of NFT marketing, subsequently impacting experiential evaluations, willingness to purchase NFTs and brand loyalty.

We conducted two experiments to test our model. Study 1 used a quasi-experiment with 1,053 participants and tested the model using partial least squares–based structural equation modeling. In Study 2, we aimed to investigate the causal influence of SCO on NFT marketing effectiveness. We employed a one-factor experiment (social comparison prime: high SCO vs. control) with 123 participants.

NFT users frequently engage in social comparisons and prefer branded NFTs that offer exclusivity (social value) and financial benefits (economic value). Social and financial superiority derived from NFTs enhances branded NFT experiences, leading to a stronger willingness to purchase NFTs and building brand loyalty. Perceived exclusivity, financial benefits and experiential evaluation mediate the effects of SCO on willingness to purchase NFTs and brand loyalty.

This study explores the effectiveness of NFT marketing through the lens of social comparison theory. In doing so, we examined the relationship between SCO and NFT marketing outcomes, revealed the causal influence of SCO on perceived exclusivity and perceived benefits in NFT marketing and shed light on the serial mediation of value- and experience-related constructs.

Cross-app interactive search has become the new normal, but the characteristics of their tactic transitions are still unclear. This study investigated the transitions of daily search tactics during the cross-app interaction search process.

In total, 204 young participants' impressive cross-app search experiences in real daily situations were collected. The search tactics and tactic transition sequences in their search process were obtained by open coding. Statistical analysis and sequence analysis were used to analyze the frequently applied tactics, the frequency and probability of tactic transitions and the tactic transition sequences representing characteristics of tactic transitions occurring at the beginning, middle and ending phases. 

Creating the search statement (Creat), evaluating search results (EvalR), evaluating an individual item (EvalI) and keeping a record (Rec) were the most frequently applied tactics. The frequency and probability of transitions differed significantly between different tactic types. “Creat? EvalR? EvalI? Rec” is the typical path; Initiate the search in various ways and modifying the search statement were highlighted at the beginning phase; iteratively creating the search statement is highlighted in the middle phase; Moreover, utilization and feedback of information are highlighted at the ending phase. 

The present study shed new light on tactic transitions in the cross-app interactive environment to explore information search behaviour. The findings of this work provide targeted suggestions for optimizing APP query, browsing and monitoring systems.

This study aims to investigate the factors influencing the social responsibility performance of Belt and Road Initiative (BRI) megaprojects. Specifically, it examines the role of isomorphic pressures and the joint influence of perceived benefits and top management support on megaproject social responsibility performance (MSRP).

Drawing from institutional theory, social exchange theory, and top management literature, this study established a conceptual model featuring eleven hypotheses. Subsequently, a questionnaire survey was administered to collect data from 238 actively engaged participants in BRI megaprojects. Structural Equation Modelling was utilised to analyse the data.

The empirical findings indicate that mimetic and coercive pressures positively influence MSRP. Perceived benefits and top management support significantly enhance MSRP. Moreover, perceived benefits and top management support partially mediate the effects of coercive and mimetic pressures. However, when it comes to normative pressures, their impact on MSRP is solely channelled through the support of top management.

This study is one of the early endeavours to explore the factors influencing the social responsibility performance of BRI megaprojects. It sheds light on the interplay between external pressures and internal factors in shaping social responsibility efforts in these projects. These findings are of particular significance for BRI actors and stakeholders, offering guidance for enhancing social responsibility strategies within the context of BRI megaprojects.

As a cornerstone of team performance, learning depends on each member’s mindset. Drawing on implicit theories of intelligence, the purpose of this study is to investigate the relationships among individual members’ mindsets and shared leadership (SL) behaviors in design teams and the mediation role of team growth mindset (TGM) on that relationship.

The authors analyzed survey results based on individuals who participated in an international design competition. To test the hypothesized model, the data was analyzed by using SEM using Mplus 7.

The results indicated that an individual growth mindset (IGM; but not an individual-fixed mindset) has significant and positive direct effects on a team growth mindset and SL behaviors. In addition, a TGM mediates the relationship between an IGM and SL.

The research discusses several theoretical and practical implications for human resource development professionals and scholars to improve understanding of a TGM and its influence on individual mindsets and SL behaviors.

This study delves into how corporations make decisions about influencer marketing. Specifically, it examines the differences between human influencers, who carry the risk of scandals, and virtual influencers, a new and unpredictable realm, regarding their integration with social media platforms.

Using game theory and empirical data, the study explores crucial factors in influencer marketing, including influencer quality, reputation repair costs and the probability of R&D failures.

This study suggests that companies favor human influencers when the risk of scandal is low. However, competing companies switch to virtual influencers at different intervals as this risk increases. The costs, likelihood of scandals and competition intensity all play a role in a company's decision-making regarding technology management. Additionally, a higher chance of R&D failure can motivate a company to invest more in R&D to gain a competitive advantage over rivals that may suffer failures.

This study provides insights into how corporations manage social media influencer marketing in the digital age. It contributes to marketing theory and technology management decisions by offering a fresh perspective on the relationship between corporate reputation and influencer marketing strategy.

This study offers valuable perspectives into a relatively uncharted area of marketing strategy. It employs game theory and empirical analysis to introduce a fresh method of comprehending the dynamics of influencer marketing, its impact on corporate reputation management and its interaction with social media.

For some years now, Cameroon has seen a significant increase in its electricity demand, and this need is bound to grow within the next few years owing to the current economic growth and the ambitious projects underway. Therefore, one of the state's priorities is the mastery of electricity demand. In order to get there, it would be helpful to have reliable forecasting tools. This study proposes a novel version of the discrete grey multivariate convolution model (ODGMC(1,N)).

Specifically, a linear corrective term is added to its structure, parameterisation is done in a way that is consistent to the modelling procedure and the cumulated forecasting function of ODGMC(1,N) is obtained through an iterative technique.

Results show that ODGMC(1,N) is more stable and can extract the relationships between the system's input variables. To demonstrate and validate the superiority of ODGMC(1,N), a practical example drawn from the projection of electricity demand in Cameroon till 2030 is used. The findings reveal that the proposed model has a higher prediction precision, with 1.74% mean absolute percentage error and 132.16 root mean square error.

These interesting results are due to (1) the stability of ODGMC(1,N) resulting from a good adequacy between parameters estimation and their implementation, (2) the addition of a term that takes into account the linear impact of time t on the model's performance and (3) the removal of irrelevant information from input data by wavelet transform filtration. Thus, the suggested ODGMC is a robust predictive and monitoring tool for tracking the evolution of electricity needs.