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Due to the different institutional pressure such as those from market, regulations and competitors, companies have implemented green supply chain management (GSCM). Unfortunately, tens of GSCM practices exist. Whether all companies should implement GSCM and how to achieve both environmental and economic performance are still not clear for many companies. The purpose of this paper is to develop models that can be helpful for companies to identify right GSCM practices and implement GSCM effectively and efficiently.

Based on about 18 years of study on GSCM with four surveys in China in 2001, 2005, 2012 and 2016, as well as numerous site visits and interviews mainly in China but also in Japan, Germany and Canada, this paper explores institutional drivers as well as opportunities and challenges using theoretical analysis and case studies. GSCM is defined considering a product life cycle. A key three-step GSCM approach is theoretically developed considering opportunities and challenges through life cycle analysis (LCA) of a product and position of a company.

All companies should implement GSCM practices to avoid risks. To effectively implement GSCM practices, a company should understand the life cycle of its product and its position in the supply chain. A key three-step LCA-based approach can help companies to identify the critical GSCM practices.

A key three-step LCA-based approach for GSCM implementation is originally developed based on theoretical analysis and eight years of study.

The purpose of the study was to ascertain the influence of innovation conviction, innovation mindset and innovation creed on the performance of women entrepreneurs in South African small and medium enterprises and their capacity for innovation. The study also examined how proactive personality and entrepreneurial education moderate the relationship between innovative capability and women entrepreneurs' performance.

The study used a quantitative research design and administered a questionnaire to collect data from participants. Since there was no sampling frame available, purposive sampling, a non-probability sampling technique, was used to select suitable respondents who were identified as entrepreneurial women. Data were collected from 304 women entrepreneurs in the Gauteng province of South Africa. The data were analyzed using smart partial least squares.

The findings demonstrated that innovation conviction, innovation mindset and innovation creed have a positive impact on innovation capability. It was also discovered that innovation capability, proactive personality and entrepreneurial education all positively and significantly impact women entrepreneurs' performance. Furthermore, the results showed that entrepreneurial education and proactive personality had a positive and significant moderating effect on the nexus between innovation capability and the performance of women entrepreneurs.

This study will add to the body of knowledge on women's small business management and entrepreneurship in Africa, two topics that are typically ignored by academics in developing nations.

This study explores key factors influencing individuals' intentions to invest in NFTs, focusing on personal innovativeness, reward sensitivity, knowledge, subjective norms, perceived value and perceived risk. The aim is to provide insights into what motivates investors within this emerging market, addressing a gap in the understanding of NFT adoption from an investor perspective.

An online survey collected data from 272 participants in China and Hong Kong. The research employs partial least squares-structural equation modeling (PLS-SEM) to assess the relationships between various individual, social and market factors and NFT investment intentions.

The results suggest that personal innovativeness, reward sensitivity, NFT knowledge, subjective norms and perceived value positively impact NFT investment intentions. Additionally, age and income moderate the effects of subjective norms and perceived value on investment intentions, highlighting demographic influences.

For practitioners, insights into investor motivators can inform strategies to promote NFT investments, such as promoting the high reward potential, enhancing investor knowledge, leveraging social proof and emphasizing NFTs' perceived value. For academics, the findings open pathways for further research into investor psychology and the evolving dynamics of NFT and traditional investment markets.

This study advances NFT literature by identifying determinants of NFT investment behavior, a relatively uncharted area. By incorporating theories from investment behavior and technology adoption, it provides a new framework to understand the psychological and social drivers specific to NFT investments.

The purpose of this paper is to lay out the data competence maturity model (DCMM) and discuss how the application of the model can serve as a foundation for a measured and deliberate use of data in secondary education.

Although the model is new, its implications, and its application are derived from key findings and best practices from the software development, data analytics and secondary education performance literature. These principles can guide educators to better manage student and operational outcomes. This work builds and applies the DCMM model to secondary education.

The conceptual model reveals significant opportunities to improve data-driven decision making in schools and local education agencies (LEAs). Moving past the first and second stages of the data competency maturity model should allow educators to better incorporate data into the regular decision-making process.

Moving up the DCMM to better integrate data into their decision-making process has the potential to produce profound improvements for schools and LEAs. Data science is about making better decisions. Understanding the path laid out in the DCMM to helping an organization move to a more mature data-driven decision-making process will help improve both student and operational outcomes.

This paper brings a new concept, the DCMM, to the educational literature and discusses how these principles can be applied to improve decision making by integrating them into their decision-making process and trying to help the organization mature within this framework.

The purpose of this study is to explore the role individual innovativeness along with e-learning self-efficacy play in predicting the e-learning readiness of first- and second-year students of an open and distance education institutions in an African context.

Therefore, building on previous related research in this area, a quantitative approach was adopted to address the research questions and to establish whether a statistically significant relationship existed between individual innovativeness, e-learning self-efficacy, the independent variables; and e-learning readiness, the dependent variable. In total, 476 first- and second-years students of the university participated in the four-Likert-type scale survey. The research instrument which comprises 74 survey items was completed by 217 of the students. Statistical tools used for analysing data included both Pearson Product Moment Correlation coefficients and t-tests.

It was discovered that a strong positive and significant relationship was observed between individual innovativeness and e-learning readiness of first- and second-year students of the Yenagoa Study Centre of the National Open University of Nigeria (NOUN); a statistically significant relationship was also found between e-learning self-efficacy scores and the e-learning readiness of the first- and second-year students of the Yenagoa Study Centre of NOUN; there was a statistically significant joint relationship between the three variables under investigation; findings equally revealed that male respondents had higher e-learning readiness than their female counterparts.

Like every other study of this nature, this one also suffers some limitations. First, NOUN is a very large university with over half a million students spread across almost 78 study centres. This means that observation from just one study centre amounts to a very small sample size. This according to Schweighofer, Weitlaner, Ebner and Rothe (2019) jeopardises the generalisability and validity of study results. The authors also maintain that empirical data generated from surveys that usually rely participants' abilities to read and select responses without further interpretation by the researchers suffer from cognitive biases like social desirability. To address the above limitations, detailed studies involving all studies centres of NOUN be undertaken and other qualitative and or mixed research methodologies be adopted in the future.

The implications for this study are that people who are innately innovative will willingly accept technology and by extension, learning in technology-rich environments like those found in like NOUN whose mode of study is blended learning inherently found in open and distance learning (ODL) institution. Therefore, this study is significant as it will provide relevant information to the management and administrators of NOUN, policymakers and regulatory institutions for the development, deployment and implementation of e-learning strategies. Findings will also benefit e-learning initiatives undertaken by similar institutions that adopt the ODL mode of education in Nigeria and other developing countries.

Even though, studies on the antecedents of e-learning readiness have been widely conducted across diverse contexts, studies exploring the associations between individual innovativeness, e-learning self-efficacy and e-learning readiness are relatively hard to come by. The above two variables as predicting the e-learning readiness in the study context are comparatively new. This study thus focuses on the relationships between the individual innovativeness levels, e-learning self-efficacy beliefs of students and their e-learning readiness which ultimately determines their ability to sustain studies in an ODL institution.

For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

The data economy mainly relies on the surveillance capitalism business model, enabling companies to monetize their data. The surveillance allows for transforming private human experiences into behavioral data that can be harnessed in the marketing sphere. This study aims to focus on investigating the domain of data economy with the methodological lens of quantitative bibliometric analysis of published literature.

The bibliometric analysis seeks to unravel trends and timelines for the emergence of the data economy, its conceptualization, scientific progression and thematic synergy that could predict the future of the field. A total of 591 data between 2008 and June 2021 were used in the analysis with the Biblioshiny app on the web interfaced and VOSviewer version 1.6.16 to analyze data from Web of Science and Scopus.

This study combined findable, accessible, interoperable and reusable (FAIR) data and data economy and contributed to the literature on big data, information discovery and delivery by shedding light on the conceptual, intellectual and social structure of data economy and demonstrating data relevance as a key strategic asset for companies and academia now and in the future.

Findings from this study provide a steppingstone for researchers who may engage in further empirical and longitudinal studies by employing, for example, a quantitative and systematic review approach. In addition, future research could expand the scope of this study beyond FAIR data and data economy to examine aspects such as theories and show a plausible explanation of several phenomena in the emerging field.

The researchers can use the results of this study as a steppingstone for further empirical and longitudinal studies.

This study confirmed the relevance of data to society and revealed some gaps to be undertaken for the future.

This study embraces the call for exploring the determinants of continuous intention in TikTok. Taking the perspective of social influence, this study not only tries to explore the contextual sources of two types of social influence but also aims to unveil the influence mechanism of how social influence affects TikTok viewers’ continuous intention.

This study empirically analyzes how TikToker attractiveness, co-viewer participation, platform reputation and content appeal affect informative and normative social influence and then lead to the continuous intention of TikTok. Based on 547 valid survey data, this study adopts a mixed analytical approach for data analysis by integrating structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA).

SEM results unveil that content appeal is the most critical antecedent of informational social influence, while the TikToker attractiveness and platform reputation have no effect on it. Differently, all four external sources positively lead to normative social influence. Among them, content appeal and co-viewer participation influence the most. The influences of both two types of social influence on continuous intention are demonstrated. FsQCA results reveal seven alternative configurations that are sufficient for influencing continuance intention and further complement and reinforce the SEM findings.

Addressing the critical contextual elements of TikTok, this study explores and confirms the sources which may engender social influence. The authors also demonstrate the critical role of social influence in affecting TikTok viewers’ continuous intentions by the hybrid analytical approach, which contributes to existing academic literature and practitioners.

The purpose of this study is to examine the trend toward purchasing locally grown food and evaluate if tourists visiting Hawai'i are willing to pay more for locally produced foods that are more ecologically sustainable.

A research questionnaire was developed in order to investigate the attitudes and behaviors of tourists from the continental United States visiting Hawai'i in purchasing locally grown food in Hawai'i. The final sample includes 454 valid survey responses collected via Momentive, a market research services company.

According to the findings of this study, there are economic prospects to expand the use of locally cultivated food into the tourists' experience, as well as a willingness for tourists to support these activities financially. The Contingent Valuation study revealed that tourists from the continental United States were ready to pay a higher price to purchase food that is locally grown, signifying that tourists to Hawai'i are willing to aid the local agriculture business by increasing their restaurant/hotel meal bill, which will help Hawai'i become a more sustainable tourist destination.

While tourists from the United States mainland, which is the “an islands” top tourist market, have agreed with paying extra or an additional fee for locally grown food products, this study might not accurately represent the attitudes and behaviors of international tourists visiting Hawai'i. Future research should focus on the international tourist markets which may have different social norms or cultural differences thus could provide a broader spectrum of the current study's findings.

The results of this study provided quantitative evidence that tourists from the United States are interested in purchasing locally grown food items in Hawaii in addition to their willingness to pay an additional fee for these locally grown food products at a restaurant or a hotel dining room, thus addressing a gap in the tourism research.

This study aims to explore how digital platforms (DPs) contribute to value co-creation in municipal solid waste (MSW) management systems.

The present paper conducts an explorative analysis using single case study methodology. The case in question involves a DPs operating in Italy.

Empirical analysis shows that DPs help engage citizens in MSW and reduce the fragmentation in waste management systems by fulfilling a brokerage role that connects citizens, municipalities and waste management companies. The development of bidirectional knowledge and resource flow among actors contributes to better waste recycling processes, as well as fosters economic, environmental and social value co-creation in a complex public service.

This research is limited to a single case study within the Italian context, which may influence the generalizability of the findings. Future research could expand the scope to include multiple case studies across different geographical regions.

For practitioners and policymakers, this paper underscores the strategic benefits of adopting DPs in MSW management systems and thereby improving public service delivery.

The case analysis highlights that DPs can assist public actors in achieving numerous sustainable development goals by enhancing recycling rates and activating learning mechanisms among citizens.

This study contributes to literature by connecting different fields of research (i.e. waste management and public management) and using network theory to show how DPs can contribute to the economic, environmental and social sustainability of MSW while generating relevant benefits for the actors involved.