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This study aims to determine and analyze the predictors of food waste reduction intention and behavior among higher education institutions (HEIs) using an extended version of the theory of planned behavior (TPB).

This study empirically tested an extended TPB model through regression analyses using data obtained through an online survey.

Attitude toward food waste reduction, subjective norms, perceived behavioral control and food waste knowledge predicted intention to reduce food waste. Furthermore, the intention to reduce food waste predicted food waste reduction behavior.

The results imply that extending the TPB by adding food waste knowledge significantly predicted food waste reduction intention and behavior.

The study identified factors that predict food waste reduction behavior and suggested ways to influence Filipino students in HEIs to reduce food waste.

The findings support the inclusion of food waste knowledge to the TPB in predicting food waste reduction intention and behavior among students in HEIs.

Starting from intention–behaviour models and building upon the growing evidence that aggregated internet search query data represent a good proxy of human interests and intentions. The purpose of this study is to demonstrate that the internet search traffic information related to the selected key terms associated with establishing new businesses, reflects well the dynamics of entrepreneurial activity in a country and can be used for predicting entrepreneurial activity at the national level.

Theoretical framework is based on intention–behaviour models and supported by the knowledge spillover theory of entrepreneurship. Monthly data on new business registration from 2018 to 2021 is derived from the open database of the Russian Federal Tax Service. Terms of internet search interest are identified through interviews with the recent founders of new businesses, whereas the internet search query statistics on the identified terms are obtained from Google Trends and Yandex Wordstat.

The results suggest that aggregated data about web searches related to opening a new business in a country is positively correlated with the dynamics of entrepreneurial activity in the country and, as such, may be useful for predicting the level of that activity.

The results may serve as a starting point for a new approach to measure, monitor and predict entrepreneurial activities in a country and can help in better addressing policymaking issues related to entrepreneurship.

To the best of the authors’ knowledge, this study is original in its approach and results. Building on intention–behaviour models, this study outlines, to the best of the authors’ knowledge, the first usage of big data for analysing the intention–behaviour relationship in entrepreneurship. This study also contributes to the ongoing debate about the value of big data for entrepreneurship research by proposing and demonstrating the credibility of internet search query data as a novel source of quality data in analysing and predicting a country’s entrepreneurial activity.

Promoting the adoption of digital payments by the elderly plays an important role in the development of the digital economy. The purpose of this study is to build an extended theory of planned behavior (TPB) model to predict the elderly's intention to pay for digital services under COVID-19 epidemic constraints.

Based on the extended TPB model, 320 qualified participants were recruited on the network. The structural equation model was tested using the SmartPLS3.3 tool, and the moderation effects were tested through SPSS26 and the Process macro.

The results showed that the three dimensions of TPB theory, the basic elements (perceived value and perceived risk), and the external environment (COVID-19 pandemic) were important factors that influence the elderly users' intention to adopt digital payments. Further research found that motivation factors (personal innovativeness, intergenerational support, and social support) can positively moderate these effects.

The results of the study provide a further explanation for understanding the willingness of elderly people to adopt digital payments during the COVID-19 pandemic and bring inspiration to system developers and social managers to reduce the risk of COVID-19 pandemic and increase the share of digital payments for this category.

This paper used the extended TPB theory to construct a fundamental environmental motivation (FEM) framework for understanding the main influencing factors of elderly users' intention to adopt digital payments during the COVID-19 pandemic.

This science mapping analysis aims to discern current, emerging and future trends of pro-environmental behavior and the theory of planned behavior (TPB).

Bibliometric analysis through bibliographic coupling and co-word analysis were used to reveal the progress of this phenomenon. Of the 1,120 documents search in Web of Science (WoS) database, 1,031 were used in this analysis after restricting to journal publications and studies after the year 2000.

The results show that four themes emerged, namely the fundamentals of TPB for pro-environmental behavior, antecedents of pro-environmental behavior, integration of TPB with the norm activation model and value belief theory and studies of pro-environmental behavior in developing countries. Environmental concern, environmental awareness, environmental knowledge and environmental education were the most commonly integrated variables.

This research is unique in the sense that the integration between TPB and other prominent theories of pro-environmental behavior is vital to predict individual pro-environmental behavior and understand the fundamental scientific importance of the domain. The norm activation model has been integrated with TPB in many pro-environmental behaviors. Mainstream media stakeholders should design and implement a plan for strategic communication and awareness campaigns in the community to encourage consumers to engage in many behaviors that lead to environmental sustainability.

This study presents a science mapping approach to uncover crucial knowledge structure related to pro-environmental behavior and the theory of planned behavior.

The study aims to investigate the presence of herding behavior in the stock market of UK with a special emphasis on news sentiment regarding the economy. The authors focus on the news sentiment because in the current digital era, investors take their decision making on the basis of current trends projected by news and media platforms.

For empirical modeling, the authors use machine learning models to investigate the presence of herding behavior in UK stock market for the period starting from 2006 to 2021. The authors use support vector regression, single layer neural network and multilayer neural network models to predict the herding behavior in the stock market of the UK. The authors estimate the herding coefficients using all the models and compare the findings with the linear regression model.

The results show a strong evidence of herding behavior in the stock market of the UK during different time regimes. Furthermore, when the authors incorporate the economic uncertainty news sentiment in the model, the results show a significant improvement. The results of support vector regression, single layer perceptron and multilayer perceptron model show the evidence of herding behavior in UK stock market during global financial crises of 2007–08 and COVID’19 period. In addition, the authors compare the findings with the linear regression which provides no evidence of herding behavior in all the regimes except COVID’19. The results also provide deep insights for both individual investors and policy makers to construct efficient portfolios and avoid market crashes, respectively.

In the existing literature of herding behavior, news sentiment regarding economic uncertainty has not been used before. However, in the present era this parameter is quite critical in context of market anomalies hence and needs to be investigated. In addition, the literature exhibits varying results about the existence of herding behavior when different methodologies are used. In this context, the use of machine learning models is quite rare in the herding literature. The machine learning models are quite robust and provide accurate results. Therefore, this research study uses three different models, i.e. single layer perceptron model, multilayer perceptron model and support vector regression model to investigate the herding behavior in the stock market of the UK. A comparative analysis is also presented among the results of all the models. The study sheds light on the importance of economic uncertainty news sentiment to predict the herding behavior.

The purpose of this study is to analyze the determinants of intention for energy and water conservation behavior in Prishtina, Kosovo by using the theory of planned behavior (TPB) conceptual framework and then examine the influence of intention and demographic factors on the conservation behavior itself. In addition, the present study examines the differences between urban and rural consumers in Prishtina in terms of their intention for energy and water conservation behaviors and their actual conservation behavior.

This study uses a qualitative approach by conducting ten in-depth interviews followed by one focus group with urban consumers and ten in-depth interviews followed by one focus group with rural consumers in Prishtina to analyze the influence of determinants on the conservation intention. In addition, the present study uses the quantitative research method to empirically examine the influence of intention and demographic variables on the actual conservation behavior.

The findings show that there is a difference between the urban and rural sample populations in Prishtina in terms of determinants that influence their intention to conserve energy and water. While attitude is the strongest determinant among the urban population, the social norms seem to be the strongest antecedent of the behavioral intention among the rural population. In addition, the study finds that the intention, income, family size and place of residence as a whole influence the actual behavior; however, the manifestation of the influence of separate variables on the actual conservation varies between urban and rural population. While intention is very strong among urban respondents and the actual conservation behavior is less dependent on the income level and family size, in the case of rural respondents, intention alone is not sufficient to predict the actual behavior and varies also on the income level.

The study brings unique and new knowledge about the application of the TPB in the context of small and developing economies bridging the research gaps arising from few scholarly research studying the differences between urban and rural populations.

This paper aims to examine whether information technology (IT) managers’ virtual listening, as rated by their high-tech employees, affected turnover behaviour beyond a new constellation of variables, some of which have never been researched as antecedents of turnover behaviour, particularly during a pandemic or crisis. Namely, the main aim, among others, is to answer the research question: does IT employees’ perception of the quality of their supervisors’ virtual listening in the pandemic and crisis era, when employees and managers work remotely, will negatively affect turnover behaviour? If yes, in which constellation of antecedents the virtual listening effecting on turnover behaviour?

Logistic regression analysis was conducted to test the hypotheses via SPSS 26 and PROCESS (Model 6). The variance inflation factor was calculated to test multicollinearity. Interaction was tested using the Hayes and Preacher PROCESS macro model. The researchers also used the J-N technique test (Johnson–Neyman via process). The supplemental analysis used also PROCESS MACRO (IBM Corp., Armonk, NY, USA, 2023) Model 4 and Bootstrap test.

The findings show that perceptions of supervisors’ virtual listening quality as rated by their employees moderated the relationship between organisational deviance as a type of organisational misbehaviour (OMB) and turnover behaviour and had the strongest effect on turnover behaviour beyond other key predictors (organisational deviance as a type of misbehaviour, turnover intention, job satisfaction, embeddedness and alternatives in the labour market). Alternatives to current work moderated the association between the perception of managers’ virtual listening behaviour as rated by their employees and turnover behaviour. Specifically, when alternatives in the labour market were high or medium, the perceived quality of managers’ virtual listening reduced turnover behaviour. Finally, the perception of the IT employees supervisors’ virtual listening moderated the relationship between organisational deviance and turnover intention among high-tech employees.

Evaluating supervisor listening in the high-tech firm may have value in terms of its relationship to outcomes such as retaining employees, turnover intention and especially turnover behaviour. The effect on turnover behaviour and of that new constellation of antecedents on turnover behaviour when people work remotely was not researched yet and important for the post COVID-19 era. Additionally, in contrast to most studies of turnover, this study also focus on the positive aspects of turnover and especially turnover behaviour to organisations in general and especially to high-tech firm and not just the negative aspect as was researched until now. Another contribution is the finding that when employees perceived their managers’ virtual listening quality as high, the effect of deviance as a type of OMB on turnover behaviour was positive. Namely, the listening as a moderator and turnover assisted in making the organisation cleaner from inappropriate behaviour. Additionally, when alternatives in the labour market are high or medium, perceived quality of virtual listening of managers as rated by their employees can reduce turnover behaviour. This virtual listening–turnover relationship and the moderator of alternatives to current work had not previously been found in the turnover literature and this is also significant a contribution to the turnover and withdrawal literature.

The purpose of this study is to empirically examine the cause–effect relationships between workplace friendship and knowledge-sharing behavior, from a static perspective. Furthermore, it investigates the bi-directional relationship between the increase in both workplace friendship and knowledge-sharing behavior over same time periods, and also endeavors to identify whether there is a significant negative lagged effect of the increase in both workplace friendship on knowledge-sharing behavior, and vice versa, across time from a dynamic perspective.

The study conducts a three-wave questionnaire survey to test the research model. A latent change score approach was used to test the direct relationship between changes in workplace friendship and changes in knowledge-sharing behavior.

The findings reveal that knowledge-sharing behavior fosters workplace friendship and workplace friendship promotes the emergence of knowledge-sharing behavior. An increase in workplace friendship promotes an increase in knowledge-sharing behavior over same time periods. However, an increase in workplace friendship will lead to a lagged decrease of knowledge-sharing behavior across time, and vice versa.

The time interval in this study is a little short to capture the full changes in workplace friendship. Some important control factors and mediating mechanisms are not included in the research model.

This study guides managers to focus on various motivators to better strengthen workplace friendship and knowledge-sharing behavior and to consider and effectively respond to the negative side of workplace friendship and knowledge-sharing behavior across time.

This study emphasizes the predictivity of one important interaction patterns, namely, knowledge-sharing behavior on friendship at the workplace, from a static perspective. This study also shows the benefits of an increase in workplace friendship for the development of knowledge-sharing behavior in the same time period. Furthermore, the study presents a counterintuitive finding when taking the lag effect into consideration in exploring the relationship between changes both in workplace friendship and knowledge-sharing behavior, and identifies a negative side of both when viewed over longer periods.

Prior work underscores the important role of customer advocacy for brands. The purpose of this study is to explore the critical role customers can play as brand heroes. The authors developed and validated a measurement scale composed of properties that are derived from distinct brand hero motivational mechanisms.

The authors conducted one exploratory pilot, using semi-structured interviews, with industry and academic experts, and employed three main studies across varying brands and market settings.

This study explores and empirically demonstrates how the brand hero scale (BHS) is related to, yet distinct from, existing scales of opinion leaders, market mavens, attachment and customer advocacy. The six-item BHS demonstrates convergent, discriminant, nomological and predictive validity across several different brand contexts.

This research extends the extant body of work by identifying and defining brand heroes, developing and validating a parsimonious BHS, and demonstrating how its predictive validity extends both to a range of key advocacy and loyalty customer behaviors.

The study provides provocative insights for marketing researchers and brand managers and ascertains the important role heroes may play for brands in terms of strong customer advocacy and loyalty behaviors.

Building on the theory of meaning, this study shows that identifying and working with brand heroes is of great managerial importance and offers critical avenues for future research.

A prime objective of the Swachh Bharat Abhiyan (SBA; Clean India Mission) is to motivate people to segregate their household waste. The purpose of this study is to assess the ground reality of waste management behaviour of Delhi residents with the help of a modified Value–Belief–Norm (VBN) model. Past researches point the need to include cost as a variable in the VBN model. This study fulfils this need and tests cost as one of the variables on the gathered data.

The research data were gathered by interacting with the people and the civic staff in the jurisdiction of the three Delhi municipalities through a stratified sampling technique (N = 250). The structural equation modelling was used to analyse the collected data.

The modified VBN model explains the waste management behaviour, but the variables do not follow the exact causal chain. Values, awareness of consequences, ascription of responsibility and personal norms all explain the resident's waste management behaviour. However, cost limits the resident's waste management behaviour.

The study could only achieve a moderate model fit; its sample size was small; and data were collected through self-reported questionnaire.

Three main practical implications of the study are: (1) While designing waste management solutions, due importance must be given to the cost to be borne by people for adopting these solutions. (2) Design such interventions that target residents' values to convince them to make the desired behavioural change. (3) People need be educated about the ways to sort waste and made aware of the importance of waste segregation in eradicating the urban waste mess.

The paper is an original contribution to testing a modified VBN model in predicting waste management behaviour. The modified model includes cost as a variable missing in the previous research. This research is useful in the backdrop of the SBA and provides suggestions for policymakers and pro-environment researchers.