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We conduct a multidisciplinary systematic literature review on climate neutrality in the supply chain. While carbon neutrality has gained prominence, our study argues that achieving carbon neutrality alone is not enough to address climate change effectively, as non-CO₂ greenhouse gases (GHG) are potent contributors to global warming.

We used multiple databases, including EBSCO, ProQuest, Science Direct, Emerald and Google Scholar, to identify articles related to climate neutrality in the context of non-CO₂ gases. A total of 71 articles in environmental science, climate change, energy systems, agriculture and logistics are reviewed to provide insights into the climate neutrality of supply chains.

We find that, in addition to CO₂, other GHG such as methane, nitrous oxide, ozone and fluorinated gases also significantly contribute to climate change. Our literature review identified several key pillars for achieving net-zero GHG emissions, including end-use efficiency and electrification, clean electricity supply, clean fuel supply, “GHG capture, storage and utilization,” enhanced land sinks, reduced non-CO₂ emissions and improved feed and manure management.

We contribute to the literature on climate neutrality of supply chains by emphasizing the significance of non-CO₂ GHG along with CO₂ and highlighting the need for a comprehensive approach to climate neutrality in addressing climate change. This study advances the understanding of climate neutrality of supply chains and contributes to the discourse on effective climate change mitigation strategies. It provides clear future research directions.

Physical activity (PA) has been widely studied for its individual and societal benefits, but research on how organisations can effectively promote workplace physical activity (WPA) programmes is lacking. This article aims to examine the organisational-level benefits and value drivers of corporate PA programmes whilst also exploring the factors that motivate employees to participate in these programmes.

The authors used the self-determination theory (SDT) and the Hungarian Sport Motivation Scale (H-SMS) – which was modified and validated from the original English SMS scale, which is one of the six most highly cited motivation questionnaires in sports – to assess employees' motivation towards sports. In conjunction, in-depth interviews with company representatives were conducted to create a mind map outlining the impacts and advantages of WPA programmes. The study involved interviews with 13 managers in Hungary and a survey completed by 728 employees.

The findings of the authors' study indicate that WPA programmes had positive effects on employee engagement, turnover reduction and absenteeism. However, the authors' results also revealed that employee motivation to participate in these programmes was primarily driven by external factors, such as identified and introjected regulators, indicating a low level of intrinsic motivation for sports in the workplace. It is important to classify employees based on their motivation and behaviour to design and promote effective PA programmes in the workplace and to increase participation rates.

The authors' findings indicate the importance of aligning WPA programme promotion with employee motivation. This can encourage organisations to initiate their programmes, address participation challenges and strive for higher engagement rates. Further research on employee motivation can support the development of more effective communication strategies for companies in this context.

Studies on sustainable finance examine how it is interrelated with economic, social, governance and environmental issues. Using financial data on publicly traded firms in Indonesia, this study aims to explore the interplay between the cost of capital, firm performance and the COVID-19 pandemic.

This study uses firm-level data sets of publicly listed firms from 2012 to 2021. The regression analysis reported in the study includes the Driscoll–Kraay estimator, propensity score matching model and fixed-effects regression.

The study revealed three significant findings. First, on average, non-environmental, social and governance (ESG) companies’ cost of capital is lower than that of ESG firms. Second, ROE in ESG enterprises is significantly impacted by capital costs. Third, the cost of capital has a negative impact on the market value (Tobin’s q) of non-ESG firms. The study specifically shows that after accounting for the pandemic, ESG firms did not benefit during the troubled COVID-19 crisis after controlling for the pandemic dummy years of 2020 and 2021. These results indicate that the adoption of green or sustainable finance is still in its infancy and that the sector requires more time to establish an enabling environment.

This study benefits from capital structure and ESG theories. It supports the argument that the debt utilization ratio is still relevant to a company’s value because it affects its financial performance. Moreover, adopting ESG principles helps businesses survive crises. Thus, the analysis confirms the superiority of ESG-based firms.

This study draws two conclusions. First, the results could be a reference for academics and practitioners to understand the effect of pandemic-related crises on a firm’s capital structure and performance. In terms of survival during a crisis, such as the COVID-19 pandemic, this study demonstrates how firms with strong ESG may perform differently than those without ESG. Second, this study supports the need for an empirical study and examination of the development of sustainable finance in the country while considering setbacks.

The results should be of interest to policymakers who focus on the ESG market and academics conducting ESG-related research on emerging markets.

This study contributes to the literature by establishing empirical evidence on the relationship between the cost of capital and firm performance of ESG- and non-ESG-rated enterprises in the Indonesian setting while controlling for the impact of the pandemic.

The present work has proposed and employed an innovative hybrid method based on the combination of factor analysis and an artificial neural network (ANN) model to forecast customer satisfaction from the identified dimensions of service quality in India, a developing country.

The qualitative study is conducted with Internet banking users to understand e-banking clients' perceptions. The data is collected with the help of a questionnaire from randomly selected 208 customers in India. Firstly, factor analysis was performed to determine the influential factors of customer satisfaction, and four factors i.e. efficiency, reliability, security and privacy, and issue and problem handling were extracted accordingly. The neural network model is then applied to the factor scores to validate the key elements. Lastly, the comparative analysis of the actual ANN and the regression predicted result is done.

The success ability of the linear regression model is challenged when approximated to nonlinear problems such as customer satisfaction. It is concluded that the ANN model is a better fit than the linear regression model, and it can recognise the complex connections between the exogenous and endogenous variables. The results also show that reliability, security and privacy are the most influencing factors; however, problem handling and efficiency have the slightest effect on bank client satisfaction.

This research is conducted in India, and the sample is chosen from the urban area. The limitation of the purposeful sampling technique and the cross-sectional nature of the data may hamper the generalisation of the results.

The conclusions from the study will be helpful for policymakers, bankers and academicians. To our knowledge, few studies used ANN modelling to predict customer satisfaction in the service 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 aims to propose a research model with eight constructs, i.e. BDA leadership, BDA talent quality, BDA security quality, BDA privacy quality, innovation, financial performance, market performance and customer satisfaction.

The research model focuses on whether (1) Big Data Analytics (BDA) leadership influences BDA talent quality, (2) BDA talent quality influences BDA security quality, (3) BDA talent quality influences BDA privacy quality, (4) BDA talent quality influences Innovation and (5) innovation influences a firm's performance (financial, market and customer satisfaction). An instrument was designed and administered electronically to a diverse set of employees (N = 188) in various organizations in the USA. Collected data were analyzed through a partial least square structural equation modeling.

Results showed that leadership significantly and positively affects BDA talent quality, which, in turn, significantly and positively impacts security quality, privacy quality and innovation. Moreover, innovation significantly and positively impacts firm performance. The theoretical and practical implications of the findings are discussed. Recommendations for future research are provided.

The study provides empirical evidence that leadership significantly and positively impacts BDA talent quality. BDA talent quality, in turn, positively impacts security quality, privacy quality and innovation. This is important, as these are all critical factors for organizations that collect and use big data. Finally, the study demonstrates that innovation significantly and positively impacts financial performance, market performance and customer satisfaction. The originality of the research results makes them a valuable addition to the literature on big data analytics. They provide new insights into the factors that drive organizational success in this rapidly evolving field.

While the three lines model (TLM) provides an organizational structure to execute risk and control duties, research and practice show limitations in the model's implementation. These limitations result in governance issues. Such issues, together with control weaknesses, could be addressed by leveraging properties of distribution, transparency, and immutability of blockchain technology. To this end, in this paper the authors propose a conceptual control framework based on blockchain technology to augment control practice.

The design of the resulting blockchain-based control framework (BBCF) and its prototype, based on the design science research methodology (DSRM), is presented and discussed in terms of the potential impact in the context of the identified problems within the TLM.

One potential outcome of BBCF could be to redefine the scope and boundaries of some of the activities in audit and control practices from a more static to a more dynamic and prospective role. In a larger context of improving governance practices, the BBCF could set the path for a more inclusive and participatory interaction between the different governance actors of an organization.

However, this assumes that blockchain is more widely adopted despite its complexity and rigidity.

BBCF covering both a conceptual model design and a reference implementation provides an innovation in audit and control. BBCF could include all relevant stakeholders who have an interest in corporate governance and control activities, including the regulators.

The contribution intends to serve both as a starting point for discussing the evolution of audit and control practice based on blockchain technology, as well as an initial actionable prototype for experimentation and further development.

Introduction: Skill development is crucial in developing economies by enhancing productivity and creating employment opportunities. At the macro level, it also leads to industrial development and economic growth.

Purpose: The research is to identify the types of skills required for increasing the probability of employability of labour. It also aims to define the challenges and opportunities in skill development to drive change.

Need of the Study: Studying opportunities and challenges for skill development in developing economies is essential for achieving sustainable economic growth, reducing poverty, increasing employment opportunities, and promoting global competitiveness.

Research Methodology: Some skills are recognised through research that has been published to determine the skill set needed to increase labour productivity. To draw lessons, some skill development initiatives by various companies are also identified and presented in case studies. Additionally, several government programs are available to assess the possibilities and prospects for skill development in the Indian market.

Practical Implications: The research will be valuable in micro and macro decision making. At the micro level, research is advantageous for a business person to initiate the skill development of its employees by using government schemes. Nations other than India can understand the policy framework for skill development.

Findings: The term ‘skilling’ has become fashionable. Due to the need for skill-based earnings data, only some studies examine the return on skill (ROS) of the labour market. Skill development plays a significant role in bringing change at the micro and macro levels. Hence it is necessary to exploit all opportunities for skill development.

To study the key determinants of chronically high inflation in Iran.

Relying on annual data from 1978 to 2019, the authors employ an Auto-Regressive Distributed Lag (ARDL) model and Error Correction Model (ECM) to study the inflationary effects of monetary and fiscal policies as well as exchange rate swings and sanctions intensification.

The authors find that increase in money supply, depreciation of nominal exchange rate, increase in fiscal deficit and intensification of sanctions are among the key drivers of inflation in Iran. Their impact is profound in the long run, but in the short run only money supply and currency depreciation are significant. Also, when exploring the inflation in different components of Consumer Price Index (CPI), we find robust long- and short-run effects from money supply and exchange rate, while the effects of fiscal deficit and sanctions vary across different components.

The authors contribute to the literature by setting apart the long-vs short-run effects of key variables on inflation in Iran. The authors also employ improved measures of fiscal deficit and sanctions that are shown to be of significance in the long run. Lastly, the authors go beyond the aggregate index and examine the variations in different CPI components.

We show political divisions in perceptions of police officers even before the divisive political and social events of 2016. We do so using respondents' interpretations of surprising and ambiguous headlines involving police officers (e.g., assumptions about what happened or who was involved).

We use affect control theory's ABO event structure and derivations of this structure to construct a set of headlines that describe ostensibly good people (A) doing bad things (B) to other good people (O) or are ambiguous on one or more of these components. We present 517 MTurk respondents with a set of seven headlines and collect quantitative and qualitative data on their reactions to, and interpretations of, these headline events.

Police headlines generate interest among readers. When interpreting events, respondents are less likely to modify or redefine police officers compared to other actors. However, assumptions related to ambiguous events involving police differ by political orientation. Liberals view police more negatively than conservatives, in part because they imagine them doing worse things to slightly better people. Qualitative analyses support and shed light on the mechanisms underlying this and other partisan effects.

The research was designed to examine interest in headline structure, not specific actors. Thus, the patterns unique to police and political differences were not an original focus. We believe these inductive results are informative, but a study expressly designed to test hypotheses regarding perceptions of events with police officers is recommended for future work.

Understanding the political divide in perceptions of police and the potential of media coverage for exacerbating these effects is essential and related to ACTs growing interest in meaning divergence.