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Farmers in India are hesitant to adopt organic farming (OF) despite high demand for organic products and favorable policy measures to encourage the practice. Therefore, this study aims to assess the OF adoption barriers faced by Indian farmers using a systematic method of multi-criteria decision making (MCDM).

The authors explored eighteen barriers to OF adoption by conducting a literature survey and discussion with experts on OF. Then the authors used a combined method of Grey Decision Making Trial and Evaluation Laboratory (DEMATEL) and Interpretive Structural Modeling (ISM) methodology to rank the barriers and analyze their interactions.

The analysis reveals that “Lack of knowledge and information,” “lack of financial capacity of farmers’ and “lack of institutional support” are the cause (independent) barriers that significantly impact other barriers. The top three effect (dependent) barriers are “lack of availability of organic inputs,” “personal characteristics such as age, attitudes and beliefs” and “lack of premium pricing,” which are affected by the other barriers.

This research work will help the decision makers understand the barriers to OF adoption in India and their interrelationships. The proposed framework enables them to focus on the high-priority independent barriers, which will subsequently impact the other dependent barriers.

Previous research on OF adoption barriers lacked a multifaceted scientific approach, which is necessary because OF is a complex system and needs a thorough investigation to assess the interaction between the barriers. The research attempts to fill this gap and addresses the complex nature of adoption barriers.

This study aims to measure the integrated impact of big data analytics and artificial intelligence (BDA-AI) adoption by using the ChatGPT generative AI online platform as a BDA-AI tool on the operational and environmental performance.

This study considers Taiwanese professionals who engage with ChatGPT; the sample consists of 388 online users.

This study’s main finding is that the considered antecedents – including technological, organizational and environmental contexts, tangible resources and workforce skills – are significantly associated with BDA-AI adoption. Notably, BDA-AI adoption exhibits a significant relationship with operational performance, environmental performance and environmental process integration. Moreover, environmental process integration is significantly correlated with environmental performance. Lastly, operational performance is significantly correlated with environmental performance.

This study contributes to the heavily lacking but developing literature on the antecedents and consequences of BDA-AI adoption. Its theoretical foundation consists of the technological-organizational-environmental model, Roger’s diffusion of innovation theory and resource-based view theory.

The Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that user-generated content can be efficiently utilised for business intelligence using data science and develops an approach to demonstrate the methods and benefits of the different techniques.

Using Python Selenium, Beautiful Soup and various text mining approaches in R to access, retrieve and analyse user-generated content, we argue that (1) companies can extract information about the product attributes that matter most to consumers and (2) user-generated reviews enable the use of text mining results in combination with other demographic and statistical information (e.g. ratings) as an efficient input for competitive analysis.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The study makes several contributions to the marketing and management literature, mainly by illustrating the methodological advantages of text mining and accompanying statistical analysis, the different types of distilled information and their use in decision-making.

In recent years, “Corporate Social Responsibility” (CSR) has gained significant traction as a strategic concept embraced by business managers. However, there remains a dearth of comprehensive research exploring the impact of CSR on “Green Entrepreneurial Orientation” (GEO), a firm’s sustainability performance, and their interplay with other influential factors. Therefore, this study aims to explore how CSR, “Policy Awareness” (PA), and “Personal Innovativeness” (PI) influence GEO and “Sustainable Performance” (SP). The research also delves into understanding the mediating role of GEO and how this mediation is moderated by “Green Innovation” (GI).

Drawing from the “natural resource-based view” (NRBV) theory and employing “partial least square structural equation modeling” (PLS-SEM), the study analyzed a sample of 137 Indian manufacturing “micro, small, and medium enterprises” (MSMEs).

The results underscore the positive associations of CSR and PA with both GEO and SP. Additionally, the findings highlight the moderating effects of GI in enhancing firms' sustainability performance. The moderated mediation analysis reveals that CSR significantly contributes to SP by fostering the adoption of GEO.

These study outcomes offer valuable insights for policymakers, managers, and entrepreneurs, emphasizing the importance of crafting effective CSR strategies intertwined with innovativeness to cultivate a conducive green entrepreneurial ecosystem in businesses aligned with sustainable development goals.

There is a paucity of research on the determinants of GEO within the context of manufacturing MSMEs, especially in developing and underdeveloped nations. Furthermore, no previous study has delved into the factors that underlie GEO using a moderated mediation framework, particularly within the Indian manufacturing MSMEs landscape.

The study aims to investigate the firm reaction to external pressures and the subsequent transformation to green innovation (GI) and sustainability, leading to an enhanced circular supply chain performance. The research examined the effect of coercive, normative and mimetic pressures on GI within the Indian tire industry context.

The authors have designed a self-administered questionnaire and received 283 responses. Nonresponse bias analysis was conducted first before proceeding to other assumptions such as constant variance and normality. The confirmatory factor analysis was used to check the data’s integrity and reliability. The PLS-structural equation modeling (SEM) is used to validate the constructs and test the established hypotheses.

External regulatory pressures significantly impact the tire industry’s move towards GI. Normative pressures from societal expectations and industry standards, along with mimetic pressures where companies emulate best practices, both drive the industry toward sustainable initiatives. Further, a clear positive relationship emerges between GI and sustainable supply chain practices (SSCP). This shift emphasizes recycling, reusing and reducing waste and showcases the industry’s commitment to minimizing its environmental impact.

Unlike existing literature that predominantly explores SSCP, the study introduces an integrated framework that intertwines institutional pressures (IP), green innovation (GI), and SSCP to elevate circular supply chain practices (CSCP). The current study highlights the relationships and practical implications within the Indian tire industry.

This study investigates the interaction between organizational strategic factors (Leadership and management support [LMS] and green learning orientation [GLO]) and green innovation performance (GIP), through the lens of resource-based view (RBV) theory. It examines both the direct and indirect impacts of these factors on GIP via green knowledge management (GKM), and explores how green absorptive capacity (GAC) enhances these relationships.

Using Partial least squares structural equation modeling (PLS-SEM) and moderated mediation analysis, we analyzed responses from 419 individuals across 154 manufacturing firms in Pakistan to understand these dynamics.

Results show that LMS and GLO significantly affect GIP, both directly and indirectly, through GKM. Furthermore, GAC intensifies the impact of GLO on GKM and the influence of GKM on GIP, indicating a moderated mediation effect.

Highlighting the importance of LMS, GLO, GKM, and GAC, the study suggests that focusing on these areas can help firms align their strategies with sustainability goals, enhancing their GIP. These insights can guide policymakers in creating supportive strategies for businesses to improve their GAC, facilitating better knowledge adoption and application.

The research contributes to the RBV theory by clarifying the role of strategic organizational factors in enhancing GIP within manufacturing firms, offering a clearer path to achieving sustainability goals.

This study aims to address the critical challenge of implementing lean manufacturing (LM) in emerging economies, where sustainability complexities on the production floor hinder production efficiency and the transition towards a circular economy (CE). Addressing a gap in existing research, the paper introduces a path analysis model to systematically identify, prioritize and overcome LM implementation barriers, aiming to enhance performance through strategic removal.

The authors used a mixed-method approach, combining empirical survey data with literature reviews to pinpoint key LM barriers. Using the grey-based Decision-Making Trial and Evaluation Laboratory (DEMATEL) along with the Network Knowledge (NK) method, they mapped causal relationships and barrier intensities. This formed the basis for developing a path simulation algorithm, integrating heuristic considerations for practical decision-making.

This analysis reveals that the primary barriers to LM adoption is the negative perception and inadequate understanding of lean tools and CE principles. The study provides a strategic framework for managers, offering new insights into barrier prioritization and overcoming strategies to facilitate successful LM adoption.

This research provides a strategic pathway for overcoming LM implementation barriers, empowering managers in emerging economies to enhance sustainability and competitive advantage through LM and CE integration. It emphasizes the significance of structured barrier management in the manufacturing sector.

This research pioneers a systematic exploration of LM implementation barriers in the CE context, making a significant contribution to the literature. It identifies, evaluates barriers and proposes a practical model for overcoming them, enriching sustainable manufacturing practices in emerging markets.

The aims of this study are to analyse the impact of brand-centric relationships (individual and collective) and negative emotions on brand hate felt towards brands used in extreme sports and to assess whether brand hate is a driver of negative consumer behavioural intentions.

This study takes a quantitative approach, using a sample of 300 Spain-based users of snow-sports brands. After validating the measurement scales, the hypotheses were tested through structural equation modelling. Drawing on the cognitive perspective of emotions and the triangular theory of hate, this study posits that individual and collective consumer-brand relationships evoke inward negative emotions and brand hate, thus influencing brand avoidance, brand switching and negative word-of-mouth.

The results showed that collective and individual consumer-brand relationships (negative brand experience, consumer-based brand equity and symbolic incongruence) impact inward negative emotions towards snow-sports brands. Inward negative emotions directly influence brand hate, which in turn affects consumers’ intention to avoid brands, switch brands and engage in negative word-of-mouth.

This research provides novel insights into how individual and collective brand-centric relationships evoke inward negative emotions towards extreme sports’ brands, which in turn increases brand hate, and expands knowledge of how brand hate might increase the consumer’s intentions to avoid the brand, intention to switch brands and intention to engage in negative word-of-mouth.

This study aims to examine the impact of extensible business reporting language (XBRL) technology and its functionality on various aspects of financial reporting and its overall quality.

To conduct this study, data was collected from a variety of professionals, including accountants, auditors, tax advisors and others. A structured research instrument was developed, and the collected data were analysed using structural equation modelling and mediation analysis techniques.

The study’s results showed that XBRL technology and its functionality have a noteworthy impact on different aspects of financial reporting. Moreover, the various aspects of financial reporting positively affect the overall quality of financial reporting.

This study solely relied on the opinions of various professionals regarding the current issue under investigation and did not empirically assess the reporting practices of companies by examining their XBRL-based reports. Additionally, it concentrated solely on financial reporting aspects and did not account for non-financial aspects. The main theoretical contributions of this paper to technology in financial reporting, XBRL and accounting literature are that it sheds light on the influence of the use of technologies in the business reporting process and their influence on various aspects of business reporting, which has only received confined focus from earlier studies so far.

This study’s findings could provide valuable insights to the managerial teams of organizations seeking to digitize their business reporting practices, specifically in areas such as regulatory compliance, integrated reporting and timely dissemination of reports in a sustainable way. Furthermore, it could help these teams reap the benefits of technology for various regulatory compliance matters.

This study could assist business organizations and regulatory authorities in adopting and implementing technology such as XBRL for accounting and business reporting. Furthermore, the study’s findings can aid in enhancing financial reporting practices by considering emerging aspects such as ESG and sustainability aspects.

Blockchain’s potential is so significant that business activities across all industries can be drastically altered. Furthermore, the characteristics of blockchain appear to be well-suited to accounting requirements. However, accounting professionals’ attitude and intention toward blockchain adoption are not clear, particularly in India. Thus, this study aims to investigate and evaluate accountants’ intention to adopt blockchain technology in accounting activities.

This study examined and assessed accountants’ intention to use blockchain in accounting. To effectively measure usage intention, this study extended the unified theory of acceptance and use of technology (UTAUT) model by including context-specific constructs. To empirically test and validate the proposed model, data were collected from “369” professional accountants in India.

The findings revealed that facilitating conditions, performance expectancy and initial trust had a significant impact on adoption. Furthermore, the regulatory framework materially moderated the association between usage intention and its predictors.

These findings provide new empirical evidence about the impact of different predictors of usage intention by extending the UTAUT model. Relevant stakeholders can refer to this pioneering study to increase the adoption of blockchain as an efficient and trustworthy system among professional accountants, particularly in developing countries such as India.