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This study investigates barriers to consumers’ organic food purchasing. It identifies the factors and the extent to which they influence their purchase behaviours and future purchase intentions (i.e. continuance purchase intentions).

It combines qualitative and quantitative methods across two phases. Longitudinal research was carried out in two phases. It involved a thematic analysis and a covariance-based structural equation modelling approach. During Phase-1 and Phase-2, responses were collected from 376 and 351 respondents, respectively.

Phase 1 found the value barrier was significantly affecting the consumers’ purchase intention, while Phase 2 identified the impacts from both image and value barriers on purchase intentions. Notably, purchase intention affected continuance intention in both phases, while ethnocentrism showed no influence.

Using the innovation resistance theory, this study sheds light on the factors that prevent purchase intention. It offers valuable insights for policymakers and for the marketers of organic foods. This contribution implies that value and usage barriers were affecting the consumers’ purchase intentions in the short as well as in the long term. In sum, it suggests that consumers were not purchasing organic food as they felt it was either overpriced, not available in the market or because they were sceptical about its organic labelling.

This research addresses the critical challenge of fostering customer loyalty within the highly competitive landscape of the insurance industry. The study investigates the interplay between customer satisfaction, loyalty, and the influence of technology and service quality in the context of insurance services and in periods of crisis.

A quantitative research approach was employed, utilizing a structured questionnaire distributed among diverse insurance customers in Lebanon during crisis conditions. The data were analyzed using SPSS-Amos, incorporating descriptive statistics, correlation analysis, and structural equation modeling (SEM).

This research emphasizes the crucial role of customer satisfaction in fostering loyalty in the insurance sector, especially during crises. High satisfaction levels, influenced by user-friendly online platforms, positively correlate with increased customer loyalty. Technology plays a vital role in maintaining and improving satisfaction, making it a key driver during challenging times. Positive interactions between service quality and satisfaction further highlight the multifaceted impact of technology on shaping customer loyalty.

The research findings provide valuable insights with practical implications for insurers aiming to boost customer loyalty. The study recommends strategic investments in critical areas like claims processing, customer service, communication strategies, digitalization initiatives, and employee training. The study provides insights applicable particularly to insurance companies navigating crisis conditions.

This research contributes both to academic understanding and practical applications by shedding light on the distinctive challenges and opportunities faced by insurers in cultivating customer loyalty within the insurance industry during crisis. The elucidations provided serve as a foundation for developing targeted strategies to address these challenges and to leverage opportunities for enhanced customer loyalty.

This study aims to provide a framework aligning corporate social responsibility (CSR) initiatives with sustainable development goals (SDGs) 2030, applying the triple bottom line (TBL) approach. The research examines and evaluates the reach of Maharatna Central Public Sector Enterprises’ (CPSE) CSR spending towards sustainability and maps them with SDGs focusing on economic, social and environmental aspects. In addition, state-wise spending for CSR of all eligible Indian companies has been discussed.

The study used secondary data related to CSR spending and disclosure from the annual reports and sustainability reports accessible on the official websites of CPSE, Global Reporting Initiative standards, CSR Guidelines of Department of Public Enterprises and Securities Exchange Board of India, Government of India’s National Guidelines on Responsible Business Conduct (NGRBC) (2018) research papers, financial dailies and websites. The study includes the CPSEs awarded with the status of Maharatna companies under the Guidelines of Maharatna Scheme for CPSEs.

The top CSR initiatives focused on by Maharatna companies were related to poverty, hunger, sanitation and well-being, promotion of education and contribution to the Prime Minister’s National Relief Fund. These initiatives aligned with the top SDGs related to life on land, education and health care, which proved responsible business leadership (RBL) through TBL. The alignment indicates that India is moving towards sustainable development achievements systematically.

The practical consequences can be understood through the CSR spending of Maharatna Public Sector Undertakings towards economic, social and environmental aspects. The spending demonstrates their commitment, which other public and private sector organizations can adopt.

The Government of India’s NGRBC’s guidelines towards inclusive growth and equitable development, addressing environmental concerns, and being responsive to all its stakeholders is a thorough indication of driving the business towards being more responsible. This research has developed a framework aligning CSR and SDG through the TBL approach, which other developing countries can adopt as a model.

There is dearth of research among public sector company’s contribution towards attaining SDGs and demonstrating RBL. This research fulfils this gap. Mapping CSR activities to SDG’s also has not been clearly carried out in previous research, which is a contribution of this study.

This study aims to analyze the factors that drive or prevent interorganizational data sharing in the context of digital transformation (DT). Data sharing appears as a precondition for companies to capture emerging opportunities in supply chain management and for product-related servitization; however, there are ongoing concerns, and data are often perceived as the “new oil.” It is thus important to gain a better understanding of the determinants of firms’ decisions.

The authors develop an embedded case study analysis involving 16 firms within an extended supply network in the automotive industry. The authors focus on the peculiarities of the new context, as opposed to elements highlighted by research prior to the advent of the latest technologies. Abductive reasoning is applied to the theoretical foundations of the resource-based view, resource dependence theory and the complex adaptive systems perspective.

Data sharing is largely underpinned by factors identified prior to DT, such as data specificity, dependence dynamics and protection mechanisms and the dynamism of the business context. DT, however, can influence the extent of data sharing. New factors concern complementarities whenever data are pooled from different sources and digital platforms, as well as different forms of data ownership protection.

This study stresses that data sharing in the context of DT can be explained through established theoretical lenses, providing the integration of elements accounting for new technological opportunities.

This study aims to examine the environmental effects of plastic waste on the atmosphere and its implications for disaster waste management. It focuses on using ammonia, pyrolyzed plastic oil and the effectiveness of alumina nanoparticles as a catalyst.

The research explores different combinations of conventional diesel and nano Al₂O₃ derived from pyrolyzed plastic oil (ranging from P10 to P40). Critical performance metrics evaluated include brake mean effective pressure (BMEP), brake specific fuel consumption, brake thermal efficiency and emissions of CO₂, CO and NOx. The study specifically investigates the impact of adding 50 ppm of Al₂O₃ nanoparticles to these blends.

The findings indicate that using blended fuels with nanoadditives significantly lowers pollution. Specifically, the P30 blend with 50 ppm of Al₂O₃ nanoparticles greatly reduced CO emissions. Additionally, the same blend reduced NOx emissions and CO₂ emissions. The P30 mix showed improved BMEP and brake thermal efficiency due to its density, calorific value and viscosity (6.3 bar). The P30 blend exhibited higher thermal efficiency due to decreased heat loss, whereas conventional diesel demonstrated the best mechanical efficiency due to its longer ignition delay.

This study highlights the potential of using Al₂O₃ nanoparticles and pyrolyzed plastic oil to reduce emissions and enhance the performance of internal combustion engines. It underscores the environmental benefits and implications for disaster waste management by converting plastic waste into useful resources and reducing air pollution.

The timeliness and quality of information provided to board members are crucial for them to effectively monitor and advise a firm. This study examines the influence of board composition and structure on (1) the board’s actions to mitigate the information asymmetry problem by implementing enterprise information systems (EIS) and (2) the board of directors’ awareness of information asymmetry, their perception of its causes and their efforts to address it.

Our research employs a mixed-methods approach. First, using data from 115 publicly listed Chinese companies, we empirically assess the likelihood of top-level EIS modules adoption at the firm level. Subsequently, through 23 semi-structured interviews, we aim to gain deeper insights into the behavioral motivations behind directors’ attempts to reduce information asymmetry.

The study reveals that boards with a higher number of independent directors or with a strategy committee – indicative of a greater concern regarding information asymmetry problems – are more inclined to adopt top-level EIS modules. Additionally, we identify three primary sources of information asymmetry that directors consider significant in prompting the adoption of top-level EIS modules to alleviate perceived information asymmetry.

This study contributes to both the corporate governance and information systems literature. The implementation and utilization of EIS at the board level have not been extensively explored previously. Moreover, while the issue of information asymmetry at the board level is recognized as a critical governance challenge, the ways in which directors perceive and address this issue remain largely unknown. Our research seeks to illuminate this relatively less-explored area.

This paper focuses on image-to-text manuscript processing through Handwritten Text Recognition (HTR), a Machine Learning (ML) approach enabled by Artificial Intelligence (AI). With HTR now achieving high levels of accuracy, we consider its potential impact on our near-future information environment and knowledge of the past.

In undertaking a more constructivist analysis, we identified gaps in the current literature through a Grounded Theory Method (GTM). This guided an iterative process of concept mapping through writing sprints in workshop settings. We identified, explored and confirmed themes through group discussion and a further interrogation of relevant literature, until reaching saturation.

Catalogued as part of our GTM, 120 published texts underpin this paper. We found that HTR facilitates accurate transcription and dataset cleaning, while facilitating access to a variety of historical material. HTR contributes to a virtuous cycle of dataset production and can inform the development of online cataloguing. However, current limitations include dependency on digitisation pipelines, potential archival history omission and entrenchment of bias. We also cite near-future HTR considerations. These include encouraging open access, integrating advanced AI processes and metadata extraction; legal and moral issues surrounding copyright and data ethics; crediting individuals’ transcription contributions and HTR’s environmental costs.

Our research produces a set of best practice recommendations for researchers, data providers and memory institutions, surrounding HTR use. This forms an initial, though not comprehensive, blueprint for directing future HTR research. In pursuing this, the narrative that HTR’s speed and efficiency will simply transform scholarship in archives is deconstructed.

This study proposes a machine learning framework to predict customer complaints from production line tests in an automotive company's lot-release process, enhancing Quality 4.0. It aims to design and implement the framework, compare different machine learning (ML) models and evaluate a non-sampling threshold-moving approach for adjusting prediction capabilities based on product requirements.

This study applies the Cross-Industry Standard Process for Data Mining (CRISP-DM) and four ML models to predict customer complaints from automotive production tests. It employs cost-sensitive and threshold-moving techniques to address data imbalance, with the F1-Score and Matthews correlation coefficient assessing model performance.

The framework effectively predicts customer complaint-related tests. XGBoost outperformed the other models with an F1-Score of 72.4% and a Matthews correlation coefficient of 75%. It improves the lot-release process and cost efficiency over heuristic methods.

The framework has been tested on real-world data and shows promising results in improving lot-release decisions and reducing complaints and costs. It enables companies to adjust predictive models by changing only the threshold, eliminating the need for retraining.

To the best of our knowledge, there is limited literature on using ML to predict customer complaints for the lot-release process in an automotive company. Our proposed framework integrates ML with a non-sampling approach, demonstrating its effectiveness in predicting complaints and reducing costs, fostering Quality 4.0.

This article discusses the underlying teaching framework of relational cultural theory (RCT), as well as additional teaching practices used within a doctoral-level, interdisciplinary social studies course on critical social justice. Areas for future development are identified.

A research-engaged, conceptual report on practice was used to identify and integrate relevant scholarship for the purpose of formulating and analyzing teaching practices for this type of course, and to iteratively identify possible directions for future development.

RCT is a generative, underlying teaching framework for the interdisciplinary social study of critical social justice. Additional teaching practices including a community agreement to guide challenging discussions; participant-led presencing activities at the outset of classes; and, co-creation by participants of the content topics can be fruitfully embedded within RCT. Potential future development could include team-based, community-engaged, experiential term projects aimed at further deepening interdisciplinarity and civic engagement skills.

Practical guidance is provided on the use of RCT, community agreements, co-creation, presencing activities and Indigenous land acknowledgments or contemplations on Indigenous works.

RCT can be used across different educational levels or contexts. Practices of co-creation, presencing and contemplation of Indigenous works are receiving increased consideration in diverse contexts. However, conventional grading procedures can be inconsistent with critical social justice, suggesting the need for research-engaged policy review.

This article responds to recent scholarly calls for discussion of teaching practices in the interdisciplinary, social study of critical social justice in post-secondary education.