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One of the most neglected sources of energy loss is streetlights that generate too much light in areas where it is not required. Energy waste has enormous economic and environmental effects. In addition, due to the conventional manual nature of operation, streetlights are frequently seen being turned ‘ON’ during the day and ‘OFF’ in the evening, which is regrettable even in the twenty-first century. These issues require automated streetlight control in order to be resolved. This study aims to develop a novel streetlight controlling method by combining a smart transport monitoring system powered by computer vision technology with a closed circuit television (CCTV) camera that allows the light-emitting diode (LED) streetlight to automatically light up with the appropriate brightness by detecting the presence of pedestrians or vehicles and dimming the streetlight in their absence using semantic image segmentation from the CCTV video streaming. Consequently, our model distinguishes daylight and nighttime, which made it feasible to automate the process of turning the streetlight ‘ON’ and ‘OFF’ to save energy consumption costs. According to the aforementioned approach, geo-location sensor data could be utilised to make more informed streetlight management decisions. To complete the tasks, we consider training the U-net model with ResNet-34 as its backbone. Validity of the models is guaranteed with the use of assessment matrices. The suggested concept is straightforward, economical, energy-efficient, long-lasting and more resilient than conventional alternatives.

This paper aims to conduct a tertiary review to analyse the state of the art of literature reviews on knowledge management (KM) published in academic journals and provide an overview of their evolution. From 2000 to 2022, about 500 reviews have been published in the KM field, with most systematic studies compared to bibliometric or meta-analytic studies, and an absence of previous tertiary studies. Therefore, given the lack of previous tertiary research, this paper provides a complete picture of the evolution of review topics in the past and presents implications for both researchers and practitioners.

A classification scheme was defined to cluster and evaluate the literature reviews, both in terms of methodological approach and content. Regarding the content, the various secondary papers were classified according to the purpose of the research (state of the art, taxonomy, research agenda and research framework), the unit of analysis (small and medium enterprise, large company, start-up and university), the KM models adopted and the thematic areas addressed. Furthermore, a tertiary review methodology was identified integrating two main approaches: a bibliometric approach for cluster identification and a systematic approach for the discussion.

Two categories of contributions emerge from the results: those concerning research topics that have found a continuous interest over time and those that have not yet found a constant research interest. This latter aspect is relevant to help researchers conduct future literature analysis in KM research to bridge existing research gaps.

This paper provides a unique compendium of search directions to offer a comprehensive overview of the scientific debate about KM. This overview can also be used as a managerial panacea to identify best KM practice guidelines from existing reviews.

This is a unique attempt to conduct a tertiary study on KM for more than two decades by providing insights into the structural body of knowledge through academic progress in the subject of KM. Thus, this study expands the field of KM and provides original approaches for research in the field.

As digital procurement continues to transform heavily as a value center and create new business models by linking businesses with a web of external partners, the full path to achieving such an all-encompassing thing is unknown. Thus, the study aims to explore the research gap through an exhaustive bibliometric and systematic literature review on the Digital procurement theme in the supply chain domain.

This study is a qualitative and quantitative analysis of this field, using performance analysis and science mapping to examine 583 articles published from 2002 to 2021.

A systematic literature review indicated core topics on “sustainable or green procurement” and “emerging landscape of technology” in the field of study.

Though the Scopus database used for the analysis is the largest, it may not have complete coverage of all published articles in the field of study; thus, this study is a representation of only a sample rather than its entire population.

Outcome is based on the review of the past 20 years’ contribution on the topic starting from 2002 to 2021.

The growing relevance of environmental sustainability calls for identification of factors that contribute to green innovation and build green corporate reputation. Drawing on the resource-based view theory, this study aims to explore the influence of green logistics knowledge, green customer knowledge, green supplier knowledge, green competitor knowledge, non-supply chain learning on green innovation and green corporate reputation.

This study adopts the quantitative research method where questionnaire is used to gather data from managers of the sampled 208 small and medium enterprises (SMEs). The structural equation modelling is used to analyse the survey data and test the proposed hypotheses.

The findings reveal that non-supply chain learning, green customer knowledge and green competitor knowledge have both direct and indirect impact on green innovation and green corporate reputation. However, green supplier knowledge and green logistics knowledge directly impact green innovation but indirectly impact green corporate reputation through green innovation.

Despite the growing literature exploring the relationship between learning, innovation and reputation, their literature in emerging economies remains underdeveloped. This study provides empirical evidence to confirm the role of non-supply chain learning and green supply chain knowledge in building green corporate reputation and developing green innovation of SMEs in an emerging economy.

The digital direct-to-consumer (D2C) approach has seen widespread adoption across many industries, but its application to regional food products has been slower. This study aims to identify and rank the most significant challenges to the widespread use of digital D2C for regional food products.

A multi-criteria decision-making method called a fuzzy analytic hierarchy process (FAHP) was used to determine the order in which these obstacles were evaluated. Thirty-five barriers were identified and categorized into six categories named technological, operational, sociocultural, financial, market and institutional and regulatory barriers.

Market barriers ranked as the top barrier, and the technological barrier ranked the least significant amongst the main barrier categories for the adoption of digital D2C model for the regional food products. Lack of consumer awareness ranked number one globally, and lack of government subsidies ranked the least amongst the thirty-five identified barriers. Operational barriers came out to be second most significant barrier followed by institutional and regulatory barrier, sociocultural barrier, financial barrier and technological barrier.

The findings of this research were derived through a numerical examination of data gathered from the Indian setting. It follows that the technological, sociocultural, financial, market, operational and institutional constraints, among others, outlined here are all unique to India. Because of the unique nature of the Indian setting, the results of this study can only be used there and not elsewhere. It is possible that future research will broaden the aims of this one and refine its methodology. Digital D2C adoption for regional food products may be prioritized and ranked using quantitative and qualitative data sources like ANP and TOPSIS. It is possible that similar studies may be conducted in nations which have a different set of operational, technological, sociocultural market and financial and regulatory barriers. Conceptual framework can be formed by integrating TAM and TPB to understand the buying behavior of regional food products via digital D2C.

This research is the first to identify challenges to the widespread use of the digital D2C model for regional food products. Policymakers and other interested parties can use this information to better understand the difficulties of expanding the distribution of regional food products beyond their immediate regions.

The purpose of this study is to evaluate food supply chain stakeholders’ intention to use Industry 5.0 (I5.0) drones for cleaner production in food supply chains.

The authors used a quantitative research design and collected data using an online survey administered to a sample of 264 food supply chain stakeholders in Nigeria. The partial least square structural equation model was conducted to assess the research’s hypothesised relationships.

The authors provide empirical evidence to support the contributions of I5.0 drones for cleaner production. The findings showed that food supply chain stakeholders are more concerned with the use of I5.0 drones in specific operations, such as reducing plant diseases, which invariably enhances cleaner production. However, there is less inclination to drone adoption if the aim was pollution reduction, predicting seasonal output and addressing workers’ health and safety challenges. The findings outline the need for awareness to promote the use of drones for addressing workers’ hazard challenges and knowledge transfer on the potentials of I5.0 in emerging economies.

To the best of the authors’ knowledge, this study is the first to address I5.0 drones’ adoption using a sustainability model. The authors contribute to existing literature by extending the sustainability model to identify the contributions of drone use in promoting cleaner production through addressing specific system operations. This study addresses the gap by augmenting a sustainability model, suggesting that technology adoption for sustainability is motivated by curbing challenges categorised as drivers and mediators.

The purpose of this study is to investigate the influence that technological, environmental and organizational factors have on the rate of Internet of Things (IoT) adoption within the logistics industry. In addition, the moderating effect that the risk factor has on the technological, environmental and organizational factors regarding the implementation of IoT in logistics.

For the purpose of testing the models and hypotheses, a survey was carried out in order to collect the responses from currently employed individuals at various companies working in the field of logistics or IoT. For the purpose of analysis, the authors made use of the partial least squares structure equation model (PLS-SEM) technique.

Findings of this study concluded that technology- and environmental-related factors significantly affect the adoption of IoT in logistics, while risk acts as a moderator for the technological-related factor only in the adoption of IoT in logistics.

The relevance of the authors' study lies in the growing importance of IoT in logistics and the need for logistics companies to understand the factors that impact the adoption of IoT in their operations. By identifying and analyzing the factors that influence IoT adoption in logistics, the authors' study provides valuable insights that can help logistics companies make informed decisions about whether and how to adopt IoT.

The research will help organizations make strategies for the successful adoption of IoT and ease the lives of all the stakeholders.

In this research, the authors attempted to find the factors that influence the adoption of IoT in logistics management. The influence of the technological, environmental, organizational and risk-related factors on the adoption of IoT in logistics management was studied. The moderating effect of risk over these factors on the adoption of IoT in logistics was also analyzed. This is original work and has never been done earlier.

Decision-making, reinforced by artificial intelligence (AI), is predicted to become potent tool within the domain of supply chain management. Considering the importance of this subject, the purpose of this study is to explore the triggers and technological inhibitors affecting the adoption of AI. This study also aims to identify three-dimensional triggers, notably those linked to environmental, social, and governance (ESG), as well as technological inhibitors.

Drawing upon a six-step systematic review following the preferred reporting items for systematic reviews and meta analysis (PRISMA) guidelines, a broad range of journal publications was recognized, with a thematic analysis under the lens of the ESG framework, offering a unique perspective on factors triggering and inhibiting AI adoption in the supply chain.

In the environmental dimension, triggers include product waste reduction and greenhouse gas emissions reduction, highlighting the potential of AI in promoting sustainability and environmental responsibility. In the social dimension, triggers encompass product security and quality, as well as social well-being, indicating how AI can contribute to ensuring safe and high-quality products and enhancing societal welfare. In the governance dimension, triggers involve agile and lean practices, cost reduction, sustainable supplier selection, circular economy initiatives, supply chain risk management, knowledge sharing and the synergy between supply and demand. The inhibitors in the technological category present challenges, encompassing the lack of regulations and rules, data security and privacy concerns, responsible and ethical AI considerations, performance and ethical assessment difficulties, poor data quality, group bias and the need to achieve synergy between AI and human decision-makers.

Despite the use of PRISMA guidelines to ensure a comprehensive search and screening process, it is possible that some relevant studies in other databases and industry reports may have been missed. In light of this, the selected studies may not have fully captured the diversity of triggers and technological inhibitors. The extraction of themes from the selected papers is subjective in nature and relies on the interpretation of researchers, which may introduce bias.

The research contributes to the field by conducting a comprehensive analysis of the diverse factors that trigger or inhibit AI adoption, providing valuable insights into their impact. By incorporating the ESG protocol, the study offers a holistic evaluation of the dimensions associated with AI adoption in the supply chain, presenting valuable implications for both industry professionals and researchers. The originality lies in its in-depth examination of the multifaceted aspects of AI adoption, making it a valuable resource for advancing knowledge in this area.

The Internet of Things (IoT), as one of the new digital technologies, has created wide applications in various industries, and one of the most influential industries of this technology is the transportation industry. By integrating the IoT with the transportation industry, there will be dramatic changes in the industry, and it will provide many entrepreneurial opportunities for entrepreneurs to develop new businesses. Opportunity identification is at the heart of the entrepreneurial process, and entrepreneurs identify innovative goods or services to enter a new market by identifying, evaluating, and exploiting opportunities. Despite the desire of transportation managers to invest in the IoT and the increase in research in this area, limited research has focused on IoT-based entrepreneurial opportunities in the transportation industry. Therefore, the present study aims to identify IoT-based entrepreneurial opportunities in the transportation industry and examine their importance.

To achieve the research objective, the authors applied a mixed approach. First, adapting the lens of the industry value chain theory, a comprehensive literature review, besides a qualitative approach including semi-structured interviews with experts and thematic analysis, was conducted to identify the entrepreneurial opportunities. The identified opportunities were confirmed in the second stage using a quantitative survey method, including the Student t-test and factor analysis. Finally, the identified opportunities were weighted and ranked using the best worst method (BWM).

Entrepreneurial opportunities are classified into five main categories, including “smart vehicles”, “business partners/smart transportation supply side”, “supporting services”, “infrastructures”, and “smart transport management and control”. The infrastructures group of opportunities ranked the highest amongst the identified groups.

This study adds to the digital entrepreneurship opportunity recognition literature by addressing opportunities in a smart industry propelled by digital technologies, including developing new products or new applications of the available technologies. Additionally, inspired by the industry value chain theory, this article develops a framework including various digital entrepreneurial opportunity networks which are necessary to add value to any industry and, thus, could be applied by entrepreneurs to recognize opportunities for new intermediaries to enter other digital-based industries. Finally, the present study identifies the IoT-based entrepreneurial opportunities in the smart transportation industry and prioritizes them, providing practical insights regarding the creation of entrepreneurial ecosystems in the field of smart transportation for entrepreneurs and policymakers.

The purpose of this research is to propose and empirically validate a theoretical framework to investigate the willingness of the elderly to disclose personal health information (PHI) to improve the operational efficiency of AI-integrated caregiver robots.

Drawing upon Privacy Calculus Theory (PCT) and the Technology Acceptance Model (TAM), 274 usable responses were collected through an online survey.

Empirical results reveal that trust, privacy concerns, and social isolation have a direct impact on the willingness to disclose PHI. Perceived ease of use (PEOU), perceived usefulness (PU), social isolation, and recognized benefits significantly influence user trust. Conversely, elderly individuals with pronounced privacy concerns are less inclined to disclose PHI when using AI-enabled caregiver robots.

Given the pressing need for AI-enabled caregiver robots due to the aging population and a decrease in professional human caregivers, understanding factors that influence the elderly's disclosure of PHI can guide design considerations and policymaking.

Considering the increased demand for accurate and comprehensive elder services, this is the first time that information disclosure and AI-enabled caregiver robot technologies have been combined in the field of healthcare management. This study bridges the gap between the necessity for technological improvement in caregiver robots and the importance of transparent operational information by disclosing the elderly's willingness to share PHI.