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Although growing Internet penetration in the hinterlands has attracted agribusinesses to promote digital platforms, farmers are sceptical about using them. The literature discusses agricultural platforms from the theoretical perspective of technological determinism, where the platforms are developed and promoted by firms in a top-down manner to be accepted by farmers. However, this approach results in poorly configured platforms with limited utility for farmers. It is evident from the existing literature that the mere creation of a platform business is not sufficient to guarantee adoption by users. Hence, this study explores how to make the agricultural platform more attractive for farmers.

The present study is based on a discrete choice experiment performed on 126 Indian farmers using agricultural platforms. The data were analysed using the conditional logistic regression method.

The study suggests that farmers expect government and cooperative entities to be also embedded with the platforms. Complementary features such as prompt service, competitive pricing and farm credit were identified as essential attributes. Further, the platforms should enable smallholders to trade farm produce by providing a mechanism for real-time online nudging and bargaining with buyers.

The study is based on the applications of random utility theory. The research has utility for Agtech managers, cooperative institutions and agricultural policymakers.

This is one of the first studies focussing on agricultural platform design from the farmers' perspective. The study implies that incorporating preferred attributes can help practitioners configure platforms to benefit farmers with prospects concerning farm management decisions.

Electric street car (ESC) is a globally popular clean and safe electric transport system for urban agglomeration. India envisions achieving “all-electric transport” by 2030, yet ESC as a modal transport alternative is not distinct in the policy discussion. The emerging market for electric transportation in urban spaces requires a detailed demand study at the service user level to remove behavioural barriers and design integrated energy planning in developing economies. This paper explores the probabilistic uptake intentions of the daily public transport commuters for ESCs over e-buses from the only Indian city with operational ESCs, Kolkata.

Using a random utility model on primary survey data from daily commuters, the authors identify demographic, psychometric and socio-economic factors influencing probabilistic uptake of ESC over e-buses.

It estimates that 38% of the commuters demand ESC over e-buses, given the alternatives' comparative details. Factors like frequent availability and technological upgradation would increase the uptake of ESCs.

The study highlights that even though there are infrastructural challenges in the implementation of ESC, so does any other electric transport system; it is worth considering as a decarbonising transport alternative, given the high up-take intension of the users.

This is the first attempt to study the demand for ESC in developing economies, identifying the factors which may be considered in the sustainable urban transportation policy perspective.

The purpose is to provide decision support for tourists recommending scenic spots and corresponding suggestions for the management of scenic spots.

Based on the Baidu index data generated, this paper analyzes the temporal and spatial characteristics of network attention of 5A scenic spots in Sichuan Province. The online comment data are used to build the assessment model of scenic spots based on network attention, and the comment information of tourists is mined and analyzed through statistical analysis. At the same time, the key attributes of scenic spots from the perspective of network attention are evaluated and analyzed by using the probabilistic linguistic term set. Finally, this paper further constructs a recommendation model based on the key attribute set of scenic spots.

This paper uses different types of tourism network information, integrates multi-types of data and methods, fully excavates the value information of tourism network information, constructs the research framework of “scenic spot assessment + scenic spot recommendation” from the perspective of network attention, analyzes the network attention characteristics of scenic spots, evaluates the performance of scenic spots, and implements scenic spot recommendation.

This paper integrates multi-source data and multidisciplinary theoretical methods to form a scenic spot research framework of “assessment + recommendation” from the perspective of network attention.

This study aims to compare and evaluate the application of commonly used machine learning (ML) algorithms used to develop models for assessing energy efficiency of buildings.

This study foremostly combined building energy efficiency ratings from several data sources and used them to create predictive models using a variety of ML methods. Secondly, to test the hypothesis of ensemble techniques, this study designed a hybrid stacking ensemble approach based on the best performing bagging and boosting ensemble methods generated from its predictive analytics.

Based on performance evaluation metrics scores, the extra trees model was shown to be the best predictive model. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method. Finally, it was discovered that stacking is a superior ensemble approach for analysing building energy efficiency than bagging and boosting.

While the proposed contemporary method of analysis is assumed to be applicable in assessing energy efficiency of buildings within the sector, the unique data transformation used in this study may not, as typical of any data driven model, be transferable to the data from other regions other than the UK.

This study aids in the initial selection of appropriate and high-performing ML algorithms for future analysis. This study also assists building managers, residents, government agencies and other stakeholders in better understanding contributing factors and making better decisions about building energy performance. Furthermore, this study will assist the general public in proactively identifying buildings with high energy demands, potentially lowering energy costs by promoting avoidance behaviour and assisting government agencies in making informed decisions about energy tariffs when this novel model is integrated into an energy monitoring system.

This study fills a gap in the lack of a reason for selecting appropriate ML algorithms for assessing building energy efficiency. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method.

This study aims to conduct the domain mapping of consumer behaviour research in the context of solar energy. The study can help in understanding the intellectual structure, evolution of keywords and key research producers (at the author, institutional and source level) related to the domain of solar energy consumer research.

This study uses R-studios’ bibliometrix package for analysing the bibliographical data collected from the Scopus database. Analysis has been conducted at the descriptive level (summary, author, institution and source) and analytical level (co-citation analysis, co-occurrence analysis, thematic maps and historiography).

This study finds out the most relevant authors, institutions and sources using criteria such as production, citations and H-index. Relevant research clusters have been identified using the clustering of authors, co-citations and keywords. Thematic mapping has identified the basic and motor themes. Historical citation analysis shows the direct linkage of previous studies. Overall, this study reports the most relevant bibliometric indicators in the domain of solar energy consumer research.

Identified patterns can help policymakers, business experts, social marketers and energy conservation organisations to study consumer behaviour.

Thiis bibliometric study can effectively assess sustainable development goals and suggest improved action plans.

This study examined bibliometric analysis in solar energy products (SEPs), recognised varied domains of research work on consumers’ intention to purchase solar household products and mapped them into six groups. This study provides an overview of 40 years of research on consumer behaviour towards SEPs and discusses its findings to identify the research gap.

Drawing on the social presence theory, this study aims to explore how supplier–customer social media interactions influence supplier observers’ trust in the customers and what mechanisms contribute to variation in trust experience.

A total of 36 semi-structured interviews were conducted with Chinese suppliers using WeChat for business-to-business interactions. Data were analyzed in three steps: open coding, axial coding and selective coding.

Findings reveal that varied trust is based not only on the categories of social presence of interaction – whether social presence is embedded in informative interactions – but also on the perceived selectivity in social presence. Observer suppliers who experience selectivity during social and affective interactions create a perception of hidden information and an unhealthy relationship atmosphere, and report a sense of emotional vulnerability, thus eroding cognitive and affective trust.

The findings contribute new understandings to social presence theory by exploring the social presence of interactions in a supplier–supplier–customer triad and offer valuable insights into business-to-business social media literature by adopting a suppliers’ viewpoint to unpack the mechanisms of how social presence of interaction positively and negatively influences suppliers’ trust and behavioral responses.

The authors analyzed the evolution of working from home (WFH) within industries in 12 European countries in the period 2008–2017 and studied its relationship with information and communication technologies (ICT).

The authors used data from the European Union Labour Force Survey (EU-LFS) to document the trends and levels of WFH within industries in 12 European countries. The authors further used the EU-KLEMS database and a difference-in-difference approach to study whether the fall in prices of ICT is associated with a higher share of employees who work from home in industries that depend more on ICT relative to industries that depend less.

The authors show that WFH has increased almost everywhere and that there is significant heterogeneity across industries. The authors provide evidence that the fall in prices of ICT is associated with a higher share of employees who work from home in industries that depend more on ICT relative to industries that depend less. This result also holds within age, gender and occupation groups. While the authors find no significant differences among gender and occupation groups, the positive association between the fall in ICT prices and WFH increases with age.

This paper has two main contributions: First, it reports that WFH has increased in European countries in the period 2008–2017. Second, it provides new explorations about the relationship between ICT and WFH by using the price variation of ICT.

This study aims to explore and ranks the factors that might determine attitudes and intentions toward using autonomous vehicles (AVs).

The “technology acceptance model” (TAM) was extended by assessing the moderating influences of personal-related factors. Data were collected from 378 Vietnamese and analysed using a combination of “partial least squares” and the “adaptive neuro-fuzzy inference system” (ANFIS) technique.

The findings demonstrated the power of TAM in explaining the attitude and intention to use AVs. ANFIS enables ranking the importance of determinants and predicting the outcomes. Perceived ease of use and attitude were the most crucial drivers of attitude and intention to use AVs, respectively. Personal innovativeness negatively moderates the influence of perceived ease of use on attitude. Data privacy concerns moderate positively the impact of perceived usefulness on attitude. The moderating effect of price sensitivity was not supported.

These findings provide insights for policymakers and automobile companies' managers, designers and marketers on driving factors in making decisions to adopt AVs.

The study extends the AVs literature by illustrating the importance of personal-related factors, ranking the determinants of attitude and intention, illustrating the inter-relationships among AVs adoption factors and predicting individuals' attitudes and behaviours towards using AVs.