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Reverse logistics services are designed to move goods from their point of consumption to an endpoint to capture value or properly dispose of products and materials. Artificial intelligence (AI)-based reverse logistics will help Micro, Small, and medium Enterprises (MSMEs) adequately recycle and reuse the materials in the firms. This research aims to measure the adoption of AI-based reverse logistics to improve circular economy (CE) performance.

In this study, we proposed ten hypotheses using the theory of natural resource-based view and technology, organizational and environmental framework. Data are collected from 363 Indian MSMEs as they are the backbone of the Indian economy, and there is a need for digital transformation in MSMEs. A structural equation modeling approach is applied to analyze and test the hypothesis.

Nine of the ten proposed hypotheses were accepted, and one was rejected. The results revealed that the relative advantage (RA), trust (TR), top management support (TMS), environmental regulations, industry dynamism (ID), compatibility, technology readiness and government support (GS) positively relate to AI-based reverse logistics adoption. AI-based reverse logistics indicated a positive relationship with CE performance. For mediation analysis, the results revealed that RA, TR, TMS and technological readiness are complementary mediation. Still, GS, ID, organizational flexibility, environmental uncertainty and technical capability have no mediation.

The study contributed to the CE performance and AI-based reverse logistics literature. The study will help managers understand the importance of AI-based reverse logistics for improving the performance of the CE in MSMEs. This study will help firms reduce their carbon footprint and achieve sustainable development goals.

Few studies focused on CE performance, but none measured the adoption of AI-based reverse logistics to enhance MSMEs’ CE performance.

The research paper examines the complex relationship between gamification, student engagement and academic performance in educational environments. The study employed a structural equation model that highlights important connections among key constructs within the educational setting.

This research aims to explore the connection between gamification, student engagement and academic performance in educational settings. The study employs various statistical techniques such as factor analysis, Kaiser–Meyer–Olkin (KMO), Bartlett’s test, component transformation matrix, correlation and regression analysis, descriptive statistics, ANOVA, coefficients and coefficient correlations, residual statistics and confirmatory factor analysis (CFA) to analyze the data.

It was found that active participation by the instructor and good time management skills have a positive impact on student engagement levels (β = 0.380, p < 0.001; β = 0.433 and p < 0.001). However, peer interaction does not significantly predict student engagement (β = −0.068 and p = 0.352). Additionally, there is a positive correlation between student engagement and performance (β = 0.280 and p < 0.001).

The study highlights the importance of innovative design to fully utilize gamification. Future research should consider design, user characteristics and educational context. The findings can guide informed decisions about gamification in education, fostering motivation and learning objectives.

The study presents a reliable tool for assessing student engagement and performance in educational settings, demonstrating high Cronbach’s alpha and robust reliability. It identifies student engagement and time management as significant predictors of Global Learning Outcome. The findings can inform decisions on implementing gamification in educational settings, promoting intrinsic motivation and aligning with learning objectives.

The research highlights the transformative impact of gamification on educational practices, highlighting its potential to enhance student experiences, motivate, promote diversity and improve long-term academic performance, highlighting the trend of integrating technology into education.

In today’s ever-changing education landscape, it is essential to incorporate innovative techniques to keep students engaged and enthusiastic about learning. Gamification is one such approach that has become increasingly popular. It is a concept that takes inspiration from the immersive world of games to enhance the overall learning experience.

The study aimed to understand the significance of how employee personhood and the act of speaking up is shaped by factors such as employees' professional status, length of employment within their hospital sites, age, gender and their ongoing exposure to unprofessional behaviours.

Responses to a survey by 4,851 staff across seven sites within a hospital network in Australia were analysed to interrogate whether speaking up by hospital employees is influenced by employees' symbolic capital and situated subjecthood (SS). The authors utilised a Bourdieusian lens to interrogate the relationship between the symbolic capital afforded to employees as a function of their professional, personal and psycho-social resources and their self-reported capacity to speak up.

The findings indicate that employee speaking up behaviours appear to be influenced profoundly by whether they feel empowered or disempowered by ongoing and pre-existing personal and interpersonal factors such as their functional roles, work-based peer and supervisory support and ongoing exposure to discriminatory behaviours.

The findings from this interdisciplinary study provide empirical insights around why culture change interventions within healthcare organisations may be successful in certain contexts for certain staff groups and fail within others.

The appalling abuse healthcare workers have endured from patients is long documented in the popular press and social media. Less explored in the healthcare management literature is workplace abuse that professional nurses experience from their coworkers.

The authors use text-based first-hand accounts from nurses posting on Reddit (N = 75) to better understand the types and context of abusive acts endured by their coworkers in the contemporary healthcare setting. Each account is content analyzed using two raters, and thematic analysis is utilized to summarize findings.

Findings indicate that nurse workplace abuse frequently targets new entrants to a work unit (e.g. recent grads), typically is ongoing, takes verbal and nonverbal forms, mainly stems from coworkers (i.e. lateral mistreatment), and frequently takes place in front of other coworkers, mainly in hospital settings.

By applying the lens of mindfulness, healthcare organizations can transform these harmful interactions within the nursing profession. The authors offer administrators and frontline workers practical implications for mitigating workplace abuse, including reshaping the culture, bystander interventions and explicit leadership support.

First-hand accounts from nurses in the frontlines of healthcare provide a rich voice that reveals the reality of ongoing verbal and nonverbal peer abuse in hospitals and healthcare settings.

The study attempts to estimate farm subsidies the governments can save by transitioning to a millet-based production system, replacing GHG emission-intensive crops.

It updates a 131 × 131 commodity input–output (IO) table of the year 2015–16 into 2021–22 using the RAS procedure and simulates the economy-wide impacts of replacing rice and wheat with pearl millet and sorghum using consumption and production approaches. It then quantifies fertilizer, electricity and credit subsidy expenses the government can save through this intervention. It also estimates the potential reduction in GHG emissions that the transition could bring about. India is taken as a case.

Results show pearl millet expansion brings greater benefits to the government. It is estimated that when households return to their pearl millet consumption rates that prevailed in the early-reform period, this could save the Indian government Rs. 622 crores (USD 75 m). The savings shall be reinvested in agriculture to finance climate adaptation/mitigation efforts, contributing to a sustainable food system. Net GHG emissions also decline by 3.3–3.6 MMT CO2e.

Indian government has been actively aiming to bring down paddy areas since 2013–14 through the Crop Diversification Program and promoting millets (and pulses and oilseeds) on these farms. The prime reason is to check rapidly declining groundwater irrigation in Green Revolution states. Regulations in the past in these states have not brought the intended results. Meanwhile, electricity and fertilizers are heavily subsidized for agriculture. A slight shift in the cropping system can help conserve these resources. Meanwhile, GHG emissions could also be brought down and subsidies could well be saved. The results of the study indicate the same.

A less warm society is what governments and nongovernment organizations across the world are aiming for at present. Financial implications affect actions against climate change to a greater extent, apart from technological innovations. The effects of policy strategies discussed in the study, taking a large country as a case, when implemented appropriately around the regions, could help move a step closer to action against climate change.

The paper addresses a key but rarely explored research issue – that how a climate-sensitive crop choice will help reduce the government’s fiscal burden to finance climate adaption/mitigation. It also offers a mechanism to estimate the benefits within an economy-wide framework.

Purpose — To develop methodologies to evaluate search engines according to an individual's preference in an easy and reliable manner, and to formulate user-oriented metrics to compare freshness and duplication in search results.

Design/methodology/approach — A personalised evaluation model for comparing search engines is designed as a hierarchy of weighted parameters. These commonly found search engine features and performance measures are given quantitative and qualitative ratings by an individual user. Furthermore, three performance measurement metrics are formulated and presented as histograms for visual inspection. A methodology is introduced to quantitatively compare and recognise the different histogram patterns within the context of search engine performance.

Findings — Precision and recall are the fundamental measures used in many search engine evaluations due to their simplicity, fairness and reliability. Most recent evaluation models are user oriented and focus on relevance issues. Identifiable statistical patterns are found in performance measures of search engines.

Research limitations/implications — The specific parameters used in the evaluation model could be further refined. A larger scale user study would confirm the validity and usefulness of the model. The three performance measures presented give a reasonably informative overview of the characteristics of a search engine. However, additional performance parameters and their resulting statistical patterns would make the methodology more valuable to the users.

Practical implications — The easy-to-use personalised search engine evaluation model can be tailored to an individual's preference and needs simply by changing the weights and modifying the features considered. A user is able to get an idea of the characteristics of a search engine quickly using the quantitative measure of histogram patterns that represent the search performance metrics introduced.

Originality/value — The presented work is considered original as one of the first search engine evaluation models that can be personalised. This enables a Web searcher to choose an appropriate search engine for his/her needs and hence finding the right information in the shortest time with the least effort.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

This study aims to identify the key drivers of consumer engagement in e-commerce among young consumers at bottom-of-pyramid (BoP) markets and their impact on continued usage intention.

A cross-sectional research design was used to understand low-income customers’ engagement in e-commerce, specifically online shopping. The data for this study were collected from BoP customers in the Indian market. A conceptual model was proposed, and hypotheses were developed using the stimulus–organism–response (S-O-R) framework. For analysis, structural equation modeling was performed using AMOS 20.0 software to test the structural model.

The results of the study highlight that perceived importance, technology and infrastructure and social influence are key drivers of e-commerce at BoP customers. Key drivers have shown a significant positive impact on customer engagement which leads to continue usage intention of e-commerce. Furthermore, customer engagement has shown a strong relationship with continue usage intention of e-commerce.

This study indicates that young consumers’ engagement is important for e-commerce service providers to gain a market share. BoP markets offer immense opportunities to create, develop and sustain e-commerce firms for a long time, especially in India. Managers should recognize the potential of BoP markets, which can generate a huge demand for products and services on e-commerce platforms.

This study contributes both theoretically and empirically. Theoretically, this adds to the existing knowledge of customer engagement, especially in e-commerce and BoP market segment. Empirically, it tested the conceptual research model of low-income customer engagement in the e-commerce marketplace using the S-O-R framework. The study recommended practical implications for e-retailers/e-commerce service providers engaging BoP customers in a digitally connected and intensively competitive era.

In a volatile agricultural postharvest market, producers require more personalized information about market dynamics for informed decisions on the marketed surplus. However, this adaptive strategy fails to benefit them if the selection of a computational price predictive model to disseminate information on the market outlook is not efficient, and the associated risk of perishability, and storage cost factor are not assumed against the seemingly favourable market behaviour. Consequently, the decision of whether to store or sell at the time of crop harvest is a perennial dilemma to solve. With the intent of addressing this challenge for agricultural producers, the study is focused on designing an agricultural decision support system (ADSS) to suggest a favourable marketing strategy to crop producers.

The present study is guided by an eclectic theoretical perspective from supply chain literature that included agency theory, transaction cost theory, organizational information processing theory and opportunity cost theory in revenue risk management. The paper models a structured iterative algorithmic framework that leverages the forecasting capacity of different time series and machine learning models, considering the effect of influencing factors on agricultural price movement for better forecasting predictability against market variability or dynamics. It also attempts to formulate an integrated risk management framework for effective sales planning decisions that factors in the associated costs of storage, rental and physical loss until the surplus is held for expected returns.

Empirical demonstration of the model was simulated on the dynamic markets of tomatoes, onions and potatoes in a north Indian region. The study results endorse that farmer-centric post-harvest information intelligence assists crop producers in the strategic sales planning of their produce, and also vigorously promotes that the effectiveness of decision making is contingent upon the selection of the best predictive model for every future market event.

As a policy implication, the proposed ADSS addresses the pressing need for a robust marketing support system for the socio-economic welfare of farming communities grappling with distress sales, and low remunerative returns.

Based on the extant literature studied, there is no such study that pays personalized attention to agricultural producers, enabling them to make a profitable sales decision against the volatile post-harvest market scenario. The present research is an attempt to fill that gap with the scope of addressing crop producer's ubiquitous dilemma of whether to sell or store at the time of harvesting. Besides, an eclectic and iterative style of predictive modelling has also a limited implication in the agricultural supply chain based on the literature; however, it is found to be a more efficient practice to function in a dynamic market outlook.

The purpose of this paper is to conduct a structured literature review using advanced bibliometric tools to understand the existing knowledge base, understand the trends in omni-channel (OC) research and identify emerging research topics.

More than 500 articles selected through a keyword combination search from reputed databases of peer-reviewed academic sources from period 2009–19 are analyzed for the purposes of this study. The study first presents an exploratory analysis to determine influential authors, sources and regions, among other key aspects. Second, several network analyses including co-citation and dynamic co-citation network analyses are conducted to identify themes. These allow identifying research clusters and emerging research topics algorithmically. Both centrality and modularity-based clustering are employed. A content analysis of the most influential groups within OC literature for each cluster is included.

The findings of this paper make unique contributions by using advanced tools from network analysis along with the standard bibliometric analysis tools to explore the current status of OC research, identify existing themes and the guidance for potential areas of future research interest in OC.

This research provides a comprehensive view of the range of topics of importance that have been discussed in the literature of OC management. These research trends can serve as a quick guide to researchers and practitioners to improve decision making and also develop strategies.

The paper employs advanced tools for the first time to review the literature of OC retailing. The sophisticated tools include co-citation and dynamic co-citation network analysis.