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This paper is a literature review on use of artificial intelligence (AI) among agricultural value chain (AVC) actors, and it brings out gaps in research in this area and provides directions for future research.

The authors systematically collected literature from several databases covering 25 years (1994–2020). They classified literature based on AVC actors present in different stages of AVC. The literature was analysed using Nvivo 12 (qualitative software) for descriptive and content analysis.

Fifty percent of the reviewed studies were empirical, and 35% were conceptual. The review showed that AI adoption in AVC could increase agriculture income, enhance competitiveness and reduce cost. Among the AVC stages, AI research related to agricultural processing and consumer sector was very low compared to input, production and quality testing. Most AVC actors widely used deep learning algorithm of artificial neural networks in various aspects such as water resource management, yield prediction, price/demand forecasting, energy efficiency, optimalization of fertilizer/pesticide usage, crop planning, personalized advisement and predicting consumer behaviour.

The authors have considered only AI in the AVC, AI use in any other sector and not related to value chain actors were not included in the study.

Earlier studies focussed on AI use in specific areas and actors in the AVC such as inputs, farming, processing, distribution and so on. There were no studies focussed on the entire AVC and the use of AI. This review has filled that literature gap.

How psychological variables especially self-efficacy plays significant role to attain workplace well-being is yet to be explained. The extant literature calls for further research works in the field of sustainability practices to bridge the gap between self-efficacy and workplace well-being. The purpose of this paper is to extend the literature of workplace well-being while scientifically examining the moderating role of sustainability practices.

The study collected data from 527 full-time executives of Indian public and private manufacturing industries. The authors performed moderated regression analysis through a series of hierarchical models to test the hypotheses of the study.

The result indicates positive relationship between self-efficacy and workplace well-being. Furthermore, the result suggests that the relationship between self-efficacy and workplace well-being was stronger among executives with high level of sustainability practices and vice versa.

The cross-sectional sample of executives employed in Indian manufacturing organizations limits the generalizability of the findings.

HR functionaries and senior management may benefit by closely examining their sustainability practices along with their employees perceived ability to address workplace well-being.

The study contributes to extend the literature on self-efficacy and workplace well-being. This research work is one of the first few studies to examine the moderating effect of sustainability practices.

The purpose of this study is to examine the relationship between organisational learning and adaptive performance. Furthermore, the study investigates the moderating role of emotional intelligence in the perspective of organisational learning for addressing adaptive performance of executives employed in manufacturing organisations.

The participants were selected through purposive sampling. The study has used established scales on organisational learning, emotional intelligence and adaptive performance to collect data from the respondents. Data were analysed through structural equation modelling using linear structural model (LISREL 8.72). Moderated regression analysis was carried out through a series of hierarchical models to test the hypotheses. The authors have followed the interaction graphs recommended by Aiken and West (1991) to check the moderating effect of emotional intelligence.

The result of the study indicates a significant relationship between organisational learning and adaptive performance. The significant moderation effect was observed in the interaction graph, wherein it was found that the relationship between organisational learning and adaptive performance was stronger among the executives with high levels of emotional intelligence and weaker for those having low levels of emotional intelligence.

The present study gains significance through highlighting the role of emotional intelligence in the perspective of organisational learning and, thus, offers insights to practitioners for addressing adaptive performance of employees.

With the development of research toward damage detection in structural elements, the use of artificial intelligent methods for crack detection plays a vital role in solving the crack-related problems. The purpose of this paper is to establish a methodology that can detect and analyze crack development in a beam structure subjected to transverse free vibration.

Hybrid intelligent systems have acquired their own distinction as a potential problem-solving methodology adopted by researchers and scientists. It can be applied in many areas like science, technology, business and commerce. There have been the efforts by researchers in the recent past to combine the individual artificial intelligent techniques in parallel to generate optimal solutions for the problems. So it is an innovative effort to develop a strong computationally intelligent hybrid system based on different combinations of available artificial intelligence (AI) techniques.

In the present research, an integration of different AI techniques has been tested for accuracy. Theoretical, numerical and experimental investigations have been carried out using a fix-hinge aluminum beam of specified dimension in the presence and absence of cracks. The paper also gives an insight into the comparison of relative crack locations and crack depths obtained from numerical and experimental results with that of the results of the hybrid intelligent model and found to be in good agreement.

The paper covers the work to verify the accuracy of hybrid controllers in a fix-hinge beam which is very rare to find in the available literature. To overcome the limitations of standalone AI techniques, a hybrid methodology has been adopted. The output results for crack location and crack depth have been compared with experimental results, and the deviation of results is found to be within the satisfactory limit.

This study aims to identify and prioritize various growth-accelerating factors in the Indian automotive industry. It further develops a hierarchical model to examine the mutual interactions between the factors, their dependence and their driving power.

This study first identifies the growth-accelerating factors and then uses the modified total interpretive structural modeling (m-TISM) framework, which is an extended version of TISM. It further uses MICMAC analysis to analyze the mutual interrelation between the identified factors.

This study highlights the interrelation amongst the factors using m-TISM model. A hierarchical model shows the level of autonomous, dependence, linkage and independent factors considering the Indian automotive industry. This study also provides the understanding related to the interdependence of growth-accelerating factors.

The government and practitioners could evaluate the growth-accelerating factors which have higher driving power for implementing efficient policies and strategy formulation. By implementing m-TISM model in the Indian automotive industry, auto manufacturers can become more productive and profitable. Future studies could use other methods such as expert opinion to derive the factors, and further model could be verified using structural equation modeling technique.

This study uses a novel m-TISM framework for the analysis of growth-accelerating factors in the context of the Indian automotive industry. It further provides a detailed theoretical and conceptual understanding relating to the philosophy and establishes an interrelation amongst these under-researched growth-accelerating factors.

This study aims to examine a dual-pathway model that recognizes two distinct (formal and informal) but complementary mechanisms of knowledge exchanges – knowledge sharing and knowledge helping. It also investigates how team members use their limited human and psychosocial capital for prosocial knowledge effectiveness.

A survey-based approach was used to examine the hypotheses of the study. A moderated-mediation model was proposed and tested using bootstrap approach.

Knowledge sharing and knowledge helping were found to be the significant links through which human capital (capability) and psychosocial capital (motivation and efficacy) significantly predict prosocial knowledge effectiveness. Post hoc analysis suggests that human capital through knowledge sharing influences team learning, whereas the psychosocial capital through knowledge helping influences team leadership.

The present study found two distinct but complementary and yet necessary mechanisms of knowledge exchanges to be linked as the important outlay for the human and psychosocial capital to be effective in the prosocial knowledge behaviours.

The purpose of this paper is to examine the elements of asymmetric motives, i.e., initial cross-border joint venture (CBJV) conditions and relative partner characteristics in emerging nations. The two main objectives of the present research are to identify the elements affecting asymmetric motives in Indian bilateral CBJV and to construct modified total interpretive structural modelling (TISM) for the identified elements of asymmetric motives.

For the current study, the qualitative technique named total interpretive structural modelling was used. The TISM (Sushil, 2012) is a novel extension of interpretive structural modelling (ISM) where ISM helps to understand the “what” and “how” of research (Warfield, 1974) and TISM answers the third question, i.e., “why” in the form of TISM; further checks for the correctness of TISM are given in Sushil (2016). TISM provides a hierarchical model of the elements selected for study and the interpretation of each element by iterative process and also a digraph that systematically depicts the relationship among various elements. TISM is an innovative modelling technique used by researchers in varied fields (Srivastava and Sushil, 2013; Wasuja et al., 2012; Nasim, 2011; Prasad and Suri, 2011). Steps involved in TISM are shown in Figure 1. It uses reachability matrix and partitioning of elements similar to ISM. Also, along with traditional TISM, the modified TISM process was also used where both paired comparisons and transitivity checks were done simultaneously which helped in minimising the redundant comparisons being made in the original process. Furthermore, for identifying the elements of study, SDC Platinum database was used, which was taken from research papers of major journals namely British Journal of Management, Administrative Science Quarterly, Strategic Management Journal, Management Science, Academy of Management Journal and Organization Science (Schilling, 2009). The database included all joint ventures that were formed in India, having India as one of the partner firms during fiscal year April 2000 and March 2010. From these, 361 CBJVs and 76 domestic joint ventures were identified. Although 54 CBJVs were excluded from these, a total number of 307 CBJVs were studied in the current research. Among these 307 CBJVs, 201 were from super-advanced nations (G7), 40 CBJVs from developing nations and 66 CBJVs from other developed nations. As 65 per cent of the CBJVs came from G7 nations (France, Italy, Japan, Canada, Germany, USA and UK), in the current study, we tried to examine Indian CBJVs with G7 partners only for a period of ten years as mentioned above.

The results of the study indicate that asymmetric motives are directly affected by critical activity alignment and interdependency. Thus, we can conclude that critical activity alignment of partners in CBJV is an antecedent of CBJV motive and thereby minimises the number of asymmetric motives. Bottom level variables such as culture difference and relative capital structure are considered as strong drivers of asymmetric motives. Diversification, resource heterogeneity and inter-partner conflict are middle level elements. Effect of these elements on asymmetric motives can only be improved and enhanced when improvement in bottom level variables is found. It has been believed that as the relative capital structure among firm increases, CBJVs’ asymmetric motives also increase, the reason being that as the difference in capital structure occurs, gradual change in bargaining power will also occur.

TISM used in the present study provides valuable insights into the interrelationship between identified elements through a systematic framework. The methodology of TISM used has its implications for researchers, academicians as well for practitioners. Further study also examines driver-dependent relationship among elements of interest, i.e., relative partner characteristics and initial CBJV conditions by using MICMAC analysis, which can be viewed as a significant step in research related to bilateral CBJV.

The purpose of this paper is to apply systems modeling to explore the usability of the online learning platform in the future compared to its usefulness during the pandemic era.

The applied systems research methodology has been used to develop a stock-flow model encompassing enablers and constraints for learning platform usage from the primary data collected through a survey of 163 respondents.

The model simulation observed promising trends over one year for online learning platforms provided the challenges are reduced in seven to eight months. Challenges linked to the Internet and interaction need must be removed for future usage.

The results of the survey and model simulation suggest actions for product planning and development of online learning platforms based on customer insights. Product customization and feature enhancement will be required for the continued usability of online learning products. Actions for Internet service providers are to capture the online learner market by removing issues of Internet access bandwidth, and quality of content. Also, there should be sufficient teacher–student interaction in the online learning mode.

This is an original study using systems modeling to evaluate factors contributing to students' intention to use online learning conducted at Dayalbagh Educational Institute (Deemed to be University) Dayalbagh Agra, UP, India, 282005.

Performance management systems (PMS) are integral to an organization's human resource management but research is ambivalent on their positive impact and the mechanism through which they influence employee behavior. This study fills this gap by positing work engagement as a mediator in the relationship between perceptions of PMS effectiveness, employee job satisfaction and turnover intentions.

The study uses a survey-based design. Data were collected from 322 employees in India attending a management development program at a premier business school. Partial least squares–based structure equation modeling package ADANCO was used for data analysis.

Positive perception of PMS effectiveness was found to enhance employee work engagement. This increased job satisfaction and reduced turnover intentions among employees. Thus, work engagement mediated the relationship between PMS perceptions and job satisfaction and turnover intentions.

The results suggest that organizations need to focus on three characteristics of PMS, namely its distinctiveness, consistency and consensus. These characteristics determine the effectiveness of PMS in engaging employees and influencing their job satisfaction and turnover intentions.

Prior studies on performance management have largely been limited to aspects of justice and focused disproportionately on the appraisal aspect of performance management. This study takes a systems view of performance management and addresses prior shortcomings by examining the role of clarity and horizontal fit between PMS practices in determining employee engagement. The study also provides much needed empirical support to theoretical studies which have argued that PMS is a driver of engagement in organizations (Gruman and Saks, 2011; Mone and London, 2014).