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The purpose of this paper is to investigate a supply chain consisting of a producer and multiple suppliers of a type of component needed for the production of a certain product. The effects of carbon emission taxes, quality of components and human inspection errors as well as the collaboration among the supply chain members are considered.

A mathematical model is formulated for a non-collaborative supply chain, and the optimal policy is shown to be the solution of a constraint optimization problem. The mathematical model is modified to the case of a collaborative supply chain and to account for inspection errors. Algorithms are provided, and a numerical example is given to illustrate the determination of the optimal policy.

This study offers a new conceptual and analytical model that analyzes the production problem from a supply chain perspective. Human resource management practices and environmental aspects were incorporated into the model to reduce risk, optimally select the suppliers and properly maximize profit by accounting for human inspection error as well carbon emission taxes. Algorithms describing the determination of the optimal policy are provided.

This study provides practical results that can be useful to researchers and managers aiming at designing sustainable supply chains that incorporate economic, environmental and human factors.

This study can be useful to researchers and managers aiming for designing sustainable supply chains that incorporate economic and human factors.

As organizations globalize, they are facing twin challenges of (1) how to develop actionable intelligence from the vast amount of data flowing into their organization and (2) how to effectively manage the increasing risks to their supply chain. Therefore, the purpose of this paper is to bring these two issues on a single platform to understand how firms can effectively predict supply chain risk by developing and using BDA capabilities, through an automated risk alert tool.

The authors used a questionnaire-based survey methodology supported by secondary data to collect information related to managerial perceptions on how firms can develop a risk alert tool by improving BDA capabilities. A database of 213 senior and middle-level managers was developed and used to test the proposed hypothesis. Using econometric techniques, the authors identify the conditions necessary for such an automated risk management tool to be effective.

The results suggest that if organizations focus on developing an effective IT infrastructure supported by a strong BDA capability, they will be able to leverage these capabilities to develop an effective risk management tool. Moderating influences of Upstream and Downstream Supply Chain IT Infrastructure capabilities were also observed on different types of BDA capabilities within a firm. In conclusion, it was argued that the effectiveness of a risk alert tool is dependent on how well firms harness big data analytics capability.

The value of the research stems from the fact that it uses managerial surveys to identify specific BDA capabilities that can enable firms to develop risk resilience capabilities. In addition, the article is one of the few empirical studies that aims to identify how firms can use BDA capabilities within a supply chain context to develop an automated risk alert tool. The article, therefore, contributes to the literature that identifies the value of BDA capabilities within the context of supply chain risk management.

Purpose: The emotions that accompany failure, in and of organizations, and their consequences have been researched in multiple domains of management, but comparative approaches have seldom been attempted. The failure of organizations to survive has been a common occurrence over centuries, particularly in the modern era of start-ups, innovation, and political, economic, and environmental turbulence. With the advent of the COVID-19 pandemic, failure at many levels of society, including the organizational and individual, has increased significantly and produced even more intense emotions. Study Design/Methodology/Approach: For this conceptual chapter, literature from many disciplines was consulted on failure in organizations, and the emotions it elicit, including studies on the process of failure as well as its outcomes. Findings: Failing and failure are likely to evoke negative emotions, with negative consequences for the actor. However, positive emotions can also occur, and a matrix of emotional valence and consequences presents an intriguing set of possibilities. The dimensions of emotions (valence, intensity, duration, and frequency) interact with a wide range of contributing factors (salience, personality, identity, emotional intelligence, emotional regulation, prior experience of failure, and context) in producing the emotions of failure and their consequences. Originality/Value: This chapter contributes to the literature by explicating the types of emotions that emanate during and after failure across many domains of management research, their dimensions and contributing factors, and the consequences for the individual actor. The model of the emotions of failure that is presented here assembles a wider variety of elements than prior research has offered. We indicate avenues for further research as we approach an era of even more demanding challenges.

This chapter aims to make sense of the growing research that examines the role of culture in mergers and acquisitions. We provide a detailed review of the many related but distinct constructs that have been introduced to the literature. While each construct has contributed to our understanding of the role of culture, the lack of connections made among constructs has limited the consolidation of contributions. The review shows what these constructs mean for mergers and acquisitions, what major findings have been discovered, and, most importantly, how constructs interrelate. Our discussion provides several opportunities to foster the needed consolidation of this research.

Climate change-related weather events significantly affect rice production. In this paper, we investigate the impact of and interrelationships between agriculture inputs, climate change factors and financial variables on rice production in India from 1970–2021.

This study is based on the time series analysis; the unit root test has been employed to unveil the integration order. Further, the study used various econometric techniques, including vector autoregression estimates (VAR), cointegration test, autoregressive distributed lag (ARDL) model and diagnostic test for ARDL, fully modified least squares (FMOLS), canonical cointegrating regression (CCR), impulse response functions (IRF) and the variance decomposition method (VDM) to validate the long- and short-term impacts of climate change on rice production in India of the scrutinized variables.

The study's findings revealed that the rice area, precipitation and maximum temperature have a significant and positive impact on rice production in the short run. In the long run, rice area (ß = 1.162), pesticide consumption (ß = 0.089) and domestic credit to private sector (ß = 0.068) have a positive and significant impact on rice production. The results show that minimum temperature and direct institutional credit for agriculture have a significant but negative impact on rice production in the short run. Minimum temperature, pesticide consumption, domestic credit to the private sector and direct institutional credit for agriculture have a negative and significant impact on rice production in the long run.

The present study makes valuable and original contributions to the literature by examining the short- and long-term impacts of climate change on rice production in India over 1970–2021. To the best of the authors’ knowledge, The majority of the studies examined the impact of climate change on rice production with the consideration of only “mean temperature” as one of the climatic variables, while in the present study, the authors have considered both minimum as well as maximum temperature. Furthermore, the authors also considered the financial variables in the model.

The purpose of this paper is to examine how firms can develop business risk resilience from supply chain disruption events, by developing big data analytics (BDA) capabilities within their organization. The authors test whether BDA mediates the impact of institutional response to supply chain disruption events, and information technology infrastructure capabilities (ITICs), on firm’s ability to develop risk resilience from supply chain disruption events.

The study is based on survey data collected from 225 firms, spread across several sectors in the USA and Europe. The respondents are primarily senior and middle management professionals who have experience within the information technology (IT) and supply chain domain. Validity and reliability analyses were performed using SPSS and AMOS; and covariance-based structural equation modeling was used to test the hypothesis.

The analysis reveals two significant findings. First, the authors observe that institutional experience with managing supply chain disruption events has a negative impact on firm’s ability to develop business risk resilience. However, if the organizations adopt BDA capabilities, it enables them to effectively utilize resident firm knowledge and develop supply chain risk resilience capacity. The results further suggest that BDA positively adds to an organization’s existing IT capabilities. The analysis shows that BDA mediates the impact of ITIC on the organization’s ability to develop risk resilience to supply chain disruption events.

This study is one of the few works that empirically validate the important role that BDA capabilities play in enabling firms develop business risk resilience from supply chain disruption events. The study further provides a counterpoint to the existing perspective within the supply chain risk management literature that institutional experience of managing past supply chain disruption events prepares the organization to deal with future disruption events. This paper adds to our understanding of how, by adopting BDA capabilities, firms can develop supply chain risk resilience from disruption events.

Building on the proposition that Bernstein's ideas are due for a revival in higher education research, the call for studies in which theory is put to use and for policy studies to engage in textual analysis, this chapter argues for the affordances of the theoretical underpinnings of Bernstein's pedagogic device and critical discourse studies in investigating connections between policy and practice. Drawing on the sociology of pedagogy and applied linguistics, this chapter aims to explore the theoretical complementarities of the chosen approaches for exploring how policy ideas move through time and space. A focus on the notion of recontextualisation enables an understanding of how influences beyond the discipline itself, including policy discourses, can shape learning, teaching and assessment practices. The illustrating case examines policy on learning and teaching and how these ideas are recontextualised from national policy through to institutional policy and individual practices. The critical or questioning angle of both approaches in seeing ideas, including policy, as never value-free but as situated within their sociopolitical context can shed light on how policy ideas make their way into universities and in whose interests.

This chapter aims to contribute to research on standards and regulation in education, taking as a point of departure three sets of interrelated policies, core to the globalised educational agenda: policies on competencies and skills, school autonomy and performance-based accountability, representing a new governmental logic founded on the values of efficiency, quality, competitiveness and outcomes. The chapter has a double purpose: first, to make a theoretical contribution to the literature interrogating the new modes of governing schools and curricular knowledge. It does this, by explicating the relationship between the regulative dimension of global policy discourses, embodying the principle of performativity, and the discourses regulating pedagogic practices in local sites, where policies are enacted. Second, to present aspects of a study carried out in the Greek education context, in which policies towards a post-bureaucratic administration regime (school autonomy, national testing, accountability mechanisms) have failed to be institutionalised. Focusing on the Modern Greek Language curriculum and its enactments in demanding school settings, the study illustrates how discourses on inclusion, diffused within the educational field and invading the school space, exert strong control over teachers' instructional practices. It is argued that developments of Bernstein's theory of knowledge pedagogisation provide a language to describe the complex ways in which regulative discourses operate in global times, affecting the recontextualisations of curricular policies. The theory thus contributes to the literature on the enactments of globalised education policies and helps explain the diversity of national and institutional responses to such policies.

Purpose: Nowadays, many terms like computer vision, deep learning, and machine learning have all been made possible by recent artificial intelligence (AI) advances. As new types of employment have risen significantly, there has been significant growth in adopting AI technology in enterprises. Despite the anticipated benefits of AI adoption, many businesses are still struggling to make progress. This research article focuses on the influence of elements affecting the acceptance procedure of AI in organisations.

Design/Methodology/Approach: To achieve this objective, propose a hierarchical paradigm for the same by developing an Interpretive Structural Modelling (ISM). This paper reveals the barriers obstructing AI adoption in organisations and reflects the contextual association and interaction amongst those barriers by emerging a categorised model using the ISM approach. In the next step, cross-impact matrix multiplication is applied for classification analysis to find dependent, independent and linkages.

Findings: As India is now focusing on the implementation of AI adoption, therefore, it is essential to identify these barriers to AI to conceptualise it systematically. These findings can play a significant role in identifying essential points that affect AI adoption in organisations. Results show that low regulations are the most critical factor and functional as the root cause and further lack of IT infrastructure is the barrier. These two factors require the most attention by the government of India to improve AI adoption.

Implications: This study may be utilised by organisations, academic institutions, Universities, and research scholars to fill the academic gap and faster implementation of AI.