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This paper aims to understand the reported awareness and response of Indian corporates to climate change and explores how and why the approaches vary with ownership and industry types. The paper also evaluates whether companies' financial and climate performances are correlated.

The authors collected data from the sustainability/integrated reports, websites and carbon disclosure project (CDP) responses of top-60 companies from the 2019 Fortune India 500 list against 52 indicators designed for the purpose. Descriptive statistics, ANOVA analyses, exploratory factor analysis (EFA) and linear correlations of climate responses with annual revenue and profitability were carried out.

Private sector firms outperform public sector companies, and manufacturing industries surpass services industries. Organizations disclosing to CDP and detailing United Nations Sustainable Development Goals (UN SDGs) and SDG-13 in their reports score significantly higher (at 5%) than their counterparts. The number of positive responses is positively correlated to the revenue and profitability of the companies. EFA indicated the presence of two underlying factors responsible for the observed variances.

Businesses constitute an essential driver of growth in large economies and hence are critical for addressing the issue of climate change. The study underlines the role played by the private sector in mitigating climate change and its impacts.

This is among the first studies that comprehensively evaluate the climate responses of Indian corporates. The current study is also among the foremost studies in the Indian context that identify the underlying factors behind the observed variances and explore correlations between financial and climate performances.

After completion of the case study, students will be able to analyze the marketing mix strategy of a firm, discuss the importance of a generic strategy to gain a competitive advantage, analyze the basis of consumer segmentation in furniture and highlight the importance of positioning in influencing the overall marketing mix strategy of a firm.

It was April 18, 2022. Puneet Singh Seehra (Seehra), the owner and director of Shearling Skins Private Limited (Shearling), was visibly worried as he was looking at the recent sales report. Shearling was in the business of manufacturing premium-quality furniture for corporate clients. Seehra was happy about the growth of his company. However, he was lately concerned about the declining sales figures. Some important questions were troubling Seehra. Was competition eating into his business? How could he differentiate Shearling from competition? What was the right marketing strategy for a market dominated by unorganized competitors and a few major players? His head spinning, he leaned back on his chair as he looked out of his office window.

The case study can be taught in a graduate-level course in marketing or strategy.

Teaching notes are available for educators only.

CSS: 8 Marketing

Data mining along with its varied technologies like numerical mining, textual mining, multimedia mining, web mining, sentiment analysis and big data mining proves itself as an emerging field and manifests itself in the form of different techniques such as information mining; big data mining; big data mining and Internet of Things (IoT); and educational data mining. This paper aims to discuss how these technologies and techniques are used to derive information and, eventually, knowledge from data.

An extensive review of literature on data mining and its allied techniques was carried to ascertain the emerging procedures and techniques in the domain of data mining. Clarivate Analytic’s Web of Science and Sciverse Scopus were explored to discover the extent of literature published on Data Mining and its varied facets. Literature was searched against various keywords such as data mining; information mining; big data; big data and IoT; and educational data mining. Further, the works citing the literature on data mining were also explored to visualize a broad gamut of emerging techniques about this growing field.

The study validates that knowledge discovery in databases has rendered data mining as an emerging field; the data present in these databases paves the way for data mining techniques and analytics. This paper provides a unique view about the usage of data, and logical patterns derived from it, how new procedures, algorithms and mining techniques are being continuously upgraded for their multipurpose use for the betterment of human life and experiences.

The paper highlights different aspects of data mining, its different technological approaches, and how these emerging data technologies are used to derive logical insights from data and make data more meaningful.

The paper tries to highlight the current trends and facets of data mining.

In data grids, replication has been regarded as a crucial optimization strategy. Computing tasks are performed on IoT gateways at the cloud edges to obtain a prompt response. So, investigating the data replication mechanisms in the IoT is necessary. Henceforth, a systematic survey of data replication strategies in IoT techniques is presented in this paper, and some suggestions are offered for the upcoming works. In two key classifications, various parameters dependent on the analysis of the prevalent approaches are considered. The pros and cons associated with chosen strategies have been explored, and the essential problems of them have been presented to boost the future of more effective data replication strategies. We have also discovered gaps in papers and provided solutions for them.

Progress in Information Technology (IT) growth has brought the Internet of Things (IoT) into life to take a vital role in our everyday lifestyles. Big IoT-generated data brings tremendous data processing challenges. One of the most challenging problems is data replication to improve fault-tolerance, reliability, and accessibility. In this way, if the primary data source fails, a replica can be swapped in immediately. There is a significant influence on the IoT created by data replication techniques, but no extensive and systematic research exists in this area. There is still no systematic and full way to address the relevant methods and evaluate them. Hence, in the present investigation, a literature review is indicated on the IoT-based data replication from papers published until 2021. Based on the given guidelines, chosen papers are reviewed. After establishing exclusion and inclusion criteria, an independent systematic search in Google Scholar, ACM, Scopus, Eric, Science Direct, Springer link, Emerald, Global ProQuest, and IEEE for relevant studies has been performed, and 21(6 paper analyzed in section 1 and 15 paper analyzed in section 3) papers have been analyzed.

The results showed that data replication mechanisms in the IoT algorithms outperform other algorithms regarding impressive network utilization, job implementation time, hit ratio, total replication number, and the portion of utilized storage in percentage. Although a few ideas have been suggested that fix different facets of IoT data management, we predict that there is still space for development and more study. Thus, in order to design innovative and more effective methods for future IoT-based structures, we explored open research directions in the domain of efficient data processing.

The present investigation encountered some drawbacks. First of all, only certain papers published in English were included. It is evident that some papers exist on data replication processes in the IoT written in other languages, but they were not included in our research. Next, the current report has only analyzed the mined based on data replication processes and IoT keyword discovery. The methods for data replication in the IoT would not be printed with keywords specified. In this review, the papers presented in national conferences and journals are neglected. In order to achieve the highest ability, this analysis contains papers from major global academic journals.

To appreciate the significance and accuracy of the data often produced by different entities, the article illustrates that data provenance is essential. The results contribute to providing strong suggestions for future IoT studies. To be able to view the data, administrators have to modify novel abilities. The current analysis will deal with the speed of publications and suggest the findings of research and experience as a future path for IoT data replication decision-makers.

In general, the rise in the knowledge degree of scientists, academics, and managers will enhance administrators' positive and consciously behavioral actions in handling IoT environments. We anticipate that the consequences of the present report could lead investigators to produce more efficient data replication methods in IoT regarding the data type and data volume.

This report provides a detailed literature review on data replication strategies relying on IoT. The lack of such papers increases the importance of this paper. Utilizing the responses to the study queries, data replication's primary purpose, current problems, study concepts, and processes in IoT are summarized exclusively. This approach will allow investigators to establish a more reliable IoT technique for data replication in the future. To the best of our understanding, our research is the first to provide a thorough overview and evaluation of the current solutions by categorizing them into static/dynamic replication and distributed replication subcategories. By outlining possible future study paths, we conclude the article.

This paper aims to examine the effect of anthropomorphic entities in the public service advertisements (PSA) on individuals’ pro-social behavior. In addition, the role of individuals’ need for affect and self-construal in moderating the effect of anthropomorphism toward pro-social behavior is explored.

An experimental research design is executed to identify the causal relationship between anthropomorphic representations in the advertisements and pro-social behavior.

The findings suggest that anthropomorphism in PSA is helpful in increasing compliance behavior amongst individuals. Besides, an individual’s need for affect was found to positively moderate pro-social behavior.

The authors extend the existing literature on the usage of anthropomorphism in social causes. The authors also explore the role of one of the intrinsic motivations, need for affect, in pro-social behavior.

The study demonstrates how best one could use anthropomorphization in PSA by sensitizing individuals to social causes and compliance behavior.

The study builds upon the existing research on anthropomorphization, need for affect and pro-social behavior in increasing compliance with PSA.

Purpose: This chapter investigates the current skill gap in small-scale industries, the need for skill development and digital training in micro, small, and medium enterprises (MSME), and reviews policies for skill development and solutions.

Need for the Study: While the legislature and organisations have initiated various considerations for the successful implementation of the Skill Development System in the country’s MSMEs, there are significant challenges that must be addressed quickly to fill the skill gap in workers in this digital era.

Research Methodology: Secondary data has been used for the chapter review. Analysis has been done based on review data from women handloom and handicraft workers in the micro or craft industry who received a Star rating from the National Skill Development Corporation (NSDC) partners in Lucknow. For data collection, a questionnaire based on random sampling was used. The data were analysed using a rudimentary weighted average and a percentage technique.

Findings: The studies provide answers to some fundamental problems: are small industry employees indeed mobilised to be skilled outside the official schooling system? Is the training delivery mechanism adequate to prepare pupils for employment? Would industries be willing to reduce minimum qualification criteria to foster skill development?

Practical Implication: Non-technical aptitudes digital and soft skills for workers in this sector should be emphasised in MSMEs, and significant reforms in MSME sectors and capacity-building education and training programmes should be implemented in the Indian industry to generate small and medium enterprises production and employment.

This research aimed to determine how COVID-19 affected Micro small and medium enterprises’ (MSMEs) participation in sustainable development goals (SDGs) and their adoption of Fin-tech developing nations like India. Involving women in the workforce through microenterprises is crucial to accomplishing the SDGs, and its subsection is focused on decent work and small industries. Data related to knowledge of digital finance was collected using a convenience sample method. Nonetheless, descriptive research was conducted, and although interesting, no definitive findings could be determined. Women entrepreneurs aged between 25 and 35 years provided input into creating this link based on the United Theory of Acceptance and Use of Technology (UTAUT) concept. Using SMART-PLS, we found a significant relationship between the UTAUT model of sustainable development and the level of Fin-tech literacy among women in low-income households in developing countries like India. However, we also know that MSMEs’ adoption of Fin-tech and SDG is less tightly linked to the UTUAT model. The trend towards online transactions or digital payments and the widespread adoption of digital technology applications are changing the face of the global economy. The COVID-19 period saw the rise of contactless transactions as the primary payment method. Because of this shift in public opinion, small- and medium-sized enterprises (SMEs) have begun implementing Fin-tech for long-term growth. People worldwide use it for transactions and other economic functions. However, there is a scarcity of research incorporating this kind of data from India and the rest of the developing world.

Financial and nonfinancial disclosures (sustainable accounting) are crucial in the annual financial reports of many firms. This study aims to explore the dynamic relationship between sustainability disclosure quality (SDQ) and financial performance (FP) within mandatory disclosure frameworks. SDQ is evaluated across six dimensions, encompassing both the quality and quantity of disclosures, aiming to understand their reciprocal influence.

Using the generalized method of moments (GMM), this research analyzes data from 2013 to 2019, focusing on 99 listed Indian firms within the S&P Bombay stock exchange (BSE) 500 index. The study uses rigorous measurement criteria to assess SDQ and uses statistical methods to unveil the causal link between SDQ and FP.

The results show a positive causal connection between SDQ and FP, where organizations with good FP make relatively higher disclosures across FP proxies than their counterparts. Additionally, the study investigates the impact of research and development (R&D) expenditure and dividend payments (DIVD) on SDQ. Notably, lower R&D spending is associated with higher quality SDs, and companies with superior SDQ exhibit increased DIVD.

The findings advocate for strengthened regulatory compliance, incentivized sustainable practices and heightened reporting standards for a transparent business environment and achieving the relevant United Nations Sustainable Development Goals.

This research contributes original insights by uncovering the intricate relationship between SDQ and FP, shedding light on the impact of R&D expenditure and DIVD on SDQ. These findings contribute to a nuanced understanding of the interplay between FP and sustainability reporting within the context of mandatory disclosure frameworks.

Climate change and global warming pose a serious threat to the community as deglaciating environments trigger glacial lake outburst floods (GLOFs). The Sendai framework underpins the role of disaster risk reduction (DRR) for sustainable development, thereby calling for the development of GLOF hazard and risk assessment procedures. This study aims to delineate the enablers of GLOF risk and establish a contextual relationship between them.

The risk enablers are identified from the extant GLOF literature, and the experts from disaster management area are consulted to explore the underlying contextual relationship among them. An interpretive structural model is developed from the responses to analyze the contextual relationships among the risk enablers.

The interpretive structural modeling results manifest that seismic activity, temperature and precipitation events, and proximity are key drivers that trigger flash floods from the lakes and an effective assessment of these triggers may contribute significantly to GLOF risk mitigation.

The study models the complex relationship among the risk enablers for a glacial flood hazard that find commonality across all geographic contexts. The findings provide the practitioners of DRR useful insights into these factors and their subsequent impact on others, while conducting hazard assessment of glacial lakes that may contribute to the sustainable development of settlements lying downstream to glacial lakes.