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This paper aims to propose an innovative approach to risk measurement for the abolition of selection bias arising from the specious selection of different horizons for investment and risk computation of equity-linked-saving schemes (ELSS).

ELSS has a lock-in period of three years, but shorter horizons’ (daily/weekly/monthly) return data are preferred, in practice, for risk computation. This results in horizon mismatch. This paper studies the consequences of this mismatch and provides a noble solution to diminish its effect on investors’ decision-making. To accomplish this objective, the paper uses an innovative methodology, maximal overlap discrete wavelet transformation, to segregate the price movements across different horizons. Risk across all horizons is measured using Cornish-Fisher expected shortfall and Cornish-Fisher value-at-risk methods.

The degree of consistency of risk-based rankings across horizons is examined by means of the Spearman and Kendall’s rank correlation tests. The risk-based ranking of ELSS is found to vary significantly with the change in investor’s horizon. Precisely, the rankings formulated using daily net asset values are significantly different from the rankings developed using fluctuations over longer horizons (two-four and four-eight years).

This finding indicates that the ranking exercise may mislead investors if horizon correction is not done while developing such rankings.

Despite several efforts during the last years, the web model and semantic web technologies have not yet been successfully applied to empower Ubiquitous Computing architectures in order to create knowledge‐rich environments populated by interconnected smart devices. In this paper we point out some problems of these previous initiatives and introduce SoaM (Smart Objects Awareness and Adaptation Model), an architecture for designing and seamlessly deploying web‐powered context‐aware semantic gadgets. Implementation and evaluation details of SoaM are also provided in order to identify future research challenges.

This study aims to describe patients presented in an interprofessional, virtual education program focused on the mental health of adults with intellectual and developmental disabilities (IDD), as well as present interprofessional recommendations for care.

In this retrospective chart review, descriptive statistics were used to describe patients. Content analysis was used to analyze interprofessional recommendations. The authors used the H.E.L.P. (health, environment, lived experience and psychiatric disorder) framework to conceptualize and analyze the interprofessional recommendations.

Themes related to the needs of adults with IDD are presented according to the H.E.L.P. framework. Taking a team-based approach to care, as well as ensuring care provider knowledge of health and social histories, may help better tailor care.

This project draws on knowledge presented in a national interprofessional and intersectoral educational initiative, the first in Canada to focus on this population.

The purpose of this paper is to analyze the Environmental Sustainability Enablers (ESEs) for an Indian steel manufacturing company to select the appropriate set of ESEs for implementing and enhancing environmental sustainability.

A methodology using fuzzy Decision-Making Trail and Evaluation Laboratory (DEMATEL) is proposed to analyze ESEs capturing multiple experts’ qualitative judgments on their mutual impacts. It is applied to an Indian steel manufacturing company to understand the salient features of the concept.

The relevant 18 ESEs were classified into cause and effect group and also interactions (i.e. influencing and influenced) of each ESE with other ESEs were evaluated. It was observed that the five ESEs (i.e. competitors’ environmental sustainability strategy, environmental compliance certification, government regulations and incentives, influence of external factors and air pollution controlling system) are the most prominent in the cause group.

The results obtained are specific to an Indian steel manufacturing company and it cannot be generalized for steel manufacturing sector. However, the current paper can show direction to carry out such work in other manufacturing companies.

The proposed methodology will be helpful for allocating efforts and resources to enhance the impact of ESEs for successful achievement of environmental sustainability.

No such paper has been reported in the literature with an objective to develop structural framework for Indian steel manufacturing company to improve environmental sustainability.

The purpose of this paper is to examine the impact of structural breaks on the conditional variance of carbon emission allowance prices.

The authors employ the symmetric GARCH model, and two asymmetric models, namely the exponential GARCH and the threshold GARCH.

The authors show that the forecast performance of GARCH models improves after accounting for potential structural changes. Importantly, we observe a significant drop in the volatility persistence of emission prices. In addition, the effects of positive and negative shocks on carbon market volatility increase when breaks are taken into account. Overall, the findings reveal that when structural breaks are ignored in the emission price risk, the volatility persistence is overestimated and the news impact is underestimated.

The authors are the first to examine how the conditional variance of carbon emission allowance prices reacts to structural breaks.

This study investigates the relationship between buyer-supplier top management team (TMT) demographic misalignment (defined as differences in TMT composition based on background, age and gender) and environmental performance (EVP).

The empirical setting is publicly held US manufacturing firms that are present in both the Kinder, Lydenberg and Domini’s (KLD's) annual EVP ratings and Bloomberg's supply chain database. The study employs panel data regression methods on an unbalanced panel dataset of 7,493 dyad-year observations comprising 427 unique firms.

The research shows that misalignment in functional background and gender composition between TMTs have a negative outcome on both the buyer's and the suppliers' EVP. However, increasing presence of females across TMTs has a positive influence on EVP. Further, the research shows that misalignment based on age between the TMTs does not impact EVP in any significant way. On the contrary, increasing age across TMTs is a significant predictor of EVP.

This study builds on existing works in TMT heterogeneity and adds context to the heightening belief in the positive linkage between heterogeneity and performance through extension to a boundary spanning interfirm context.

The purpose of this research is to examine the impact of external assurance on the level of voluntary corporate climate change disclosures by Finnish firms.

The sample of this study includes 228 firm-year observations over the period 2008–2015 for listed Finnish companies that have issued sustainability reports and responded to the Carbon Disclosure Project (CDP) questionnaire at least once during the sample period. The authors conduct a panel regression analysis to study the afore-mentioned linkage. In addition, the Tobit regression model is also estimated to check the robustness of our findings.

The findings suggest that assurance has a highly significant positive impact on the level of corporate climate change disclosures even after controlling for the effect of a number of control variables. Moreover, among the control variables, firm size and asset age are found to have significant effect on the extent of carbon emissions disclosure. Furthermore, the additional analysis reveals that the type of assurance providers (accounting firms vs non-accounting firms) and the type of financial auditors (Big4 financial auditors vs non-Big4 financial auditors) do not influence the level of climate change disclosure of assured companies.

This research is subject to certain limitations. First, the source of the data used in this research is the CDP database which has limitations in that it is a voluntary disclosure process where all the observations collected are self-reported by the responding firms. This may bias the reported findings. Second, our sample includes only listed companies and hence the results might have limited explanatory capacity for unlisted firms.

By using the results of this research, corporate managers will be able to reduce the information asymmetry between various stakeholders and them through disclosure of accurate, reliable and credible environmental information. Such disclosures will, in turn, allow socially responsible investors to choose eco-friendly investments and will thus enable them to make appropriate investment decisions.

Research on the external assurance-corporate climate change disclosure nexus is scarce. This study addresses this gap in the nonfinancial disclosure assurance literature by demonstrating that external assurance increases the level of voluntary corporate climate change disclosure. Drawing on stakeholder-agency theory, this study views external assurance as a monitoring structure that potentially curbs the monitoring problem between corporate managers and other stakeholders and increases the amount of climate change disclosures making a possible avenue for the reduction of the information asymmetry between them.

In recent years, determining the effective and suitable supplier in the supply chain management (SCM) has become a key strategic consideration to the success of any manufacturing organization in terms of business intelligence (BI), as many quantitative and qualitative critical factors are measured from big data. In today’s competitive business scenario, the main purpose of this study is to determine suitable and sustainable suppliers during supplier selection process is to reduce the risk of investment along with maximize overall value to the customer and develop closeness and long-term relationships between customers and suppliers to build a resilient SCM to mitigate uncertainty for automotive organizations.

As these types of decisions generally involve more than a few criteria and often necessary to compromise among possibly conflicting factors, the multiple-criteria decision-making becomes a useful approach to solve this kind of problem. Considering both tangible and intangible criteria, the aim of this paper is the presentation of a new integrated fuzzy analytic hierarchy process and fuzzy additive ratio assessment method with fuzzy entropy using linguistic values to solve the supplier selection problem to build the resilient SCM under uncertain data. Fuzzy entropy is used to obtain the entropy weights of the criteria.

Organizations gather massive amounts of information known as BD on the basis of historical records of uncertainties from several internal and external sources to manage uncertainty to improve the overall performance of organizations using BI strategy for analyzing and making effective decision to support the managements of automotive manufacturing organizations in an information system.

Although this study tries to represent a full analysis on suitable and resilient global supplier selection under various types of uncertainty, still there are some improvements that can be made in the future by developing a more refined and more sophisticated approach to further enhance the performance of the proposed scheme to calculate overall rating scores of the alternatives.

The novelty of this paper is to propose a framework of BI in SCM to determine a suitable and resilient global supplier where all the meaningful information, relevant knowledge and visualization retrieved by analyzing the huge and complex set of data or data streams, i.e. BD based on decision-making, to develop any manufacturing organizational performance worldwide.

Environment, social, governance (ESG) has taken on increased importance in real estate investment in recent years, with benchmarking ESG being critically important for more informed real estate investment decision-making. Using 60 stakeholder interviews with senior real estate executives, this paper examines the strategic issues regarding benchmarking ESG in real estate investment; specifically, identifying areas going forward where ESG benchmarks need to be improved. This includes the issues of granularity, climate resilience and climate risk, as well as an increased focus on outcomes and performance, and using best practice procedures in delivering ESG in real estate investment.

In total, 60 stakeholder interviews were conducted with key real estate players globally to assess the use of ESG benchmarking in real estate investment at various levels (asset/fund-level, listed real estate, delivery, reporting and internal benchmarking), across regions and across different types of real estate investment players (real estate fund manager, real estate investment trust (REIT), institutional investor and real estate advisor). This enabled key strategic insights to be identified for improved ESG benchmarking practices in real estate investment going forward.

There was clear evidence of the need for improved benchmarks for ESG in real estate investment. More focus was needed on performance, outcomes and impacts, with a stronger focus on granularity around the issues of climate resilience and climate risk. Improvements in Global Real Estate Sustainability Benchmark (GRESB), as well as increased attention to Task Force for Climate-Related Financial Disclosures (TCFD) were seen as important initiatives. Clear differences were also seen in the use of these ESG benchmarks on a regional basis; with Australia and Europe seen as the world leaders. These strategic stakeholder insights regarding ESG saw the development of best practice guidelines for the more effective delivery of ESG benchmarks for more informed real estate investment decision-making, as well as a series of recommendations for improving ESG benchmarking in real estate investment.

ESG benchmarking is a critical area of real estate investment decision-making today. By utilising stakeholder interviews, the strategic insights from key players in the real estate investment space are identified. In particular, this paper identifies how the current ESG benchmarks used in real estate investment need to be improved for a more critical assessment of climate resilience and climate risk issues at a more granular level. This enables the identification and delivery of more effective ESG best practice procedures and recommendations for improving ESG benchmarking in real estate investment going forward. These issues have clear impacts on ongoing capital raisings by investors, where benchmarking ESG is an increasingly important factor for real estate investors, tenants and real estate asset managers.

Based on the stakeholder interview responses, this paper has identified key areas for improvement in the current benchmarks for ESG in real estate investment. It is anticipated that an increased focus on technology and the availability of more granular data, coupled with user demand, will see more focus on assessing performance, outcomes and impacts at a real estate asset-specific level and produce a fuller range of ESG metrics, more focused on climate resilience and climate risk. This will see a more effective range of ESG benchmarks for more informed real estate investment decision-making.

The purpose of this paper is to study the influence of factors such as financial literacy on a consumer’s investment decisions, particularly in the stock market. Based on two empirical studies, the theory of planned behaviour (TPB) was used to understand stock market participation (SMP) in India while developing a model to represent the relationships between the various factors. Consumer financial literacy was conceptualised to be a part of perceived behavioural control and included in the TPB.

A mixed methods research was followed where qualitative research preceded a quantitative survey-based study. In-depth interviews were conducted with investors and experts, results of which, when combined with the literature review, revealed seven variables including financial literacy which were pooled into three distinct groups based on the TPB. Responses obtained from 506 retail investors from four cities in India were analysed. Structural equation modelling was used to test the models and arrive at a final empirical model.

Results of the study indicated that investment intention predicts actual investments in the stock market (which represented behaviour). Financial literacy – both subjective and objective – were also found to be significant influencers on intention while only objective financial literacy seemed to affect behaviour. Three variables – perception of regulator, risk avoidance, and hassle factor – were combined to form a second-order construct which was named “Attitude to Investment Behaviour”. This had a negative impact on intention to invest in the equity markets. Financial well-being seemed to have a negative impact on intention while having a positive relationship with behaviour.

The results present significant investor behaviour and policy implications for financial services marketing. Some interventions, especially in the area of consumer financial literacy, are more likely than others to help consumers bridge the gap between non-participation and participation in the stock market.

The study makes a contribution to investor behaviour theory in the form of a comprehensive model to explain SMP in an emerging market. This can be further tested across geographies.