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The purpose of this paper is to generalize the traditional risk evaluation methods and to specify a multi-level risk evaluation framework, in order to prepare customized risk evaluation and to enable effectively integrating the elements of risk evaluation.

A real case study of an electric motor manufacturing company is presented to illustrate the advantages of this new framework compared to the traditional and fuzzy failure mode and effect analysis (FMEA) approaches.

The essence of the proposed total risk evaluation framework (TREF) is its flexible approach that enables the effective integration of firms’ individual requirements by developing tailor-made organizational risk evaluation.

Increasing product/service complexity has led to increasingly complex yet unique organizational operations; as a result, their risk evaluation is a very challenging task. Distinct structures, characteristics and processes within and between organizations require a flexible yet robust approach of evaluating risks efficiently. Most recent risk evaluation approaches are considered to be inadequate due to the lack of flexibility and an inappropriate structure for addressing the unique organizational demands and contextual factors. To address this challenge effectively, taking a crucial step toward customization of risk evaluation.

This paper tests whether Bayesian A/B testing yields better decisions that traditional Neyman-Pearson hypothesis testing. It proposes a model and tests it using a large, multiyear Google Analytics (GA) dataset.

This paper is an empirical study. Competing A/B testing models were used to analyze a large, multiyear dataset of GA dataset for a firm that relies entirely on their website and online transactions for customer engagement and sales.

Bayesian A/B tests of the data not only yielded a clear delineation of the timing and impact of the intellectual property fraud, but calculated the loss of sales dollars, traffic and time on the firm’s website, with precise confidence limits. Frequentist A/B testing identified fraud in bounce rate at 5% significance, and bounces at 10% significance, but was unable to ascertain fraud at the standard significance cutoffs for scientific studies.

None within the scope of the research plan.

Bayesian A/B tests of the data not only yielded a clear delineation of the timing and impact of the IP fraud, but calculated the loss of sales dollars, traffic and time on the firm’s website, with precise confidence limits.

Bayesian A/B testing can derive economically meaningful statistics, whereas frequentist A/B testing only provide p-value’s whose meaning may be hard to grasp, and where misuse is widespread and has been a major topic in metascience. While misuse of p-values in scholarly articles may simply be grist for academic debate, the uncertainty surrounding the meaning of p-values in business analytics actually can cost firms money.

There is very little empirical research in e-commerce that uses Bayesian A/B testing. Almost all corporate testing is done via frequentist Neyman-Pearson methods.

Presentation of the different industrial carbon linkages of India. The purpose of this paper is to understand the direct and indirect impact of these industrial linkages.

This study uses a hypothetical extraction method with its various extensions. Under this method, different carbon linkages of a block are removed from the economy, and the effects of carbon linkages are determined by the difference between the original and the post-removal values. Energy and non-energy carbon linkages are also estimated.

“Electricity, gas and water supply (EGW)” at 655.61 Mt and 648.74 Mt had the highest total and forward linkages. “manufacturing and recycling” at 231.48 Mt had the highest backward linkage. High carbon-intensive blocks of “EGW” plus “mining and quarrying” were net emitters, while others were net absorbers. “Fuel and chemicals” at 0.08 Mt had almost neutral status. Hard coal was the main source of direct and indirect emissions.

Net emitting and key net forward blocks should reduce direct emission intensities. India should use its huge geographical potential for industrial accessibility to cheaper alternative energy. This alongside with technology/process improvements catalyzed by policy tools can help in mitigation efforts. Next, key net-backward blocks such as construction through intermediate purchases significantly stimulate emissions from other blocks. Tailored mitigation policies are needed in this regard.

By developing an understanding of India’s industrial carbon links, this study can guide policymakers. In addition, the paper lays out the framework for estimating energy and non-energy-based industrial carbon links.

The purpose of this study is to determine how consumers perceive restaurant service in Bangladesh. The goal of this study is to determine the impact of perceived service quality and price fairness on consumer satisfaction and loyalty toward restaurant services.

The data for this study were collected from 326 respondents who have used restaurant services in Bangladesh. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to evaluate the research data with SmartPLS 3.3.3.

The current study’s findings indicate that perceived service quality and price fairness both have a direct and significant effect on consumer satisfaction. Additionally, the research findings reveal a considerable association between perceived pricing fairness and consumer pleasure and consumer loyalty. Additionally, findings indicate that perceived service quality and price fairness both have a significant indirect effect on customer loyalty via the mediating effect of consumer satisfaction.

The current study demonstrates that perceived price fairness has a substantial effect on restaurant consumers’ satisfaction and loyalty; thus, this evidence can assist restaurant owners and managers in developing and implementing their pricing strategy to retain customers. Additionally, the findings have significant implications for restaurant operators, practitioners and policymakers.

Only a few research have been conducted to determine the effect of service quality and pricing fairness on consumer satisfaction and loyalty when it comes to restaurant services. This research conclusion provides guidance to service providers on how to increase customer satisfaction and loyalty through an emphasis on price fairness and service quality.

There is a mental health crisis, particularly among young people. Despite many young people living in urban settings, reviews about the association between exposure to green or natural environments and mental health tend to focus on either children or adults. The aim of this review is to examine the scope of the global literature for this age group, to inform a systematic review on the role of exposure to green space in preventing anxiety and depression amongst young people aged 14–24 years.

Seven databases were searched for quantitative and qualitative sources published from January 2000 to June 2020. This identified 201 sources and their characteristics are described here. Gaps in the literature are also highlighted.

The number of relevant studies published per year has increased over time. Most studies are set in North America (28%) or Europe (39%). The most common study designs were observational (34%) or experimental (28%). A wide range of exposures and interventions are described.

This review included literature from predominantly high-income countries and has shown the under-representation of low-middle income countries and lack of ethnic diversity in study populations. It has also highlighted the lack of clinical measures of anxiety and depression as outcomes.

This inter-disciplinary review has contributed to the field by describing the geographic distribution of the literature and the broad range of exposures to green spaces being reported. Unlike previous scoping reviews, this review focused specifically on young people and on measures of anxiety and depression and their pre-cursers.

Quantitative and spatial-explicit flood risk information is of great importance for strengthening climate change adaptation and flood resilience. Shanghai is a coastal megacity at large estuary delta with rising flood risks. This study aims to quantify the overall economic-societal risks of storm flooding and their spatial patterns in Shanghai.

Based on multiple storm flood scenarios at different return periods, as well as fine-scale data sets including gridded GDP, gridded population and vector land-use, a probabilistic risk model incorporating geographic information system is used to assess the economic-societal risks of flooding and their spatial distributions.

Our results show that, from 1/200 to 1/5,000-year floods, the exposed assets will increase from USD 85.4bn to USD 657.6bn, and the direct economic losses will increase from USD 3.06bn to USD 52bn. The expected annual damage (EAD) of assets is around USD 84.36m. Hotpots of EAD are mainly distributed in the city center, the depressions along the upper Huangpu River in the southwest, the north coast of Hangzhou Bay, and the confluence of the Huangpu River and Yangtze River in the northeast. From 1/200 to 1/5,000-year floods, the exposed population will rise from 280 thousand to 2,420 thousand, and the estimated casualties will rise from 299 to 1,045. The expected annual casualties (EAC) are around 2.28. Hotspots of casualties are generally consistent with those of EAD.

In contrast to previous studies that focus on a single flood scenario or a particular type of flood exposure/risk in Shanghai, the findings contribute to an understanding of overall flood risks and their spatial patterns, which have significant implications for cost-benefit analysis of flood resilience strategies.

The customers are demanding the products which are not only healthy but also clean and environment friendly i.e. call for sustainable consumption products. Therefore, this study aims to identify the important drivers of organic food purchase intention.

A cross-sectional research design involving the collection of primary data from 234 respondents was adopted in this study. Responses were gathered from the consumers of organic food representative of the Indian population. Structural equation modelling was applied to analyze data and validate the research model.

The findings of the study would help practitioners understand the factors leading to the purchase intention of organic food products in a growing consumer market. This knowledge would help them devise marketing and communication strategies to increase the consumption of organic food products.

The present study advances existing literature on organic food consumption by extending the theory of planned behaviour with factors, namely, environmental concern, convenience and trust, and establishing their role in developing the purchase intention for organic food products.

Los consumidores demandan productos no sólo saludables, sino también limpios y respetuosos con el medio ambiente, es decir, productos de consumo sostenible. Por lo tanto, este estudio pretende identificar los principales factores que influyen en la intención de compra de alimentos ecológicos.

En este estudio se adoptó un diseño de investigación transversal que incluía la recogida de datos primarios de 234 encuestados. Las respuestas procedían de consumidores de alimentos ecológicos representativos de la población india. Se aplicó un modelo de ecuaciones estructurales para analizar los datos y validar el modelo de investigación.

Las conclusiones del estudio ayudarán a los profesionales a comprender los factores que conducen a la intención de compra de productos alimentarios ecológicos en un mercado de consumidores en crecimiento. Este conocimiento les ayudaría a diseñar estrategias de marketing y comunicación para aumentar el consumo de alimentos ecológicos.

El presente estudio avanza la literatura existente sobre el consumo de alimentos orgánicos mediante la ampliación de la TPB con factores, a saber, la preocupación por el medio ambiente, la conveniencia y la confianza, y el establecimiento de su papel en el desarrollo de la intención de compra de productos alimenticios orgánicos.

顾客要求的产品不仅是健康的, 而且是清洁和环保的, 即呼吁可持续消费产品。因此, 本研究旨在确定有机食品购买意向的重要驱动因素。

本研究采用横断面研究设计, 从234名受访者中收集原始数据。受访者的回答来自于代表印度人口的有机食品消费者。采用结构方程模型来分析数据并验证研究模型。

本研究的结果将有助于从业者了解在不断增长的消费市场中导致有机食品购买意向的因素。这些知识将帮助他们制定营销和沟通策略, 以增加有机食品的消费。

本研究通过扩展TPB的因素, 即环境关注、便利性和信任, 并确定它们在发展有机食品购买意向中的作用, 从而推进了现有的关于有机食品消费的文献。

The Covid-19 pandemic represents an extraordinary and unpredictable event influencing the whole economy and society. Using a case study approach, the authors explored how big companies operating in Italy were able to respond to the crisis to ensure business continuity. Therefore, the authors discussed different business disruptions caused by Covid-19; in addition, they developed an integrated policy framework in which they proposed specific strategic responses for increasing the level of organizational resilience. The aim of the paper is to discuss and reflect on the ability of specific companies to increase their level of organizational resilience when unexpected events happen.

The authors conducted semi-structured interviews with target CEOs of four major companies operating in Italy in different industries. Especially, the authors focused on multiutilities, media and communications, investment banking and mobile telecommunications. The interviews were based on questions dealing with Covid-19 implications and strategic responses to navigate this complex scenario.

Based on the CEOs' contributions, the authors presented short-term actions and long-term strategies planned and implemented by each Italian company. In particular, the authors emphasized similarities and dissimilarities in terms of strategic responses and future business development.

The analysis of practical cases was helpful for increasing their knowledge on organizational resilience and for providing the managerial community with a policy framework to navigate unexpected events like the Covid-19 pandemic.

We study five exogenous shocks: climate, violence, price hikes, spoilage and the COVID-19 lockdown. We analyze the association between these shocks and trader characteristics, reflecting trader vulnerability.

Using primary survey data on 1,100 Nigerian maize traders for 2021 (controlling for shocks in 2017), we use probit models to estimate the probabilities of experiencing climate, violence, disease and cost shocks associated with trader characteristics (gender, size and region) and to estimate the probability of vulnerability (experiencing severe impacts).

Traders are prone to experiencing more than one shock, which increases the intensity of the shocks. Price shocks are often accompanied by violence, climate and COVID-19 shocks. The poorer northern region is disproportionately affected by shocks. Northern traders experience more price shocks while Southern traders are more affected by violence shocks given their dependence on long supply chains from the north for their maize. Female traders are more likely to experience violent events than men who tend to be more exposed to climate shocks.

The data only permit analysis of the general degree of impact of a shock rather than quantifying lost income.

This paper is the first to analyze the incidence of multiple shocks on grain traders and the unequal distribution of negative impacts. It is the first such in Africa based on a large sample of grain traders from a primary survey.