Operations research (OR) has been at the core of decision making since World War II, and today, business interactions on different platforms have changed business dynamics, introducing a high degree of uncertainty. To have a sustainable vision of their business, firms need to have a suitable decision-making process at each stage, including minute details. Our study reviews and investigates the existing research in the field of decision support systems (DSSs) and how artificial intelligence (AI) capabilities have been integrated into OR. The findings of our review show how AI has contributed to decision making in the operations research field. This review presents synergies, differences, and overlaps in AI, DSSs, and OR. Furthermore, a clarification of the literature based on the approaches adopted to develop the DSS is presented along with the underlying theories. The classification has been primarily divided into two categories, i.e. theory building and application-based approaches, along with taxonomies based on the AI, DSS, and OR areas. In this review, past studies were calibrated according to prognostic capability, exploitation of large data sets, number of factors considered, development of learning capability, and validation in the decision-making framework. This paper presents gaps and future research opportunities concerning prediction and learning, decision making and optimization in view of intelligent decision making in today’s era of uncertainty. The theoretical and managerial implications are set forth in the discussion section justifying the research questions. © 2021, Springer Science+Business Media, LLC, part of Springer Nature.

Purpose: This paper aims to explore how a socio-digital platform can facilitate consumer responsibilisation in food consumption to encourage sustained responsible consumption and uncovers its possible impacts on different stakeholders in the agricultural ecosystem. Design/methodology/approach: Two-year-long case study of a socio-digital platform that aims to integrate consumers with the farming process; creating value for them and the farmers in India. Findings: The process of consumer responsibilisation happens through three mechanisms; construction of a moral-material identity, vicarious self-artisanship and shared responsibilisation. Through these key mechanisms, the socio-digital platform could foster consumer responsibilisation and engender positive societal impacts by promoting both responsible production and consumption. Research limitations/implications: This study shows how the construction of moral–material identity could move beyond an either-or choice between moralistic and material identity and allow space for the coexistence of both. This paper highlights how a socio-digital platform can be leveraged to facilitate responsible consumer engagement in an aestheticised farming process. Practical implications: This paper aims to guide policymakers to design digitally-enabled human-centred innovation in facilitating consumer engagement with farming and cultivating responsible consumers in achieving sustainable development goals. Social implications: This study shows how consumer responsibilisation can actually address market failures by enhancing the value created in the system, reducing wastage and cutting costs wherever possible, which drive better incomes for the farmers. Originality/value: Previous studies have discussed heterogeneous motivations for responsible food consumption. However, this research explores the processes through which an individual reconnects to food production and the mechanisms that support this process in the long run. © 2021, Emerald Publishing Limited.

In the wake of the ongoing Covid-19 crisis, this study explores evidence of fiscal procyclicality to the previous pandemic cycles between 2000 and 2017 across 143 countries. Using the novel data set of the World Pandemic Uncertainty index (WPUI) and Dynamic Panel Threshold estimation after considering the endogeneity of variables as proposed by Seo and Shin (2016), this is probably the first attempt to model fiscal responsiveness on the censored and discontinuous effect of pandemic uncertainties. Asymmetric responses on public health expenditure are observed. Although evidence of fiscal procyclicality to pandemic uncertainties is found among the high-income countries and also among the debt-ridden countries, the persistence of responses is not observed in the following period. Further, we find higher relative stimulus by the public sector in low-income countries and also among high debt countries than the private sector, in combating pandemic uncertainties. The study is expected to assume significance for the policymakers as it provides historical evidence of how the Governments have reinforced healthcare during earlier pandemics. © 2020 Elsevier Inc.

With the growth of multi-channel retailing, many firms that have traditionally relied upon third-party service providers to offer after-sales service (AS) have introduced direct AS channel. While there exist a few studies that examine the effects of adding a direct sales channel on the sales revenue of incumbent channels, there are no studies investigating the effects of adding direct AS channel. This study tries to fill this research gap by exploring cross-channel effects of including AS through the firm's direct brand stores on performance (i.e., revenue, assortment width, and extent of upselling) in both direct and indirect channels. A matched difference-in-difference quasi-experimental design was used to compare the sales outcomes of test and control groups using a five-year longitudinal dataset from a large dual-channel consumer durables firm. Results reveal that providing AS through the firm's direct channel has a positive significant effect on the revenue and upselling in the indirect channel, but no significant impact on assortment width in the indirect channel. However, unlike hypothesized, it results in significantly lower revenue, assortment width, and upselling in the direct channel. We interpret these negative effects as arising from customer confusion and perceived retail crowding. Overall, our research highlights that implementation of going direct with AS should be done with utmost care. Our findings would serve as a building block for future studies that are conducted to understand the dynamic cross-channel effects of introducing direct AS, and would be useful for drawing comparisons and contrasts. © 2020 Elsevier Ltd

The significance of IT in the context of non-digital social enterprises (SEs) and its influence on the viability of such SEs have been less studied themes in information systems research. In the era of ubiquitous IT, SEs have increasing pressures to cope with the ever-dynamic technological domain while balancing their dual objectives. Data from a single revelatory case of Jaipur Rugs (JR), an SE situated in a developing country context (India), is used to fathom the peculiarities of the SE context with a focus on the sociomaterial phenomena of IT emergence. Using the three fundamental notions of sociomateriality theory (relationality, performativity, and practice) this interpretive study understands the mechanisms underlying the SE's attempt to balance its financial and social sustainability objectives. Through the innovative use of IT, the SE establishes and maintains shared value, yielding stability and efficiency to the business (financial viability) while simultaneously ensuring scalability and effectiveness of their social impact. This study strengthens the perspective of IT as an emergent phenomenon situated in sociomaterial practices and fills a gap in organizational literature by examining such phenomenon in the rich context of SEs where achieving the balance between dual goals is increasingly dependent on non-human (IT) agents. © 2020 Elsevier Ltd

Global teen population constitutes a sizable market in the luxury brands industry. However, little is known about the mechanism through which teens form an attitude towards luxury brands, particularly in the context of emerging economies. Drawing insights from self-concept theory, self-discrepancy theory, and social consumption motivation, our study proposes a theoretical framework to explain how teenagers' self-related perception influence social consumption motives through avoidance-related indecisiveness, thereby determining teenagers' attitude towards luxury brands. Using a sample of 610 affluent private school students across five major cities of India, we empirically validate our theoretical framework through structural equation modelling. Our study contributes to the existing literature by demonstrating the impact of teenagers’ self-perception on the display of avoidance-related indecisiveness and resisting social consumption motives of luxury brand purchases. © 2020 Elsevier Ltd

Primary Healthcare Service (PHS) is an important area of concern for the policy makers of every nation. Key components of PHS include preventive services that require early disease detection. One major disease where mortality rate can be reduced significantly, if diagnosed early, is Cardio Vascular Disease (CVD). In this work, we have used different Machine Learning models for CVD prediction to compare their performances in terms of accuracy, and found Multilayer Perceptron as a better suited model. Since prediction of the model depends on data set, the model needs as usual continuous learning post deployment too. © 2020 IEEE.

This investigation took an attempt to solve the cluster traveling salesman problem (CTSP) by the heuristic approach. In this problem, nodes are clustered with given a set of vertices (nodes). Given the set of vertices is divided into a prespecified number of clusters. The size of each cluster is also pre-specified. The main aim is to find the least cost Hamiltonian tour based on the given vertices. Here vertices of each cluster visited contiguously, and the clusters are visited in a specific order. Standard GA is used to find a Hamiltonian path for each cluster. The performance of the algorithm has been examined against two existing algorithms for some symmetric TSPLIB instances of various sizes. The computational results show the proposed algorithm works well among the studied metaheuristics regarding the best result and computational time. © Springer Nature Switzerland AG 2020.

Opinion Dynamics procures a prudent tool for modeling opinion management. Existing studies primarily focused on agent-based opinion measures, which was the key issue in the field of Opinion Dynamics. The primary domain of this study is to determine the reliability of a statement with the help of aggregated group opinion values of the associated local agents. This paper proposes a new Mathematical model of Opinion Dynamics which is a slight modification of the Bounded Confidence model to find the Reliability Percentage of a statement. In Opinion Dynamics the Bounded Confidence model is represented as a stochastic model that evolves continuous-valued opinions within a finite group of agents. Based on the opinion values given by the local agents regarding any situation, our mathematical model computes aggregated group opinion which is used to find the Reliability Percentage of a statement that is associated with the same situation. A real data set on the Nepal earthquake is used to illustrate and justify the feasibility of the proposed mathematical model. With this model, the effects of the reliability percentage are explored based on two different Twitter statements.. © 2020 IEEE.

The traveling purchaser problem (TPP) is a notable generalization of the traveling salesman problem (TSP) which involves selecting a subset of markets at a minimum traveling cost such that the demand for each product is satisfied. A solid green traveling purchaser problem (SGTPP) is a TPP in which, at each market, some conveyances are available to travel to another market with minimum cost considering the environmental impact caused by carbon emission. In this paper, we formulate an SGTPP with travel cost between each pair of markets and purchase price of the products as uncertain variables. Using uncertainty theory, an expected value model is formulated and then transformed into the corresponding deterministic form. Finally, a noble genetic algorithm (nGA) is designed to solve the proposed model. The algorithm is called noble because it adopts a crossover of the combination of a probabilistic selection of three parents, according to real-life In Vitro Fertilization (IVF) techniques. Computational results reveal that our proposal is favorably compared to previous algorithms in the existing literature. © 2020 Taylor & Francis Group, LLC.

This paper aims to assess the factors favoring the adoption of the challenges faced and support mechanism, which will lead to the proliferation of glass fiber-reinforced gypsum (GFRG) technology in India. Design/methodology/approach: Semi-structured interviews with 35 experts, including construction developers, architects, contractors, government officials and design consultants, were conducted. This qualitative data was analyzed using thematic analysis and matrix analysis. Findings: GFRG-based buildings produce much less carbon footprints as compared to traditional ones and can be safely recommended as a promising, environmentally sensitive technology of the future. The major drivers in its adoption are its efficient construction capability, energy and soil conservation and significant waste reduction. Some of the challenges in implementation are long planning time, lack of skilled labor, lack of awareness about green building technologies and myopic perception of high cost incurred in green building adoption in people’s minds. Practical implications: This study establishes that the construction industry has the potential to contribute toward creating a sustainable and green planet. It does so by evaluating and then positively positioning GFRG as an environmentally friendly building system. Originality/value: The harmful effects of continuous environmental manipulation by humans leading to its degradation is a critical discussion agenda for most nations of the world. The issue has been taken up seriously by developing countries, and now, developing countries are also becoming sensitised to it. Several policies toward the attainment of this goal have been formulated and are being implemented by government and private bodies. Although some authors have studied the issues and challenges related to the adoption of green buildings, their attempts mostly focused on developed countries. Moreover, research that investigated the evaluation of the GFRG building system as a successful green technology of the future is inadequate. © 2020, Emerald Publishing Limited.

Spiritual well-being plays an integral role in the reconstruction of past events. In this direction, the current study investigates how differences in individuals’ spiritual well-being elicit different cognitive and emotional responses to nostalgic ads. And how such differences in spiritual well-being lead to the different purchase intention of nostalgic products. We displayed a series of old Indian ads to respondents and found spiritual well-being influenced ad-evoked nostalgia and purchase intention of nostalgic products positively. Our study contributes to a road less traveled in nostalgia marketing and necessitates marketers to consider audiences’ spiritual well-being before applying nostalgic appeal. © 2020 Taylor & Francis Group, LLC.

Skippable video advertisements (ads), which allow uninterested users to skip the ad after a few seconds, have witnessed rapid growth in the past few years. While their advantages for viewers and advertisers are obvious, they pose an ad revenue optimization problem for their publishers, i.e., the Video Sharing Platforms (VSPs). The VSPs need to critically balance the higher but uncertain revenue from skippable ads with the lower but guaranteed revenue from non-skippable ads. This problem is particularly challenging because non-skippable ads cause higher disutility to viewers. Moreover, due to network effect, this disutility has a long term impact on the VSPs’ revenue. In this paper we study the revenue management problem faced by a VSP in determining the optimal mix of skippable and non-skippable ads. We model VSP as a two sided platform, identify conditions under which an advertiser would prefer skippable ads over non-skippable ones, and derive the optimality conditions for VSP's optimal ad mix. Our model reveals the existence of an upper bound on number of non-skippable ads, such that continued violation of this upper bound leads to a cascading effect, resulting in a reduction of both skippable and non-skippable ads over time. Our analysis helps a VSP in determining the incentive it should provide to the advertisers to switch to its preferred ad type. Our study reveals that non-skippable ads are essential for VSPs with niche or low content, and the proportion of skippable ads increases as the content increases or becomes more general. © 2020 Elsevier B.V.

The literature on decision biases in the newsvendor model assumes classical version of the problem where the distribution of random demand is known. This context is decision-making under risk. In many real-life settings, firms are not able to elicit complete and exact information about the demand distribution. This results in decision-making under ambiguity. We examine the newsvendor ordering preferences under ambiguity. Our study is the first attempt in behavioural operations management research to examine the biases in newsvendor decisions under ambiguity. We design experiments to understand the ordering preferences under ambiguity and risk. The experimental results show that subjects deviate from the normative benchmarks. We observe ‘pull-to-center’ bias in newsvendor decisions under ambiguity. We also observe that subjects exhibit ‘asymmetry in ordering’. Both these biases have significant implications for both theory and practice. Our research is a building block for research in a variety of normative models in operations management literature where ambiguity in demand is a highly relevant context for decision-making. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

This study identifies a novel source of data, i.e. images from smart urban infrastructures, that would be helpful in effective disaster management decision-making. For this purpose, we collected images from disaster-hit environments of Central Mexico (2017 earthquake). Also, this study utilizes deep learning convolutional neural network to analyze this novel dataset and evaluates the model effectiveness and technical viability during crisis scenarios. TensorFlow was utilized for the image classification task. The findings have important significance for effective disaster response. © 2020 ACM.

Women with disabilities are subjected to double discrimination in Indian society. Despite growing concern for the disabled community in society at large, their education remains only aspirational. The attitudes that women with disabilities encounter in their educational endeavours are explored. A thematic analysis of 27 narratives of women with disabilities from Odisha identifies and distinguishes the familial and communal attitudes they face. Policy must aim at ensuring not just physical integration, but also the social acceptance and inclusion of women with disabilities. © 2020 Economic and Political Weekly. All rights reserved.

[No abstract available]

No amount of crystal ball gazing may help us fathom the full impact of the Covid-19 (C-19) crisis on business organizations in a distinct manner. Given the lack of precedence, any such analyses seem to demand routine revisions as we progress further up the “number of infected” curve. Most countries of the world have imposed restrictions on social congregations or even people working in close proximity to each other. Industries that produce and deliver information products and services therefore, have continued to function while those that manufacture physical products especially labor-intensive firms were forced to minimize operations or temporarily shut down. However, in most countries, physical products which were essential in nature were reluctantly permitted to be manufactured given the need for them in people's everyday life. In this viewpoint, I draw upon three dimensions – information intensity of product/service, information intensity of process/value chain; along with a third dimension – essential nature of the product/service to help understand the immediate implications of C-19. I also present some anecdotal evidences of attempts to alter business models in these circumstances in order to address the challenges that certain product characteristics impose but at the same capitalize on the business opportunities presented by the essentiality of the products. © 2020 Elsevier Ltd

‘Buy online and pick up in-store’ (BOPS), wherein a customer buys the product online and picks it up from the physical store at her convenience, is fast gaining popularity. However, BOPS service complicates the store's inventory policy as BOPS orders, while waiting to be picked up, block inventory and thus delay replenishment requests. The store now has to maintain a base-stock level that is optimal considering both walk-in and BOPS orders. We discuss an inventory control policy that suits this situation. The problem is accentuated when the online channel and the physical store are under different ownerships, since the revenue from BOPS orders will now get shared between the two. In such a scenario, the store may opt for an inventory policy that favours walk-in orders over BOPS requests, as every BOPS order fetches less revenue than every walk-in customer. The extent of restriction on BOPS orders will depend on the revenue-sharing policy and also on the relative frequencies of walk-in and BOPS demands. We discuss two inventory policies that can help the store prioritize walk-in demands over BOPS demands. We compare the performances of the policies proposed, and show that each policy has its claim to supremacy over others in different operating environments, characterized by the input parameters. © 2020 Elsevier B.V.

The advancement in immersive technologies provides online retailers the opportunity to integrate augmented reality (AR) experience for their customers. Using AR, the online product presentation is transformed from the pictorial representation to virtual interaction with the products. The virtual product interaction facilitates online retailers to detect product-related emotion through affective computing. For mobile AR, customers use touch gestures for virtual interaction. Using the theories related to immersive media and affective computing, we hypothesize that the touch movements and touch pressure in AR-based mobile applications are related to positive emotion during product interaction. Moreover, we describe a methodology to establish our hypotheses and to show that these variables can predict the produ ct-related emotion. We expect our research findings to have both theoretical and practical implications. It will explain why touch behavior can predict product-related emotion, and it will also demonstrate online retailers how to implement emotion analytics in AR shopping applications. © 2020 26th Americas Conference on Information Systems, AMCIS 2020. All rights reserved.