This article is focused on the problem of river sharing in the presence of pollution as a negative externality between two riparian states (agents). In this paper, a market-based contract mechanism is presented; it can address the issue of negative externality imposed by an upstream agent on the downstream agents while sharing a river. The proposed mechanism incorporates a penalty for pollution and also incentives for trading water between upstream and downstream agent. The mechanism introduces a new concept of negative water as penalty against pollution for an upstream agent in a contract for water sharing. The contract is analyzed by a market-based bargaining model to determine a negotiated treaty between the upstream agent and the downstream agent. The results show the characterization of agents with regard to agreement points for negotiated treaty. Also, it shows that an equilibrium exists for a unique solution that makes both the agents better off. The model discussed in this paper can be easily applied to any transboundary river conflict where pollution plays an important role. © 2021, Operational Research Society of India.

Recent unveiling of electric semi-trucks by a number of electric vehicle manufacturers indicates that part of the existing long-distance transportation fleets may soon be electrified. Operators of electric fleets will have to select travel routes considering charging station availability and cost of charging in addition to usual factors such as congestion and travel time. This requires combined modeling of transportation and electric power networks. We present such a model that considers interactions between the two networks to develop optimal routing strategies. The problem is formulated as a multi-objective bilevel conic optimization model. The upper level obtains the routing decision by minimizing a function of charging cost and travel time. The routing decision is used in the lower level that solves the AC optimal power flow model, using second order cone constraints, to determine nodal electricity prices. The model is demonstrated using a numerical problem with 24-Node transport network supported by a modified 5-Bus PJM network. The results show that our model yields optimal routes and charging strategies to meet the objectives of fleet operators. Results also indicate that the optimal routing and charging strategies of the electrified transportation fleet can support power networks to reduce nodal prices via demand response. © 2022 Elsevier Ltd

Admissions to academic programs often involve filling a number of seats by making offers to a ranked list of qualified candidates over a finite number of rounds. Two sources of uncertainty need consideration when making admission offers: first, a random fraction of offers is accepted by applicants; and second, a random fraction of applicants who initially indicate acceptance subsequently withdraw. We develop a binomial decision-tree model to determine the number of admission offers to be made while considering (a) the expected costs of exceeding or falling short of target enrollment, and (b) the fact that the more competitive students are also less likely to enroll. Insights from the model are validated using a multi-year empirical dataset of admission offers, acceptances and post-acceptance withdrawals for an MBA program. We find that having multiple rounds to make offers helps admissions offices achieve enrollment targets with greater precision. Additional rounds are particularly valuable when the uncertainty of yield rate is high. In a counterintuitive result, we identify conditions under which the recommended number of offers increases with the uncertainty in yield. We also show that it might be possible to improve the quality of an admitted class by sending out more offers sooner. © 2021 Wiley Periodicals LLC.

National Power Limited (NPL) was a public sector undertaking of the Government of India under the Ministry of Power, having a total generating capacity of 4,860 megawatts of coal-based thermal power. NPL had seven power plants in operation across three states in the northern part of the country, with 8,212 employees. The case describes an appointment of a non-executive post at a power-generating plant of NPL, which is referred to as the Chandanpur unit. This appointment was made to honour an unprincipled demand by the Minister of Forestry and Environment of the concerned state government. The matter got exposed through a complaint received by the Vigilance Department of NPL. The manager of the coal handling department of the Chandanpur unit had lodged the complaint, mentioning the possibility of anomalies in the appointment. On verification, it was found out that the executive director of the Chandanpur unit had appointed a person in the highest supervisory post of that unit by flouting all rules and guidelines of recruitment in the organization. Anomalies included not taking approval for upgradation of the offer made initially, interviewing directly without holding any written test and changing the minimum eligibility criterion for the post. All this was a major breach of Article 14 of the Constitution of India, which confers the right to equality to a citizen of India as a fundamental right. Based upon findings of the investigation report, the culpability established in the irregularity and gross violation of the established rules of NPL, major disciplinary proceedings were initiated against all the involved officials and the appointee. Since it was a criminal case, it was also referred to the Federal Investigating Agency for further investigation and prosecution of the offenders. All NPL executives involved, and the appointee was implicated, though no culpability could be established against the Minister or his confidential assistant. © 2022 Lahore University of Management Sciences.

Business operations became challenging when the COVID-19 pandemic struck, and governments applied significant lockdown measures to stop the spread. Start-ups and small-medium businesses started facing hardships due to the changes in the business environment and the resulting constricted cash flow. This case describes challenges faced by a high-tech product start-up company and the way they tackled these hardships. The company had adopted an agile programming methodology for product development which faced a major challenge because of the sudden introduction of “work from remote” (WFR). WFR had disrupted collaboration between clients, sales teams, and development teams. The success of agile methodology was dissipating as teams could not do on-premise huddles. The company witnessed an overrun in the sprint schedule, increase of budgeted cost, and sliding customer satisfaction. Senior management could not manage process parameters properly, and therefore predictability of the processes waned. The start-up owners, through the application of the “dynamic capabilities framework,” focused on reconfiguring the agile working processes and improving the remote working capability of employees. This case shows the start-up’s agile development process transformation journey following the dynamic capabilities framework to overcome the challenges posed by the pandemic. The teaching case focuses on imparting knowledge on agile development and dynamic capabilities to Information Systems students. © 2022 The Author(s). Published with license by Taylor & Francis Group, LLC.

Industrial Internet of Things emerged as one of the major technologies enabling Industry 4.0 for industries. Multiple start-ups started working in the Industrial Internet of Things field to support this new industrial revolution. Distronix, one such Industrial Internet of Things start-up of India, started operations in 2014, when companies were not even aware of Industrial Internet of Things. Distronix started executing fixed-fee projects for implementation of Industrial Internet of Things. They also started manufacturing sensors to support large customers end-to-end in their Industry 4.0 journey. With the advent of public cloud, companies started demanding pay-per-use model for the solution Distronix provided. This posed a major challenge to Distronix as they had developed technology skills focusing fixed-fee customized project delivery for their clients. The situation demanded that they change their business model from individual project delivery to creation of product sand-box with pre-registered sensors and pre-defined visualization layer to support use cases for Industrial Internet of Things implementation in multiple industry sectors. It forced Rohit Sarkar, the 26 years old entrepreneur and owner of Distronix, to upgrade capabilities of his employees and transform the business model to support pay-per-use economy popularized by public cloud providers. The case discusses the challenges Rohit faced to revamp their business model in such an emerging technology field, like, to develop new skills of the technical people to support such novel initiative, reorienting sales people towards pay as use model, developing new concept of plug and play modular product, devising innovative pricing, better alliance strategy and finding out a super early adopter. © Association for Information Technology Trust 2021.

[No abstract available]

Purpose: Innovative restaurant service designs impart food wellbeing to diners. This research comprehends customer aspirations and concerns in a restaurant-dining experience to develop a service design that enhances the dining experience using the design thinking approach and evaluates its efficiency using the Taguchi method of robust design. Design/methodology/approach: The sequential incidence technique defines diners' needs, which, followed by brainstorming sessions, helped create multiple service designs with important attributes. Prototype narration, as a scenario, acted as the stimulus for evaluators to respond to the WHO-5 wellbeing index scale. Scenario-based Taguchi experiment with nine foodservice attributes in two levels and the wellbeing score as the response variable helped identify levels of critical factors that develop better FWB. Findings: The study identified the best combination of factors and their preferred levels to maximize FWB in a restaurant. Food serving hygiene, followed by information about cuisine specification, and food movement in the restaurant, were important to FWB. The experiment revealed that hygiene perceptions are critical to FWB, and service designs have a significant role in it. Consumers prefer detailed information about the ingredients and recipe of the food they eat; being confident that there will be no unacceptable ingredients added to the food inspires their FWB. Research limitations/implications: Theoretically, this study contributes to the growing body of literature on design thinking and transformative service research, especially in the food industry. Practical implications: This paper details a simple method to identify and evaluate important factors that optimize FWB in a restaurant. The proposed methodology will help service designers and technology experts devise settings that consider customer priorities and contribute to their experience. Originality/value: This study helps to understand the application of design thinking and the Taguchi approach for creating robust service designs that optimize FWB. © 2021, Emerald Publishing Limited.

Purpose: The purpose of this paper is to investigate and compare what criteria novice and habitual entrepreneurs use while adding members to the founding team. Design/methodology/approach: This paper uses conjoint analysis (CA) to provide the order of preference for the “choice attributes.” The logic of CA is that even if two or more attributes influence the choice, it is unlikely that those attributes will have equal importance for founders with different entrepreneurial experiences. Findings: This paper found a significant difference in the ranking of the attributes by novice and habitual entrepreneurs. In novice entrepreneurs, the effect of direct ties in the form of kinship ties has the highest preference, followed by prior social contact and prior work relations. However, personal friendships and resource dependency received lesser importance than interpersonal attraction because of the similarity in vision, beliefs and values. Habitual entrepreneurs, however, valued resource dependency and prior work relations more than kinship ties. Also, unlike novice entrepreneurs, habitual entrepreneurs sought cofounders from their indirect ties. Practical implications: There has been an explosion of interest and funding for programs that help entrepreneurs establish a cofounding team. The authors inform these programs related to the decision concerning assisting novice and habitual entrepreneurs. Originality/value: While prior studies examined a single attribute at a time, the strength of this study lies in simultaneously tapping all attributes, along with multiple indicators for each attribute. Additionally, this study distinguishes the selection criteria of cofounders based on the entrepreneurial expertise of the lead founder. © 2022, Emerald Publishing Limited.

The utility of a service or product can be considered as an aggregation of utilities of multiple attributes, that the consumers consider while choosing a product. The two methods used to generate a linear utility function are the logarithmic goal programming model (LGPM) and the conjoint analysis method (CAM). In these two methodologies, the procedures used to collect data and generate the utility function differ significantly. This is possibly the first study to compare the two methods for determining the utility function of a product (here vehicle insurance policy). For this study we will be collecting the data from the same set of respondents (customers) for the same set of five different brands of the product (vehicle insurance policy) available in the market. The similarities and differences among LGPM and CAM approaches are examined to provide useful insights in terms of consistency in consumer behaviour while prioritizing their choices for a product. The study addresses if the priority order of consumer choices for a product remains the same or changes if the methodology changes. Moreover, we apply a multinomial logit choice model to derive a choice probability of the brands available in the market using both approaches. © 2021, Operational Research Society of India.

The Internet of Things (IoT) is a relatively new technology that has piqued academics’ and business information systems’ attention in recent years. The Internet of Things establishes a network that enables smart devices in an organisational information system to connect to one another and exchange data with the central storage. Android apps are placed on Android apps to enhance the user-friendliness of IoT devices in business information systems, making them more interactive and user-friendly. However, the usage of Android apps makes IoT devices susceptible to all forms of malware attacks, including those that attempt to hack into IoT devices and get access to sensitive information stored in the corporate information system. The researchers offered a variety of attack mitigation approaches for detecting harmful malware embedded in an Android application operating on an IoT device. In this context, machine learning offered the most promising strategies to detect malware attacks in IoT-based enterprise information systems because of its better accuracy and precision. Its capacity to adapt to new forms of malware attacks is a result of its learning capabilities. Therefore, we conduct a detailed survey, which discusses emerging machine learning algorithms for detecting malware in business information systems powered by the Internet of Things. This article reviews all available research on malware detection, including static malware detection, dynamic malware detection, promoted malware detection and hybrid malware detection. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

Online reviews are integral to consumer decision-making while purchasing products on an e-commerce platform. Extant literature has conclusively established the effects of various review and reviewer related predictors towards perceived helpfulness. However, background research is limited in addressing the following problem: how can readers interpret the topical summary of many helpful reviews that explain multiple themes and consecutively focus in-depth? To fill this gap, we drew upon Shannon's Entropy Theory and Dual Process Theory to propose a set of predictors using NLP and text mining to examine helpfulness. We created four predictors - review depth, review divergence, semantic entropy and keyword relevance to build our primary empirical models. We also reported interesting findings from the interaction effects of the reviewer's credibility, age of review, and review divergence. We also validated the robustness of our results across different product categories and higher thresholds of helpfulness votes. Our study contributes to the electronic commerce literature with relevant managerial and theoretical implications through these findings. © 2022 Elsevier B.V.

Using a framework of colonization in science, technology, engineering, and mathematics (STEM), this U.S.-based study examined how seven Native American PhD students/ postdoctoral scholars experienced impostor phenomenon. Participants were identified/ contacted at a national conference on minorities in STEM through purposeful sampling. Surveys computed impostor phenomenon scores on a validated scale, while interviews documented how identity and culture contributed to impostor phenomenon in academia. Using a phenomenological approach, interviews were analyzed inductively using a constant comparative method. Surveys scores indicated high to intense impostor phenom-enon. Interviews with the same participants further identified the following aspects of impostor phenomenon in relation to their minoritized identity: cultural differences and lack of understanding of Indigenous culture, lack of critical mass and fear of standing out, academic environment, family background and upbringing, and looks and diversity status. Developing a diverse and culturally competent STEM workforce requires a deeper understanding of what deters Native American individuals from pursuing a STEM career. They have the lowest college enrollment and retention rates compared with any race in the United States and could be vulnerable to racial bias and discrimination. Understanding impostor phenomenon through culturally relevant experiences would be crucial to broaden participation in STEM careers. © 2022, American Society for Cell Biology. All rights reserved.

Data-driven approaches have noteworthy significance in managing and improving logistics in E-commerce enterprises. This study focuses on the development of an integrated framework to analyse the Brazilian E-Commerce enterprise public dataset. From the analysis, it is found that sellers of Ibitinga city of SP state had the most count of late deliveries where 42 sellers are under-performing in terms of estimated delivery time. Locations of customers and sellers were spotted on a map to get a geographical representation. The proposed framework may help E-Commerce enterprise owners and retail merchants to make better decisions related to sales and E-Commerce enterprise logistics. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

We employ Data Envelopment Analysis to estimate the new technical, new cost, and new profit efficiency of Indian banks over the period 2008–2018. Then, we use Random Forest Regression to examine the impact of corporate governance (Board Size, Board Independence, Duality, Gender Diversity, and Board Meetings), bank characteristics (Return on Assets, Size, and Equity to Total Assets), and other characteristics (Ownership and Years) on bank efficiency. Among others, we found that board characteristics play a significant role particularly in new profit efficiency; therefore, policymakers and regulators should consider Board Size, Board Independence, Board Meetings, and Duality while framing guidelines for enhancing bank new profit efficiency. We also found that Board Independence plays a vital role in bank new cost efficiency, while Gender Diversity contributes to both new technical and new cost efficiency. This study makes methodological contributions by employing Machine Learning based Random Forest Regression in tandem with Data Envelopment Analysis under a two-phase model to examine corporate governance and bank efficiency, which is a pioneering attempt. © Operational Research Society 2021.

We employ Data Envelopment Analysis to estimate the new technical, new cost, and new profit efficiency of Indian banks over the period 2008–2018. Then, we use Random Forest Regression to examine the impact of corporate governance (Board Size, Board Independence, Duality, Gender Diversity, and Board Meetings), bank characteristics (Return on Assets, Size, and Equity to Total Assets), and other characteristics (Ownership and Years) on bank efficiency. Among others, we found that board characteristics play a significant role particularly in new profit efficiency; therefore, policymakers and regulators should consider Board Size, Board Independence, Board Meetings, and Duality while framing guidelines for enhancing bank new profit efficiency. We also found that Board Independence plays a vital role in bank new cost efficiency, while Gender Diversity contributes to both new technical and new cost efficiency. This study makes methodological contributions by employing Machine Learning based Random Forest Regression in tandem with Data Envelopment Analysis under a two-phase model to examine corporate governance and bank efficiency, which is a pioneering attempt. © Operational Research Society 2021.

Purpose: The purpose of this study is two-fold. First, it proposes a definition of Muslim entrepreneurship and second, it synthesizes existing literature on Muslim entrepreneurship published in the past decade. Design/methodology/approach: A systematic literature review technique has been used to identify and analyse the literature for a period between 2009 and 2019. Findings: Results of the study suggest that there is a dearth of literature conceptualizing and operationalizing the concept of Muslim entrepreneurship in the management literature. Further, studies examining the factors which affect Muslim entrepreneurship practices are limited. Research limitations/implications: The study has analysed only peer-reviewed articles from management literature. Originality/value: A synthesis of the literature on Islamic entrepreneurship is missing. Also, literature proposing a comprehensive definition of the concept and summarizing the factors which affect Muslim entrepreneurship practices are absent. © 2021, Emerald Publishing Limited.

The increasing carbon dioxide level in the earth's atmosphere and continuously changing climate creates a significant challenge to sustainability in the world. It is not easy to control pollution due to carbon dioxide emissions from coal-fired power plants into the atmosphere. However, carbon capture technology provides an advantage for capturing carbon from power plants. Various researchers suggested the non-linear optimization model with post-combustion carbon capture technology in coal-fired power plants to reduce carbon emission. However, in their research articles, most researchers did not include loss of power due to retrofitting carbon capture technology in power plants and carbon emission from the compensatory power plant. This paper proposes a linear optimization model that minimizes the emission release from the power plant and its compensatory plant by appropriate selection of carbon capture technology. Our proposed model incorporates loss of power due to adopting carbon capture technology and emission release from the power plant and compensatory power plant in the problem formulation. We have also generated the Pareto curve that determines the trade-off solutions between emission release and the overall electricity cost. The applicability of our model is illustrated through power sector data from two Indian states. The net reduction of emissions in the two states are 27.17 % and 26.29 %, achieved by a mixed integer linear programming approach in coal-fired power plants. The model developed is generic and provides a sustainable environment for the generation of electricity. © 2021 Elsevier Ltd

The pulp and paper industry is critical to global industrial and economic development. Recently, India's pulp and paper industries have been facing severe competitive challenges. The challenges have impaired the environmental performance and resulted in the closure of several operations. Assessment and prediction of the performance of the Indian pulp and paper industry using various parameters is a critical task for researchers. This study proposes a framework for performance assessment and prediction based on Data Envelopment Analysis (DEA), Artificial Neural Networks, and Deep Learning (DL) to assist industry administration and decision-making. We presented a case study based on eight industries to demonstrate the methodology's applicability. This study analyses and predicts industry performance based on sample data observations over 30 years. The result suggests the DEA-DL-based efficiency prediction has an overall MSE of 0.08 compared with the actual efficiency. Furthermore, the efficiency rankings are compared between the three techniques. The results suggest that the integrated DEA-DL method is primarily accurate in most scenarios with the actual values. The findings of this study provide a comprehensive analysis of environmental performance for policymakers. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

The pulp and paper industry is critical to global industrial and economic development. Recently, India's pulp and paper industries have been facing severe competitive challenges. The challenges have impaired the environmental performance and resulted in the closure of several operations. Assessment and prediction of the performance of the Indian pulp and paper industry using various parameters is a critical task for researchers. This study proposes a framework for performance assessment and prediction based on Data Envelopment Analysis (DEA), Artificial Neural Networks, and Deep Learning (DL) to assist industry administration and decision-making. We presented a case study based on eight industries to demonstrate the methodology's applicability. This study analyses and predicts industry performance based on sample data observations over 30 years. The result suggests the DEA-DL-based efficiency prediction has an overall MSE of 0.08 compared with the actual efficiency. Furthermore, the efficiency rankings are compared between the three techniques. The results suggest that the integrated DEA-DL method is primarily accurate in most scenarios with the actual values. The findings of this study provide a comprehensive analysis of environmental performance for policymakers. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.