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One of the most pernicious and pervasive threats to the integrity of governments, economies, and societies worldwide is the insidious and corrosive influence of corruption. E-government, everywhere, is expected to play a pivotal role in combating corruption. Nevertheless, academic research has yielded varying outcomes. To address the inherent variability, this study employed data from 188 countries to thoroughly investigate the impact of e-government on corruption. Using a path modeling approach and following New Institutional Theory, this study examined the association between the two. We found an inconsistent relationship between e-government and corruption, primarily owing to a nonlinear association between the two and an omitted mediator variable of an institutional nature. Further, accountability, a social instrument, mediates this relationship. Our study offers a fresh perspective on the relationship between e-government and corruption by establishing the crucial role of accountability in the context of corruption. [ABSTRACT FROM AUTHOR]

Purpose: The growth of the global labor force and business analytics has significantly impacted human resource management (HRM). Human resource (HR) analytics is an emerging field that creates value for employees and organizations. By examining the existing studies on HR analytics, the paper systematically reviews the literature to identify active research areas and establish a roadmap for future studies in HR analytics. Design/methodology/approach: A portfolio of 503 articles collected from the Scopus database was reviewed. The study has adopted a Latent Dirichlet allocation (LDA) topic modeling approach to identify significant themes in the literature. Findings: The HR analytics research domain is classified into four categories: HR functions, statistical techniques, organizational outcomes and employee characteristics. The study has also developed a framework for organizations adopting HR analytics. Linking HR with blockchain technology, explainable artificial intelligence and Metaverse are the areas identified for future researchers. Practical implications: The framework will assist practitioners in identifying statistical techniques for optimizing various HR functions. The paper discovers that by implementing HR analytics, HR managers and business partners can run reports, make dashboards and visualizations and make evidence-based decision-making. Originality/value: The previous studies have not applied any machine learning techniques to identify the topics in the extant literature. The paper has applied machine learning tools, making the review more robust and providing an exhaustive understanding of the domain. [ABSTRACT FROM AUTHOR]

Purpose: The COVID-19 pandemic has impacted 222 countries across the globe, with millions of people losing their lives. The threat from the virus may be assessed from the fact that most countries across the world have been forced to order partial or complete shutdown of their economies for a period of time to contain the spread of the virus. The fallout of this action manifested in loss of livelihood, migration of the labor force and severe impact on mental health due to the long duration of confinement to homes or residences. Design/methodology/approach: The current study identifies the focus areas of the research conducted on the COVID-19 pandemic. Abstracts of papers on the subject were collated from the SCOPUS database for the period December 2019 to June 2020. The collected sample data (after preprocessing) was analyzed using Topic Modeling with Latent Dirichlet Allocation. Findings: Based on the research papers published within the mentioned timeframe, the study identifies the 10 most prominent topics that formed the area of interest for the COVID-19 pandemic research. Originality/value: While similar studies exist, no other work has used topic modeling to comprehensively analyze the COVID-19 literature by considering diverse fields and domains. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to uncover and develop explicit knowledge of existing smart communities (SCs) to guide services and business solutions for enterprises and serve community users in a well-thought-out manner. These sagacious frameworks will assist in analyzing trends and reaching out to pre-existing setups with different degrees of expertise. Design/methodology/approach: A systematic overview is provided in this paper to unify insights and competencies toward building SCs; a hybrid analytical approach is used consisting of machine learning and bibliometric analysis. Scopus and Web of Science (WoS) are the primary databases for this purpose. Findings: SCs implement cutting-edge technologies to enhance mobility, elevating information and communication technology (ICT) skills and data awareness while improving business processes and efficiency. This system of SC is an evolution of the conventional method. It provides a foundation for intelligent community services based on individual users and technologies such as the Internet of Things (IoT), artificial intelligence, cloud computing and big data. Manufacturing-based, service-based, retail-based, resource management and infrastructure-based SCs exist in the literature. Originality/value: The paper summarizes a conceptual framework of SCs based on existing works around SCs. To the best of the authors' knowledge, this is the first systematic literature review that uses a hybrid approach of topic modeling and bibliometric analysis to understand SCs better. [ABSTRACT FROM AUTHOR]

In today's business, environment natural and manmade disasters like recent event (Covid 19) have increased the attention of practitioners and researchers to Supply chain vulnerability. Purpose of this paper is to investigate and prioritize the factors that are responsible for supply chain vulnerability. Extant literature review and interviews with the experts helped to extract 26 supply chain vulnerability factors. Further, the relative criticality of vulnerability factors is assessed by analytical hierarchy process (AHP). Critical part supplier; location of supplier; long supply chain lead times; Fixing process owners and mis-aligned incentives in supply chain are identified as the most critical factors among twenty-six vulnerability factors. Research concludes that not only long and complex supply chain but supply chain practices adopted by firms also increase supply chain vulnerability. Relative assessment of vulnerability factors enables professionals to take appropriate mitigation strategies to make the supply chains more robust. This research adds in building a model for vulnerability factors that are internal to supply chain & controllable. [ABSTRACT FROM AUTHOR]

One of the challenges that the hospitality and tourism industry faces is determining the best-rated and ideal hotels for people with customized preferences. Users belong to various demographic groups, and the factors they consider when selecting a hotel depend on their priorities at the time. Therefore, to provide appropriate recommendations tailored to the individual preferences of users, forecasting customer demand is required, for which hotel sales rank prediction models are to be developed. In this regard, the present paper aims to develop a customized hotel recommendation model for sales rank prediction that considers factors like distance from a strategic location, online user ratings, word-of-mouth rating, hotel tariff, and customer reviews, using the aggregated data set of Indian hotels from trivago.com. Results show that the Artificial Neural Network algorithm predicts sales rank better than the Random Forest and Gradient Boosting algorithms. Implications for practice are provided. [ABSTRACT FROM AUTHOR]

One of the significant challenges in the sports industry is identifying the factors influencing match results and their respective weightage. For appropriate recommendations to the team management and the team players, there is a need to predict the match and quantify the important factors for which prediction models need to be developed. The second thing required is identifying talented and emerging players and performing an associative analysis of the important factors to the match-winning outcome. This paper formulates a hybrid machine learning-clustering-associative rules model. This paper also implements the framework for cricket matches, one of the most popular sports globally watched by billions around the world. We predict the match outcome for One day Internationals (ODIs) and Twenty 20 s (T20s) (two formats of Cricket representing fifty over and twenty over versions respectively) adopting state-of-the-art machine learning algorithms, Random Forest, Gradient Boosting, and Deep neural networks. The variable importance is computed using machine-learning techniques and further statistically validated through the regression model. The emerging talented players are identified by clustering. Association rules are generated for determining the best possible winning outcome. The results show that environmental conditions are equally crucial for determining a match result, as are internal quantitative factors. The model is thus helpful for both team management and for players to improve their winning strategy and also for discovering emerging players to form an unbeatable team. [ABSTRACT FROM AUTHOR]

This paper determines the optimal communication by the policymakers in the wake of the Covid-19 crisis. The authors have developed a conceptual framework for optimal communication from the available literature and the opinion of the experts. Further, a hybrid methodology based on Fuzzy AHP and Goal programming has been used for the analysis. Using the conceptual framework it was revealed that there are 72 configurations from which optimal one has to be chosen by the policymakers for communicating optimally during pandemic emergencies like the Covid-19 outbreak. The analysis using hybrid methodology highlighted that FRTD is the optimal configuration out of the 72 possibilities. Considering this option would minimize the effect of the Covid-19 crisis by helping policymakers communicate to the maximum people at the minimum delay. [ABSTRACT FROM AUTHOR]

The paper undertakes a bibliometric study to analyse and identify emerging themes for future research in blockchain technology, focusing on agriculture and supply chain management domains. A sample of 1322 articles from Web of Science for 2015–2020 is the basis of the study. The publications are grouped into five clusters, of which Cluster 1 is consistently dominant in the Information Systems publication landscape. Clusters 2, 3, and 4 are evolving, and topics with scant coverage are primarily in Cluster 5, indicating saturation in the area of interdisciplinary studies. The results provide valuable insights for potential contributors and global audiences. [ABSTRACT FROM AUTHOR]

Purpose: The paper aims to identify the most supply chain resilient company suitable for the customized preferences of partner firms in the context of the Chinese supply chain framework during the COVID-19 pandemic. Design/methodology/approach: A hybrid multicriteria model, i.e. Fuzzy Analytical Hierarchy Process (AHP), was used to assign weights to each criterion, which was subsequently analyzed by three approaches, namely Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Fuzzy DEMATEL (Decision-Making Trial and Evaluation Laboratory), and Evaluation Based on Distance from Average Solution (EDA), to rank the top ten companies in descending order of supply chain resilience. Further, sensitivity analysis is performed to identify the consistency in ranking with variation in weights. The rankings are validated by a novel Ensemble Ranking algorithm and by supply chain domain experts. Findings: The rankings suggest the company "China Energy Construction Group Tianjin Electric Power Construction Co., Ltd" is the most feasible and resilient company, presenting interesting findings to partner firms, and Bosch is the least reliable supply chain company of the ten firms considered, thus presenting interesting findings to partner companies. Practical implications: "Crisis Management Beforehand" is most critical in the current pandemic scenario. This implies that companies need to first prioritize taking proactive steps in crisis management followed by the need to minimize the "Expected impact of pandemic." Performance factors also need to be regulated (sales, supply chain rank and financial performance) to maintain the company's overall reputation. Considering the consistent performance of the China Energy Construction Group Tianjin Electric Power Construction Co., Ltd., it is recommended as the most reliable supply chain firm to forge strategic partnerships with other supply chain stakeholders like suppliers and customers. On the other hand, Bosch is not recommended as a supply chain reliable company and needs to improve its crisis management capabilities to minimize the pandemic impact. Originality/value: The paper aims to identify the most supply chain resilient company suitable for the customized preferences of partner firms in the context of the Chinese supply chain framework during the COVID-19 pandemic. The rankings suggest the company "China Energy Construction Group Tianjin Electric Power Construction Co., Ltd" is the most feasible and resilient company, presenting interesting findings to partner firms, and Bosch is the least reliable supply chain company of the ten firms considered, thus presenting interesting findings to partner companies. [ABSTRACT FROM AUTHOR]

This article, titled "Analyzing online consumer purchase psychology through hybrid machine learning," has been retracted by the Editor-in-Chief and the publisher due to concerns about compromised editorial handling and peer review process, inappropriate references, and being out of scope for the journal. The investigation's findings have led the Editor-in-Chief to lose confidence in the results and conclusions of the article. Some of the authors have agreed with the retraction, while others have not responded to correspondence from the publisher. The online version of the article includes the retracted article as supplementary information. [Extracted from the article]

Purpose: The paper aims to build a customized hybrid multi-criteria model to identify the top three utilities of drones at both personal and community levels for two use cases: firefighting in high-rise buildings and logistic support. Design/methodology/approach: A hybrid multi-criterion model that integrates fuzzy analytical hierarchy process (AHP), Best Worst, fuzzy analytical network process (ANP), fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) is used to compute the criteria weights. The weights are validated by a novel ensemble ranking technique further whetted by experts at the community and personal levels to two use cases. Findings: Drones' fire handling and disaster recovery utilities are the most important to fight fire in high-rise buildings at both personal and community levels. Similarly, drones' urban planning, municipal works and infrastructure inspection utilities are the most important for providing logistics support at personal and community levels. Originality/value: The paper presents a novel multi-criteria approach, i.e. ensemble ranking, by combining the criteria ranking of individual methods – fuzzy AHP, Best-Worst, fuzzy ANP and fuzzy DEMATEL – in the ratio of optimal weights to each technique to generate the consolidated ranking. Domain experts also validate this ranking for robustness. This paper demonstrates a viable methodology to quantify the utilities of drones and their capabilities. The proposed model can be recalibrated for different use case scenarios of drones. [ABSTRACT FROM AUTHOR]

Purpose: Businesses need to make quick decisions and adjustments to fulfill the growing online demand. Previous studies examined various factors affecting the online sales performance of products such as books, electronics and movies; however, they paid limited attention toward the local brand clothing products. The current study investigates the importance of different kinds of seller-generated and consumer-generated signals such as price, discount, product ratings, review volume, review sentiment, number of questions and interaction between some of these factors for predicting the sales performance of clothing products. Design/methodology/approach: The multiple linear regressions has been employed to investigate the influence of various predictor variables on sales performance. The study also examines the importance of these predictor variables by using different machine learning models, including random forest (RF), neural networks and support vector regression (SVR). Findings: The findings of the study emphasize the importance of price and discount rates offered on the product. The quantitative characteristics of reviews, such as review volume and average rating, have been found to be more important predictors than sentiment strengths. However, the sentiment strength of reviews with higher helpfulness scores plays a significant role in predicting sales performance. Originality/value: The study highlights the varying importance of seller-based and consumer-based signals in predicting sales performance. It also investigates the interaction effect of these two kinds of signals. The consumer-generated signals have been further divided into two components based on social influence theory, and the interaction effects of these components have also been examined. [ABSTRACT FROM AUTHOR]

This article provides a bibliometric analysis of the direction of research relating to COVID-19 during the first year after the virus was first identified as a potential threat to public health. The analysis explores the number and topics of studies performed, along with patterns related to authorship, organisations and countries of origin. A sample of 2531 articles identified from the Web of Science is the basis of the study. The publications were grouped into five clusters based on their main focus. The results provide an insight into the response of the scientific community during the first few months of the crisis. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to develop two sentiment indices sourced from news stories and corporate disclosures of the firms in the National Stock Exchange NIFTY 50 Index by extracting sentiment polarity. Subsequently, the two indices would be compared for the predictive accuracy of the stock market and stock returns during the post-digitization period 2011–2018. Based on the findings this paper suggests various options for financial strategy. Design/methodology/approach: The news- and disclosure-based sentiment indices are developed using sentiment polarity extracted from qualitative content from news and corporate disclosures, respectively, using qualitative analysis tool "N-Vivo." The indices developed are compared for stock market predictability using quantitative regression techniques. Thus, the study is conducted using both qualitative data and tools and quantitative techniques. Findings: This study shows that the investor is more magnetized to news than towards corporate disclosures though disclosures contain both qualitative as well as quantitative information on the fundamentals of a firm. This study is extended to sectoral indices, and the results show that specific sectoral news impacts sectoral indices intensely over market news. It is found that the market discounts information in disclosures prior to its release. As disclosures in quarterly statements are delayed information input, firms can use voluntary disclosures to reduce the communication gap with investors by using the internet. Managers would do so only when the stock price is undervalued and tend to ignore the market and the shareholder in other cases. Otherwise, disclosure sentiment attracts only long horizon traders. Practical implications: Finance managers need to improve disclosure dependence on investors by innovative disclosure methodologies irrespective of the ruling market price. In this context, future studies on investor sentiment would be interesting as they need to capture man–machine interactions reflected in market sentiment showing the interplay of human biases with machine-driven decisions. The findings would be useful in developing the financial strategy for protecting firm value. Originality/value: This study is unique in providing a comparative analysis of sentiment extracted from news and corporate disclosures for explaining the stock market direction and stock returns and contributes to the behavioral finance literature. [ABSTRACT FROM AUTHOR]

Purpose: The new coronavirus is a highly infectious disease with mutating variants leading to pervasive risk around geographies and public health system. The economy has been suffering due to the strategic lockdown adopted by the local administrative bodies, and in most of the countries, it is further leading to a major wave of unemployment with millions of job and business losses affecting the hotels, travel and tourism industry widely. To attain a sustainable business in the post-pandemic situations, the industry now must think of information system approaches to convince tourists to feel safe with the most hygienic hospitality and services to be offered in any property. The key aspect of the study is to provide the impact of new-age AI-driven technology solutions that will dominate the future direction of the modernized hospitality industry promising robust health-safety measures in a hotel, and further help create sustainable business and leisure travel facilities to cope with post-epidemic scenarios. Design/methodology/approach: The study emphasizes to provide a robust technology-oriented framework based on a mixed research method that would help hotels to adopt and implement new-age AI-driven solution within the hotel premise to serve customers with at most hygiene, contactless service and thereafter, aiming for faster recovery of businesses and regaining customer trust to fuel booking intent in the post-epidemic scenario. Findings: The paper provides a technology-focused solution that would impact hotel industries' post-pandemic scenario. The study contributes to helping boost the tourism industry using information management solutions such as biosensors, robotic room services and contactless hosting. The findings show the adoption of robots/RPA solutions and Biosensors by the industry will be a disruptive paradigm shift. Originality/value: The study expands the scope of research in information technology and management with a focus on the hospitality industry while contributing to new factors impacting customer buying behavior in the industry. [ABSTRACT FROM AUTHOR]

Purpose: This study extends the literature on the effectiveness of e-learning by investigating the role of student engagement on perceived learning effectiveness (PLE) in the context of Indian higher education. Further, the impact of personal factors (Internet self-efficacy (ISE)) and environmental factors (information, system and service quality parameters) on various dimensions of student engagement (behavioral, emotional and cognitive) is studied through the lens of social cognitive theory (SCT). Design/methodology/approach: An online management information systems (MIS) course is delivered to a batch of 412 postgraduate students. An online survey was conducted to measure the factors affecting their PLE. In addition to the survey, a summative assessment is conducted to evaluate the students in terms of their marks to assess their achievements (actual learning). Covariance-based structural equation modeling (CB-SEM) is used to validate the developed research model. Findings: It is discovered that the IS (information system) quality parameters (environmental factors) positively impact PLE. The ISE affects the PLE through the mediating effect of all the dimensions of student engagement. Furthermore, there exists a positive relationship between PLE and student marks. Originality/value: This study develops a research model using personal and environmental factors to understand PLE through the lens of SCT and then empirically validates it. The psychological process from the students' ISE to the PLE is explained through the mediating effects of various dimensions of engagement. Further, it is found that the PLE is positively related to student marks. [ABSTRACT FROM AUTHOR]

Today, the advent of social media has provided a platform for expressing opinions regarding legislation and public schemes. One such burning legislation introduced in India is the Citizenship Amendment Act (CAA) and its impact on the National Citizenship Register (NRC) and, subsequently, on the National Population Register (NPR). This study examines and determines the opinions expressed on social media regarding the act through a Twitter analysis approach that extracts nearly 18,000 tweets during 10 days of introducing the scheme. The analysis revealed that the opinion was neutral but tended to a more negative reaction. Consequently, recommendations on improving public perception about the scheme by suitable for interpreting the Act to the public are provided in the paper. [ABSTRACT FROM AUTHOR]

The stock market is an aggregation of investor sentiment that affects daily changes in stock prices. Investor sentiment remained a mystery and challenge over time, inviting researchers to comprehend the market trends. The entry of behavioral scientists in and around the 1980s brought in the market trading's human dimensions. Shortly after that, due to the digitization of exchanges, the mix of traders changed as institutional traders started using algorithmic trading (AT) on computers. Nevertheless, the effects of investor sentiment did not disappear and continued to intrigue market researchers. Though market sentiment plays a significant role in timing investment decisions, classical finance models largely ignored the role of investor sentiment in asset pricing. For knowing if the market price is value-driven, the investor would isolate components of irrationality from the price, as reflected in the sentiment. Investor sentiment is an expression of irrational expectations of a stock's risk-return profile that is not justified by available information. In this context, the paper aims to predict the next-day trend in the index prices for the centralized Indian National Stock Exchange (NSE) deploying machine learning algorithms like support vector machine, random forest, gradient boosting, and deep neural networks. The training set is historical NSE closing price data from June 1st, 2013-June 30th, 2020. Additionally, the authors factor technical indicators like moving average (MA), moving average convergence-divergence (MACD), K (%) oscillator and corresponding three days moving average D (%), relative strength indicator (RSI) value, and the LW (R%) indicator for the same period. The predictive power of deep neural networks over other machine learning techniques is established in the paper, demonstrating the future scope of deep learning in multi-parameter time series prediction. [ABSTRACT FROM AUTHOR]

Background: In medical diagnosis and clinical practice, diagnosing a disease early is crucial for accurate treatment, lessening the stress on the healthcare system. In medical imaging research, image processing techniques tend to be vital in analyzing and resolving diseases with a high degree of accuracy. This paper establishes a new image classification and segmentation method through simulation techniques, conducted over images of COVID-19 patients in India, introducing the use of Quantum Machine Learning (QML) in medical practice.Methods: This study establishes a prototype model for classifying COVID-19, comparing it with non-COVID pneumonia signals in Computed tomography (CT) images. The simulation work evaluates the usage of quantum machine learning algorithms, while assessing the efficacy for deep learning models for image classification problems, and thereby establishes performance quality that is required for improved prediction rate when dealing with complex clinical image data exhibiting high biases.Results: The study considers a novel algorithmic implementation leveraging quantum neural network (QNN). The proposed model outperformed the conventional deep learning models for specific classification task. The performance was evident because of the efficiency of quantum simulation and faster convergence property solving for an optimization problem for network training particularly for large-scale biased image classification task. The model run-time observed on quantum optimized hardware was 52 min, while on K80 GPU hardware it was 1 h 30 min for similar sample size. The simulation shows that QNN outperforms DNN, CNN, 2D CNN by more than 2.92% in gain in accuracy measure with an average recall of around 97.7%.Conclusion: The results suggest that quantum neural networks outperform in COVID-19 traits' classification task, comparing to deep learning w.r.t model efficacy and training time. However, a further study needs to be conducted to evaluate implementation scenarios by integrating the model within medical devices. [ABSTRACT FROM AUTHOR]

Purpose: A composite sentiment index (CSI) from quantitative proxy sentiment indicators is likely to be a lag sentiment measure as it reflects only the information absorbed in the market. Information theories and behavioral finance research suggest that market prices may not adjust to all the available information at a point in time. This study hypothesizes that the sentiment from the unincorporated information may provide possible market leads. Thus, this paper aims to discuss a method to identify the un-incorporated qualitative Sentiment from information unadjusted in the market price to test whether sentiment polarity from the information can impact stock returns. Factoring market sentiment extracted from unincorporated information (residual sentiment or sentiment backlog) in CSI is an essential step for developing an integrated sentiment index to explain deviation in asset prices from their intrinsic value. Identifying the unincorporated Sentiment also helps in text analytics to distinguish between current and future market sentiment. Design/methodology/approach: Initially, this study collects the news from various textual sources and runs the NVivo tool to compute the corpus data's sentiment polarity. Subsequently, using the predictability horizon technique, this paper mines the unincorporated component of the news's sentiment polarity. This study regresses three months' sentiment polarity (the current period and its lags for two months) on the NIFTY50 index of the National Stock Exchange of India. If the three-month lags are significant, it indicates that news sentiment from the three months is unabsorbed and is likely to impact the future NIFTY50 index. The sentiment is also conditionally tested for firm size, volatility and specific industry sector-dependence. This paper discusses the implications of the results. Findings: Based on information theories and empirical findings, the paper demonstrates that it is possible to identify unincorporated information and extract the sentiment polarity to predict future market direction. The sentiment polarity variables are significant for the current period and two-month lags. The magnitude of the sentiment polarity coefficient has decreased from the current period to lag one and lag two. This study finds that the unabsorbed component or backlog of news consisted of mainly negative market news or unconfirmed news of the previous period, as illustrated in Tables 1 and 2 and Figure 2. The findings on unadjusted news effects vary with firm size, volatility and sectoral indices as depicted in Figures 3, 4, 5 and 6. Originality/value: The related literature on sentiment index describes top-down/ bottom-up models using quantitative proxy sentiment indicators and natural language processing (NLP)/machine learning approaches to compute the sentiment from qualitative information to explain variance in market returns. NLP approaches use current period sentiment to understand market trends ignoring the unadjusted sentiment carried from the previous period. The underlying assumption here is that the market adjusts to all available information instantly, which is proved false in various empirical studies backed by information theories. The paper discusses a novel approach to identify and extract sentiment from unincorporated information, which is a critical sentiment measure for developing a holistic sentiment index, both in text analytics and in top-down quantitative models. Practitioners may use the methodology in the algorithmic trading models and conduct stock market research. [ABSTRACT FROM AUTHOR]

Social media has been engulfed with one burning epidemic – the Coronavirus impacting both the society and businesses. This study examines the impact of social media opinions through a multi-channel social media approach encompassing Twitter, Facebook and YouTube from February 10th to March 10th, 2020. A country-wise analysis is performed for both major developed and developing economies, like the US, India, China, Italy and Iran. The analysis reveals that public opinion is unanimously negative. The impact of the pandemic on businesses, society and education has been highlighted to help formulate country-specific strategies to mitigate stigmatization of people impacted by the virus. [ABSTRACT FROM AUTHOR]

The paper aims to build a hybrid personalized multicriteria model in the Indian transportation industry to identify the most feasible transport mode suitable for commuters' customized preferences. A hybrid multicriterion model, i.e., Fuzzy Analytical Hierarchy Process (AHP), was used to compute the criteria weights, which were subsequently analyzed by three approaches, namely, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Fuzzy TOPSIS, Evaluation Based on Distance from Average Solution (EDA), and Interpretive Ranking Process (IRP). The case of an Indian metropolitan city, Hyderabad, is taken to illustrate the proposed approach. The paper highlights the following transport modes: metropolitan train (unconventional mode) and conventional modes such as the car, public bus transport, and bikes for Hyderabad. Furthermore, sensitivity analysis is performed to identify the consistency in ranking with variation in weights, and the Ensemble Ranking and transportation experts validate the rankings. [ABSTRACT FROM AUTHOR]

The paper aims to build a hybrid personalized multicriteria model in the Indian transportation industry to identify the most feasible transport mode suitable for commuters' customized preferences. A hybrid multicriterion model, i.e., Fuzzy Analytical Hierarchy Process (AHP), was used to compute the criteria weights, which were subsequently analyzed by three approaches, namely, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Fuzzy TOPSIS, Evaluation Based on Distance from Average Solution (EDA), and Interpretive Ranking Process (IRP). The case of an Indian metropolitan city, Hyderabad, is taken to illustrate the proposed approach. The paper highlights the following transport modes: metropolitan train (unconventional mode) and conventional modes such as the car, public bus transport, and bikes for Hyderabad. Furthermore, sensitivity analysis is performed to identify the consistency in ranking with variation in weights, and the Ensemble Ranking and transportation experts validate the rankings. [ABSTRACT FROM AUTHOR]

With increasing varieties and products, management of limited shelf space becomes quite difficult for retailers. Hence, an efficient product assortment, which in turn helps to plan the organization of various products across limited shelf space, is extremely important for retailers. Products can be distinguished based on quality, price, brand, and other attributes, and decision needs to be made about an assortment of the products based on these attributes. An efficient assortment planning improves the financial performance of the retailer by increasing profits and reducing operational costs. Clustering techniques can be very effective in grouping products, stores, etc. and help managers solve the problem of assortment planning. This paper proposes data mining approaches for assortment planning for profit maximization with space, and cost constraints by mapping it into well-known knapsack problem. [ABSTRACT FROM AUTHOR]

The evolution of information technology has resulted in increasingly fragmented digital media and multiple information channels. Organizations can develop comprehensive insights into consumer behavior and preferences by evaluating customers' perceptions of the various Internet channels that are available. Such insights can be used to identify which information channels can be employed to effectively reach and communicate with a target market and, thus, to optimize marketing strategies. This paper commences with a comprehensive literature review of existing research on consumer information search patterns and strategies, with a particular focus on Internet channels. The literature review is employed to develop a set of criterion by which consumer search preferences can be better understood. This criterion is subsequently used to develop a optimization model for organization that can effectively align marketing practices with customers' search processes and preferences during their pre-purchase information search. [ABSTRACT FROM AUTHOR]

The disparity in consumer and organization preferences of information channels is a major concern. Further, making decisions in the presence of a wide range of conflicting criteria through the use of a multiple criteria decision-making (MCDM) approach has gained increased prominence in recent years and research in this area has become an important consideration for business operations that involve dealing with complex decision problems. This paper describes how an integrated approach can be applied to a decision-making problem that combines a fuzzy analytical hierarchy process (AHP) and TOPSIS for identifying preferences consumers of information search channels according to demographic factors such as gender. [ABSTRACT FROM AUTHOR]

The article presents a case study on internet marketing channel preferences of organizations for customer management. According to the article, the study has been conducted using a hybrid Multi-Criteria Decision Making (MCDM) model, the method includes Fuzzy DEMATEL, Fuzzy AHP and Geometric Ordinal Consensus Index (GOCI).