Explore the vast range of titles available.

The media and consumer research groups have been keeping the Millennials in spotlight for many years now; perhaps it is time to turn some of the attention on Gen Z, which began its foray into mainstream consumption. This exploratory study examines the shopping orientation of Gen Z online shoppers using the generational cohort theory (GCT) as a framework and provides insights to e-retailers to understand how this generation approaches the online shopping. The penetration of Internet and accelerated growth of online shopping have enthused the e-retailers to offer a wide range of goods at greater efficiency than the traditional players. By cluster analysis (K-means) of nine online shopping orientation factors (two were eliminated prior due to low factor loading scores), four segments were identified: (a) ‘Economic-quality seekers’, (b) ‘Convenience shoppers’, (c) ‘Deal hunting-convenience seekers’ and (d) ‘Brand and quality conscious shoppers’, and the study profiled each segment based on the demographic data through chi-square analysis. Finally, implications for online retailers and marketing practitioners are enumerated towards the end of the article.

In supply chain management, the location of facilities, inventory control, and vehicle routing are three key components. This paper incorporates a two-warehouse inventory system into the location- inventory-routing problems (LIRPs) and develops LIRP models with two warehouses in one-level, two-level, and three-level supply chain networks. This study aims to minimize the average total costs of the models by reducing their average costs. To handle these models, two innovative hybrid algorithms, viz. Clarke and Wright—genetic algorithm (CW-GA) and Clarke and Wright—firefly algorithm (CW-FA) are put forward. Computational experiments and sensitivity analyses are conducted to compare the proposed two algorithms with Baron and test the algorithms’ effectiveness and the models’ feasibility. The management implications of this study are presented from two dimensions: model and method. Finally, future research directions and the gap between models and reality are discussed.

It is easier to assume that educated older adults will find digital gadgets or the Internet as simple to use as the young generation does. However, it is not as simple as that. The generation that was not born into the digital world but has had to make an effort to learn to use digital technologies during their middle or late middle age is referred to as Digital Immigrants (DIs). Most of these individuals were forced to adapt to information technologies due to environmental pressure to survive and thrive at their workplace. The objective of this study is to investigate if the proposed “digital divide” that differentiates digital immigrants from digital natives (DNs) exists among e-commerce users in India, and if so, are digital immigrants less likely to adopt and use e-commerce services? Data was collected through a self-administered survey questionnaire from 432 Indian Internet users aged 19 to 65. Multigroup structural equation modeling analysis (M-SEM) of data revealed that DIs and DNs perceive e-commerce services differently. Though digital immigrants find e-commerce services challenging to use, their higher perception of its usefulness propels them to adopt and use e-commerce. This study contributes to the existing body of literature by extending our understanding of the technology adoption behavior of digital immigrants. The study’s implications and the scope for future research are discussed at the end of the article. Plain language summary Purpose: The purpose of this study is to investigate if the proposed “digital divide” that differentiates digital immigrants from digital natives (DNs) exists among e-commerce users in India, and if so, are digital immigrants less likely to adopt and use e-commerce services? Method: Data was collected through a self-administered survey questionnaire from 432 Indian Internet users aged 19 to 65. Multigroup structural equation modeling analysis was used to investigate the proposed hypotheses. Conclusion: Digital immigrants and digital natives differ in their approaches toward e-commerce adoption and use. Implication: Though digital immigrants find e-commerce services challenging to use, their higher perception of its usefulness propels them to adopt and use e-commerce. Limitation: Studies in the past pointed out that the digital divide and generational cohorts cannot be defined merely based on the year of birth. It should be based on a complex mix of shared experiences, life events, and socioeconomic developments during individuals’ growing-up years . Hence, rather than seeing the difference between “digital natives and digital immigrants” as a rigid dichotomy based on age, we should have used the “Technology Readiness Index (TRI)” scale developed by Parasuraman or Digital Natives Assessment Scale (DNAS) to distinguish digital immigrants from digital natives.

Obstacle detection is an essential task for the autonomous navigation by robots. The task becomes more complex in a dynamic and cluttered environment. In this context, the RGB-D camera sensor is one of the most common devices that provides a quick and reasonable estimation of the environment in the form of RGB and depth images. This work proposes an efficient obstacle detection and tracking method using depth images to facilitate quick dynamic obstacle detection. To achieve early detection of dynamic obstacles and stable estimation of their states, as in previous methods, we applied a u-depth map for obstacle detection. Unlike existing methods, the present method provides dynamic thresholding facilities on the u-depth map to detect obstacles more accurately. Here, we propose a restricted v-depth map technique, using post-processing after the u-depth map processing to obtain a better prediction of the obstacle dimension. We also propose a new algorithm to track obstacles until they are within the field of view (FOV). We evaluate the performance of the proposed system on different kinds of data sets. The proposed method outperformed the vision-based state-of-the-art (SoA) methods in terms of state estimation of dynamic obstacles and execution time.

This study introduces a multimodal sentiment analysis system to assess and recognize human pain sentiments within an Internet of Things (IoT)-enabled healthcare framework. This system integrates facial expressions and speech-audio recordings to evaluate human pain intensity levels. This integration aims to enhance the recognition system’s performance and enable a more accurate assessment of pain intensity. Such a multimodal approach supports improved decision making in real-time patient care, addressing limitations inherent in unimodal systems for measuring pain sentiment. So, the primary contribution of this work lies in developing a multimodal pain sentiment analysis system that integrates the outcomes of image-based and audio-based pain sentiment analysis models. The system implementation contains five key phases. The first phase focuses on detecting the facial region from a video sequence, a crucial step for extracting facial patterns indicative of pain. In the second phase, the system extracts discriminant and divergent features from the facial region using deep learning techniques, utilizing some convolutional neural network (CNN) architectures, which are further refined through transfer learning and fine-tuning of parameters, alongside fusion techniques aimed at optimizing the model’s performance. The third phase performs the speech-audio recording preprocessing; the extraction of significant features is then performed through conventional methods followed by using the deep learning model to generate divergent features to recognize audio-based pain sentiments in the fourth phase. The final phase combines the outcomes from both image-based and audio-based pain sentiment analysis systems, improving the overall performance of the multimodal system. This fusion enables the system to accurately predict pain levels, including ‘high pain’, ‘mild pain’, and ‘no pain’. The performance of the proposed system is tested with the three image-based databases such as a 2D Face Set Database with Pain Expression, the UNBC-McMaster database (based on shoulder pain), and the BioVid database (based on heat pain), along with the VIVAE database for the audio-based dataset. Extensive experiments were performed using these datasets. Finally, the proposed system achieved accuracies of 76.23%, 84.27%, and 38.04% for two, three, and five pain classes, respectively, on the 2D Face Set Database with Pain Expression, UNBC, and BioVid datasets. The VIVAE audio-based system recorded a peak performance of 97.56% and 98.32% accuracy for varying training–testing protocols. These performances were compared with some state-of-the-art methods that show the superiority of the proposed system. By combining the outputs of both deep learning frameworks on image and audio datasets, the proposed multimodal pain sentiment analysis system achieves accuracies of 99.31% for the two-class, 99.54% for the three-class, and 87.41% for the five-class pain problems.

Multi-criteria decision-making (MCDM) is now frequently utilized to solve difficulties in everyday life. It is challenging to rank possibilities from a set of options since this process depends on so many conflicting criteria. The current study focuses on recognizing symptoms of illness and then using an MCDM diagnosis to determine the potential disease. The following symptoms are considered in this study: fever, body aches, fatigue, chills, shortness of breath (SOB), nausea, vomiting, and diarrhea. This study shows how the generalised dual hesitant hexagonal fuzzy number (GDHHχFN) is used to diagnose disease. We also introduce a new de-fuzzification method for GDHHχFN. To diagnose a given condition, GDHHχFN coupled with MCDM tools, such as the fuzzy criteria importance through inter-criteria correlation (FCRITIC) method, is used for finding the weight of criteria. Furthermore, the fuzzy weighted aggregated sum product assessment (FWASPAS) method and a fuzzy combined compromise solution (FCoCoSo) are used to rank the alternatives. The alternative diseases are chosen to be malaria, influenza, typhoid, dengue, monkeypox, ebola, and pneumonia. A sensitivity analysis is carried out on three patients affected by different diseases to assess the validity and reliability of our methodologies.

By providing important indicators, financial indices help investors make educated judgements regarding their assets, much like vital sign monitors for the financial markets. The best way for investors to keep up with the market and make strategic adjustments is to keep an eye on these indexes. Researching the most important financial indexes for making educated investing decisions is, thus, quite relevant. Finding the most essential financial indices from an investing standpoint and assigning a weight to each of those indexes are the main goals of this research. A weighted score is derived by combining four financial indices in a Multi-Criteria Decision-Making (MCDM) technique. These objectives are then pursued. Triangular Fuzzy Numbers (TFNs) and the Fuzzy Analytic Hierarchy Process (F-AHP) are used to determine the weights of criteria in this technique. Using these methods together, the research hopes to provide a thorough analysis of the role that different financial indexes have in informing investment choices. This study emphasizes the paramount importance of considering the Price Earning to Growth (PEG) ratio when making investment decisions, followed by the Debt Equity Ratio. Price to Book Value and Dividend Yield, while relevant, carry comparatively less weightage in the overall assessment. Investors are advised to use these insights as a guideline in their financial analysis and decision-making processes.

Site selection for an institute or a university is a challenging task. The selection of sites for setting up a new university depends on multiple criteria. In backward, under privileged area people’s perception towards the co-educational universities and women universities are different. Poor families with their conservative mentality possess inhibitions while sending their girl child to co-educational universities as they have concerns about safety, security and family honor. Hence many attributes which are not so important for co-educational universities are more pertinent for women university. In this research paper, we have considered a model for selecting women’s university sites in different backward locations in the state of West Bengal, India. This model incorporated different types of uncertainty related to site selection. Ten important criteria are chosen for the selection of sites. To capture the uncertainty of the problem, trapezoidal neutrosophic numbers are used along with the Multi-criteria Decision Making tool Analytic Hierarchy Process (AHP) for obtaining criteria weights. Finally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and COmplex PRoportional ASsessment (COPRAS) are applied for ranking of the sites. Comparative and sensitivity analyses are conducted to check the steadiness of the techniques used.

The aim of this study is to consolidate the state of mobile commerce consumer research from 2001 to 2022. Based on a systematic literature review employing a bibliometric technique, this study not only reports the significant contributions of authors and their affiliations but also discusses the evolution of m-commerce research over the last two decades. Examination of annual production trends revealed that publications were on the rise all along; the year 2022 clocked the highest number of publications (53 documents), which further reinforces that the research on this domain is in its blooming season. China is the most contributing country in terms of the number of publications and citations received, followed by the USA. The author Keng-Boon Ooi has been the most productive researcher; his studies continue to be the foundation on which m-commerce consumer research continues to thrive. The analysis of scientific mapping revealed that, although many studies were carried out on mobile commerce adoption intention, the focus of the researchers lately shifted towards studying continuous use intention (since 2018). Further, it was observed that the base theory, the Technology Acceptance Model, which has been widely used for determining antecedents of technology adoption intention, is losing its significance and is being overtly replaced by the Unified Theory of Acceptance and Use of Technology. While the topics “trust, loyalty, satisfaction, mobile banking, UTAUT, continuance intention, perceived enjoyment, and COVID-19” were identified as mother (engine) themes, the keywords “privacy, self-efficacy, social influence, TAM, attitude, and intention to use” became diminishing themes. The following topics have been identified as emerging themes: “Mobile social commerce, Mobile payment, Mobile marketing, Omnichannel, Fintech, and Live streaming commerce”. This study provides useful insights to potential researchers.

PurposeIn response to scholarly calls, the study aims to extend and magnify the existing understanding by unravelling the differential impact of anticipated emotions on green practice adoption intention through a proposed model by integrating anticipated pride and guilt in the same continuum along with values (altruistic, biospheric and egoistic) on an employee's attitude.Design/methodology/approachA self-administered questionnaire was used to collect data randomly from 307 employees and middle-level executives of three subsidiaries of CIL through the simple random sampling (SRS) technique. Data were analysed using structural equation modelling (SEM).FindingsResults demonstrate that anticipated guilt influences individual cognitions and future ecological decision-making through improved attitude and higher concern for the environment while pride influences only through improved attitude. Other than biospheric and altruistic values, anticipated guilt is a direct and important antecedent of concern. Altruistic values are more influential predictors of environmental intentions in comparison to biospheric values. At the same time, environmental concern is more robust in predicting eco-intentions than attitude.Originality/valueIt makes notable difference from other studies by not only exploring the validity of the relationship between values on attitude and environmental concern but has also considered anticipated emotions of pride and guilt together alongside values on the same continuum as an antecedent of environmental attitude and concern towards employees’ green behavioural intention at the workplace. The findings are believed to provide a common consensus on differential effects of different states of emotions on environmental concern and attitude.