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Shopping motivation has been extensively explored in traditional marketing context but less in online shopping. Utilitarian attribute of online shopping is one of the success factors of e-retailing. The purpose of this paper is to explore the utilitarian shopping motivation in online shopping and validate these factors through confirmatory factor analysis (CFA) in the Indian context.

The data were collected from the 183 regular online shoppers. The collected data were analysed through exploratory factor analysis and CFA using Amos 22 version.

This paper explored the utilitarian shopping motivation of online shopping into five factors, i.e. information availability, accessibility, searchability, product availability and convenience.

The research is conducted on online shoppers to measure their utilitarian motives of shopping online. Further research may be conducted to investigate other motives of online shopping. The sample is taken from Central India which is not enough to generalise the findings. Research may be conducted in other regions and on different segment of respondents to know the impact of geographical variance on utilitarian perspectives of online shoppers.

This paper addresses a significant input in online retailing platform, and it will contribute to the theory of shopping motivations in online shopping context and provide valuable inputs for developing online marketing strategies. The findings of the study also aid to retail practitioners in analysing retailing’s current transformation due to digitalization.

The purpose of this study is to identify distinct segments of apparel shoppers based on their fashion shopping orientation. The difference among the segments based on mall attractive dimension is also examined.

The data were collected through mall intercept survey from the mall shoppers. Samples of 375 respondents are used for data analysis purpose. Exploratory factor analysis is used to extract the factors of fashion shopping orientation and mall attractive dimensions while K-means cluster analysis is applied to identify the segments.

This study resulted in three factors of fashion orientation of apparel shoppers, i.e. fashion involvement, variety seeking and economic value, and four factors of mall attractive dimensions: convenience, entertainment, atmosphere and architecture design. Based on these factors, this study came out with three distinct segments of fashion shoppers: pragmatic shoppers, variety seeking shoppers and highly fashioned shoppers. These three segments are attracted towards the mall dimension differently.

This paper presents the three distinct profiles of fashion shoppers based on their fashion shopping orientation and mall attractive dimensions. The findings of this study may help retailers and mall developers to target mall visitors appropriately.

This paper explores the design dimensions that foster identity construction, legitimation, and growth of digitally mediated platform ecosystems.

A midrange theorizing approach was adopted to assimilate and induct the extant literature on ecosystems, platform business models and innovation, yielding testable propositions on ecosystem design for empirical testing.

The paper suggests that decentralized governance, partner engagement and shared context are three dimensions of criticality for designing a distinct platform ecosystem. These design dimensions nurture interactions, transactions, relationships between platform participants and external actors to make ecosystems authentic and legitimate. Decentralization is relevant for inducing flexibility and autonomy of participants on the platform. Engagement impacts the intensity of relationships the platform has with other firms in the ecosystem, while shared context is essential for creating knowledge and harnessing innovation on the platform.

The paper identifies a set of three testable propositions on ecosystem design for further empirical analysis by ecosystem researchers.

To achieve future readiness, organizations must become resilient to the market environment. With that intent, traditional businesses are revising their operating models to become more collaborative, integrative and efficient. Adoption of digital initiatives for redesigning towards platform ecosystems will make traditional models more relevant as markets evolve. But as a new organization form, platform ecosystem faces the challenge of legitimacy. Author suggests that managers use the organization design lever to meet the challenge.

Emergence of platform-based businesses and transformation of existing models to platform ecosystems are impacting today's competitive environment. During initial phases of evolution, ecosystems aim for identity and legitimacy. The authors contribute to organizational aspects of the platform ecosystem design literature by identifying decentralization of governance, engagement and shared context as dimensions of criticality for future-ready platforms. Secondly, these dimensions are associated with identity and legitimation of platform ecosystems. Decentralization is relevant for supply-side producers of goods and services on the platform, engagement has impact on both supply and demand-side participants of platforms, and shared context is essential for knowledge creation and harnessing innovation.

Emerging economy firms seek strategic assets through cross-border acquisitions (CBAs) to upgrade their capabilities. The paper explores the relation between emerging economy firms' investments in CBAs and subsequent investments in domestic R&D. It investigates the underlying mechanism that links a firm's decision to pursue CBAs and the outcomes from the CBAs. The main idea behind the study is that firms have higher possibility of creating value from cross-border acquisitions when they simultaneously invest in domestic R&D though both investments are constrained by financial and managerial resources.

The hypotheses are tested on a panel data set of 296 Indian firms over a period of 13 years (2003–2015). The authors use a two-stage Heckman procedure for testing their hypotheses. In the first stage, a probit model predicts the probability of a firm being a cross-border acquirer. The second stage model is estimated by a pooled-data GLS (generalized least squares) regression technique.

The authors find a nonlinear (inverted U-shaped) relationship between firm's investments in CBAs and domestic R&D. This suggests a complementary relation between investments in CBAs and a firm's domestic R&D at lower levels of investments in CBAs. At higher levels of investments in CBAs, CBA investments begin to substitute for firm's domestic R&D investments. For firms with higher international product-market experience and those operating in the hi-tech industry, the relationship between investments in CBAs and domestic R&D is complementary even at higher levels of CBA investments.

The study highlights the role of an emerging market firm's investment in domestic R&D as a link between the decision to invest in CBAs and related outcomes thereof. Emerging market firms face resource constraints while pursuing simultaneous investments in CBAs and R&D, but investment in R&D is essential for realizing the acquisition objectives. The authors also establish the significance of industry context and experiential learning in deciding the allocation of resources between CBAs and internal R&D.

The paper aims to develop a novel method for the classification of different physical activities of a human being, using fabric sensors. This method focuses mainly on classifying the physical activity between normal action and violent attack on a victim and verifies its validity.

The system is realized as a protective jacket that can be worn by the subject. Stretch sensors, pressure sensors and a 9 degree of freedom accelerometer are strategically woven on the jacket. The jacket has an internal bus system made of conductive fabric that connects the sensors to the Flora chip, which acts as the data acquisition unit for the data generated. Different activities such as still, standing up, walking, twist-jump-turn, dancing and violent action are performed. The jacket in this study is worn by a healthy subject. The main phases which describe the activity recognition method undertaken in this study are the placement of sensors, pre-processing of data and deploying machine learning models for classification.

The effectiveness of the method was validated in a controlled environment. Certain challenges are also faced in building the experimental setup for the collection of data from the hardware. The most tedious challenge is to collect the data without noise and error, created by voltage fluctuations when stretched. The results show that the support vector machine classifier can classify different activities and is able to differentiate normal action and violent attacks with an accuracy of 98.8%, which is superior to other methods and algorithms.

This study leads to an understanding of human physical movement under violent activity. The results show that data compared with normal physical motion, which includes even a form of dance is quite different from the data collected during violent physical motion. This jacket construction with woven sensors can capture every dimension of the physical motion adding features to the data on which the machine learning model will be built.

Unlike other studies, where sensors are placed on isolated parts of the body, in this study, the fabric sensors are woven into the fabric itself to collect the data and to achieve maximum accuracy instead of using isolated wearable sensors. This method, together with a fabric pressure and stretch sensors, can provide key data and accurate feedback information when the victim is being attacked or is in a normal state of action.

304 SS substrates have been covered with polymeric class low melting barriers, like polyurethane, acrylate and epoxy with and without incorporation of eco‐friendly (non‐toxic) ceramic particular of antifouling origin. The results of corrosion resistance tests are encouraging. Other physical parameters like hardness, adhesion, scratch resistance are also studied, for these synergistically organized low melting point barrier layers.

The purpose of this paper is to develop an interpretive structural modeling (ISM) of barriers related to viability of Power Distribution Companies (discoms) in India.

Feedback from the Experts of Indian power sector has been taken as the basis to develop the model for barriers to viability of discoms, where major barriers have been identified through extent review of literature and through discussions with experts in the power sector keeping the viability of discoms in focus, and the hierarchical structure of barriers has been developed using ISM.

An interpretive structural model has been developed for discom-related factors (barriers) affecting its viability. The hierarchical structure portrays the impeding factors of viability and showcases that lack of regulatory effectiveness, inadequate tariffs and lack of government’s expenditures on power sector are the key barriers.

This paper has implications for both practitioners and academics. For practitioners, it provides an indicative list of major barriers affecting the viability of Indian discoms. For academics, the methodology used provides a mechanism to conduct an exploratory study by identifying the key variables of interest and emphasizing their interactions through hierarchical structures.

The proposed model for barriers to viability of discoms developed through qualitative modeling technique is a pioneering effort altogether in the context of power distribution companies in India. Understanding contextual relationships among key barriers to viability of discom’s is neglected in existing literature, and this paper makes a contribution in this regard.

The purpose of this paper was to examine the perceived outcomes of e-learning by identifying key attributes affecting user (learner) satisfaction in higher education institutes.

A conceptual model considering user satisfaction as a key construct was developed through critical literature review and expert opinion. The model is empirically validated using confirmatory factor analysis and structural equation model in the context of higher education institutions. A sample of 802 users comprising of engineering and management students has been used for the analysis.

Course attributes, system attributes, interactive attributes and instructor attributes were found to have an influence significantly on user satisfaction. Instructor attributes were the topmost significant contributor followed by the course attributes.

Delivery of educational programs through e-learning platforms has increasingly gained traction throughout the world owing to its locational, time and convenience-related facets. Further, the ongoing global pandemic has catalysed acceptance of e-learning platforms thus attracting large number of learners and teachers for facilitating the teaching-learning process. This paper is a novel attempt to identify the existing gaps in teaching-learning process in the context of e-learning.

This study is original and provides new insights into how e-learning platforms and higher education institutions can ensure higher user satisfaction and learning in current challenging times. This paper will also be of interest to policymakers.

The purpose of this paper is to formulate heavy-duty lithium complex grease using low molecular weight poly tetra fluoro ethylene (PTFE) micro-particles as extreme pressure (EP) additive manufactured by E-beam scissoring and ultra-high speed grinding process of pre-sintered PTFE scrap.

Lithium complex grease is formulated with PTFE micro-particles, and optimum treat rate was studied by standard bench tests by ASTM D 2266 and IP-239 for tribological properties and compared with commercially available Molybdenum Di sulphide (Moly)-based lithium complex grease. The performance of the grease was further evaluated by a cyclic load test at varying speeds and loads to simulate the operational field conditions.

The lithium complex PTFE grease was manufactured using PTFE micro-particles as EP additive. The PTFE micro-particles dispersed in the lithium complex grease significantly improve the anti-wear performance and load bearing properties. Further, when the product was tested under a cyclic load conditions on standard tribological bench test against commercially available Moly lithium complex grease, shows stable anti-wear properties and reduced coefficient of friction.

The low molecular weight PTFE micro-particles, manufactured in the in-house electron beam (E-beam) and ultra-high speed micronizer facility from a pre-sintered PTFE scrap has been used as EP additive for grease applications for the first time. The results on the cyclic load tests indicate significant performance improvement in retaining the anti-wear and friction properties. Thus, value addition is done in formulating superior performance grease and evaluating under cyclic load conditions similar to field operating conditions and also in creating value added additives by converting the pre-sintered PTFE scarp which is environmental hazard due to poor biodegradability, creating a cyclic economy and a sustainable concept.

The purpose of this study is to explore the effect on distress of senior citizen’s anxiety levels in quarantine during the second wave of COVID-19 pandemic. This is a descriptive study, in which data were collected using the online survey method, a sociodemographic form, a semi-structured data form for second wave COVID-19, the anxiety-level scale and the distress scale.

Data were collected during the period when a curfew was imposed for the senior citizen. Data were analyzed using a structural equation model. According to the structural equation model, anxiety was determined as a predictor of distress.

The anxiety levels of the senior citizen who were 60–69 years old, female, single; had inadequate knowledge about the pandemic; and had not encountered a similar pandemic before considered that family bonding was affected negatively, so they became alone and reported that they became bored, exhausted and distressed during the pandemic, which increased their distress levels. Anxiety affects distress in the senior citizens.

One of the policy implications of this study is that governments should provide behavioral support to citizens during a pandemic. For example, short-term home-based psychological interventions should be developed to reduce the negative effects of the second wave of COVID-19 pandemic on mental health.

The anxiety levels increased distress levels for those senior citizens who considered their information about the second wave of COVID-19 insufficient; had hyper-emotionality; longed for their families; and felt tense, overwhelmed and lonely during the pandemic. In addition, factors such as constraints of flexibilities, prevention of socialization and decreased physical movements also affect distress in senior citizens.