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Wearable medical devices (WMDs) are improving people’s health and well-being in a noble way, as these aid in effective personal health monitoring, remote surveillance and overall illness management. Despite its wider applicability and usage, it is prevalent that users discontinue its usage, which presents an obstacle in the proliferation of such vital innovations among the masses. Therefore, relying on the expectation-confirmation model (ECM), this study aims to delve deeper to explain the factors that motivate users to continually use WMDs by incorporating novel variables, namely, health belief, health information accuracy and privacy protection.

The study proposes and tests an extended ECM perspective to predict the continuance intention (CI) of WMDs among users. By using structural equation modelling using SmartPLS, the authors tested the model on Indian people (n = 451) who had an erstwhile experience of using WMDs.

The study results show that confirmation of users’ expectations positively impacts their usefulness and satisfaction towards WMDs. Moreover, satisfaction towards WMDs is the strongest predictor of users’ CI, followed by perceived usefulness. Interestingly, personal factor such as health beliefs reveals a greater influence on perceived usefulness than technological factors like health information accuracy and privacy protection.

The study findings demonstrate the significance of using the expectation-confirmation perspective in technology-based studies in general and WMDs, in particular. This study aids by offering an integrated model of WMD’s continued usage intention for the users, in addition to practical implications for marketers and policymakers.

A paucity of research exists when understanding the predictors of CI for WMDs. This study fills this gap and adds to behavioural literature by offering a noble viewpoint involving an extended ECM perspective.

This study aims to investigate the effects of peers on corporate payout policies in one of the largest emerging markets – India. It also examines the motives for mimicking payout decisions.

The sample is composed of 3,024 non-financial and non-government firms listed on the National Stock Exchange (NSE) and Bombay Stock Exchange (BSE) for the period 1995 to 2020. To encounter the endogeneity problem, the instrumental variable technique based on peer firms' idiosyncratic risk is used to estimate the effects of peers on firms' payout policy. To define peer reference groups, the authors use the basic industry classification of the firms.

The results indicate a significant positive impact of peers on firms' dividend policies in India. A firm with all dividend-paying peers is more likely to declare dividends than the one with no dividend-paying peers. Further, peer effects are found to be more pronounced amongst larger and older firms, thus supporting the rivalry theory of mimicking.

To the best of the authors' knowledge, the present study is the first of its kind that attempts to understand peer effects on payout decisions in an emerging market India, that offers a unique institutional setting. Moreover, the authors extend the existing literature by investigating the peer effects on a firm's payout policies considering various firm-level characteristics, such as growth opportunity, cash holding, financial constraint and profitability, which previous studies have not taken into consideration. These results provide additional insights into the heterogeneity and motives behind peer effects.

Sustainable production (SP) is an efficient and influential approach of production for Indian manufacturing industries as it preserves the social, environmental and economic aspects of production activities altogether. The objective of this research work is to investigate the implementation status of SP practices in Indian manufacturing industries by utilizing empirical survey methodology.

A questionnaire survey methodology was adapted, and the questionnaire was prepared by intense literature survey along with by opinions from experts in the field of SP. This questionnaire was sent to 753 different organizations at different locations across India. This study collected responses from manufacturing industries as per 2021 directory of Confederation of Indian Industries for the duration of 7 week. Top level managers were the target respondents. The study propagated with 242 responses which were observed complete in all respects.

The study identified that though the majority of the organizations are claiming to follow SP practices since long time, they actually are lagging in proper understanding SP practices. Majority of them are implementing it in specific departments in their organization. They are coming across multiple barriers in the implementation of SP practices among which unrecognized financial benefits and lack of proper government policies are prime. The study suggests that the Indian organizations needs feasible framework with adaptable guidelines.

This work is centered towards manufacturing organizations and targets only the leading industrial sectors in India. Thus, the outcomes of this study may not be generalized for all the sectors of Indian industries. Additionally, it can also be assumed that higher number of responses would have contributed to more clear visualization of implementation status of SP practices among Indian industries.

Sustainable approaches in production activities are very lucrative for industries worldwide, due to their advantages. Numerous researchers are also putting their efforts to explore more about various aspects of sustainability. Mostly they are focusing on single or few aspects of SP and its implementation in particular region or country. Very few research works are dedicated to knowing the implementation status of SP in Indian manufacturing industries and they are limited in various aspects. This study presents a dedicated approach to investigate the implementation status of SP practices in Indian manufacturing industries.

This study aims to discover the key performance indicators (KPIs) of the agricultural cold supply chain (ACSC) and analyze their consequences on the performance of ACSC within the bounds of Indian topography.

The KPIs have been explored based on the literature review both in global and Indian context and domain expert's opinions. The interdependency characteristics and cause–effect relationship among the KPIs have been analyzed using a fuzzy decision-making trial and evaluation laboratory (f-DEMATEL) approach.

The findings extracted from the empirical assessment of the problem find strong compliance with the notions of theoretical model assessment. The results highlight that the cost of product waste and operating and performance costs are the two most important performance indicators of an Indian ACSC. Furthermore, governmental policies and regulations and the effectiveness of cold chain (CC) equipment also have a high degree of influencing characteristics on ACSC performance.

To connect the study with practicalities, the assessment of the KPIs is allied with real-time practices by clustering the beliefs of Indian professionals. Therefore, the decision-making behavior of the experts might be influenced by geographical constraints. However, the key findings provide advantages to the ACSC players, a bright hope for future food security and a significant profit for farmers.

The presented paper encompasses various aspects of the ACSC, including theoretical and empirical perspectives exercised to contemplate the system dynamics, which inculcates the essence of the associated practicalities. Thus, this study has various practical contributions relevant to managerial and societal perspectives.

Privacy and security of personal data is the prime concern in any communication. Security algorithms play a crucial role in privacy preserving and are used extensively. Therefore, these algorithms need to be effective as well as energy-efficient. Advanced Encryption Standards (AES) is one of the efficient security algorithms. The principal purpose of this research is to design Energy efficient implementation of AES, as it is one of the important aspects for a step toward green computing.

This paper presents a low voltage complementary metal oxide semiconductor (LVCMOS) based energy efficient architecture for AES encryption algorithm on Field Programmable Gate Array (FPGA) platform. The experiments are performed for five different FPGAs at different input/output standards of LVCMOS. Experiments are performed separately at two frequencies (default and 1.6 GHz).

The comparative study of total on-chip power consumption for different frequency suggested that LVCMOS12 performed best for all the FPGAs. Also, Kintex-7 Low Voltage was found to be the best performing FPGA. At 1.6 GHz frequency, the authors observed 55% less on-chip power consumption when switched from Artix-7 with LVCMOS33 (maximum power consuming combination) to Kintex-7 Low Voltage with LVCMOS12. Mathematical models are developed for the proposed design.

The green implementation of AES algorithm based on LVCMOS standards has not been explored yet by researchers. The energy efficient implementation of AES will certainly be beneficial for society as it will consume less power and dissipate lesser heat to environment.

The purpose of this paper is to develop optimal estimation procedures for the stress-strength reliability (SSR) parameter R = P(X > Y) of an inverse Pareto distribution (IPD).

We estimate the SSR parameter R = P(X > Y) of the IPD under the minimum risk and bounded risk point estimation problems, where X and Y are strength and stress variables, respectively. The total loss function considered is a combination of estimation error (squared error) and cost, utilizing which we minimize the associated risk in order to estimate the reliability parameter. As no fixed-sample technique can be used to solve the proposed point estimation problems, we propose some “cost and time efficient” adaptive sampling techniques (two-stage and purely sequential sampling methods) to tackle them.

We state important results based on the proposed sampling methodologies. These include estimations of the expected sample size, standard deviation (SD) and mean square error (MSE) of the terminal estimator of reliability parameters. The theoretical values of reliability parameters and the associated sample size and risk functions are well supported by exhaustive simulation analyses. The applicability of our suggested methodology is further corroborated by a real dataset based on insurance claims.

This study will be useful for scenarios where various logistical concerns are involved in the reliability analysis. The methodologies proposed in this study can reduce the number of sampling operations substantially and save time and cost to a great extent.

The impact of COVID-19 has caused a recession in economies all over the world. In this context, the current study aims to analyze the prevailing economic scenario using a machine learning approach and suggest sustainable measures to recover the global economy taking the case of Make in India (MII) initiative of developing the economy as a base for the study.

A well-known topic modeling technique – Latent Dirichlet allocation (LDA) algorithm has been employed to extract useful information characterizing the existing state of selected sectors under the MII initiative alongside catalytic policies that have been implemented for the same. The textual data acts as the base of the study upon which suggestions are provided.

The findings obtained suggest that digital transformation will play a key role in concerned sectors to optimize the performance of manufacturing organizations. Additionally, inter-relationship between Key Performance Indicators for the economy's revival is crucial for effective utilization of foreign direct investment resources.

The novel efforts to utilize MII initiative as a case present crucial information which can be used by policy makers and various other stakeholders across the globe to enhance decision-making and draft legislation across different sectors to empower the economy.

The study presents a novel approach to utilize the MII initiative by identifying important measures for crucial sectors and associated policies that have been presented by employing a text mining approach which in itself makes it unique in its contribution to research literature.

New product development (NPD) is necessary for business sustenance and customer satisfaction. Six Sigma and Design for Lean Six Sigma (DLSS) efficiently employ the repetitive stages for NPD, leading to quality performance and profitability. This study aims to map the quality performance through NPD attributes through the Lean methodology.

The data on NPD were collected from 267 respondents from manufacturing companies to map the relationship between Six Sigma and DLSS for NPD. Confirmatory factor analysis was employed to confirm model fit, while structural equation modeling was employed to analyze the empirical data for framework testing. The study included nine variables and fourteen hypotheses identified from the literature.

The statistical results of this study show that NPD attributes such as innovation, marketing, organization, customer, product and technology positively influence the Lean Six Sigma structured improvement process (LSSSIP) and DLSS. Moreover, integrating these attributes in Lean planning enhance quality performance. This empirical investigation's findings indicate that ten of the 14 hypotheses were supported, giving the study a strong foundation.

The data collection was limited to northern India; therefore, the results may not be generalizable to other areas of the world.

NPD involves handling technical issues and factors such as cost, operational bottlenecks, economic changes, competitors' strategy and company policy. This study helps understand the various NPD parameters and their relationship to Lean, which enables an effective NPD implementation strategy.

The current philosophy of NPD calls for a concurrent engineering approach; therefore, the entire organization must be part of this process. This study uses the holistic framework by optimizing NPD with Lean Six Sigma (LSS) principles. The study is unique in that, to date, research does not integrate NPD attributes with the objectives of LSS to develop an efficient NPD implementation strategy.

The current study aims to deliver a consolidated view of environmental sustainability in cold supply chain performance systems (CSCPS), incorporating theoretical and empirical analysis for improving environmental standards. For this purpose, this study firstly aims to explore and analyze the various crucial challenging factors for environmental sustainability in the cold supply chain (CSC). Secondly, it discovers the most effective sustainable strategies for improving the environmental sustainability of CSCPS.

The exploration of the crucial challenging factors and the proposed sustainable strategies have been done using a systematic literature review relevant to the sustainable performance of CSC. At the same time, semi-structured brainstorming sessions were conducted with the domain professionals having an industrial and academic background to finalize the strategies. Empirical analysis has been performed using an intuitionistic fuzzy (IF) based hybrid approach of SWARA and COPRAS methods.

The key findings of the study address that “higher energy consumption during refrigerated transportation and storage” is the most crucial challenge for environmental sustainability in CSC. In addition, “managerial refrain to profit decline due to sustainability implementation” is the second most crucial challenge that hinders the adoption of sustainable practices in CSCs. Meanwhile, the governmental attention to motivating organizations for green adoption and implementation of solar energy-driven refrigeration technologies are the two most important discoveries of the study that might help in improving CSC's environmental performance.

From the implications side, the study enriches and extends the current literature content on CSC sustainability. In addition, it offers sound managerial implications by identifying the challenges that create threats among the management for sustainability adoption and suggesting the most suitable sustainable strategies, which may help the management to raise the environmental performance of their CSC. Besides having various important theoretical and managerial implications for the study, contemplation of only environmental sustainability traits as a broader perspective limits the scope of the study.

The study's main contribution is the exploration of the most crucial challenges imparting obstructions in sustainable development and sustainable strategies, which may get the interest of the CSC players, market leaders, and industrial and academic practitioners working in the domain of CSC sustainability. In addition, this study offers structured theoretical and empirical evidence for CSC's environmental sustainability, thus playing a bridging role between theoretical sustainability concepts and its practical implications in CSC industries.

Sustainable supply chain management (SSCM) ensures integration of socially, environmentally and economically feasible practices in entire supply chain. SSCM principles can be implemented to improve efficiency and productivity of a system by different attributes of the system. The purpose of this article is to identify the most appropriate existing (SSCM) framework that can be implemented suitably in Indian smart manufacturing industries.

Validity and reliability analysis on the existing SSCM frameworks was carried out with the help of empirical data collected using questionnaire survey methodology from various Indian smart manufacturing organizations. The empirical data were gathered from various experts from top- and middle-level management in different smart manufacturing organizations across the country. Further, factor analysis was carried on the collected data to estimate the unidimensionality of each SSCM frameworks. Cronbach's alpha value was used to assess reliability of each framework. Subsequently, the frequency distribution analysis was done to obtain familiar elements in the segregated frameworks based on validity and reliability analysis.

The work observed that only five SSCM frameworks have shown unidimensionality in terms of the elements or constructs. The work further found that these segregated frameworks have not shown sufficiently high level of reliability. Additionally, this work attempted frequency distribution analysis and observed that there were very few elements which were being repeatedly used in numerous frameworks proposed by researchers. Based on the findings of this work, the work concluded that there is acute need of a new SSCM framework for Indian smart manufacturing industries.

This study gathered empirical data from 388 Indian smart manufacturing organizations. Thus, before generalizing the findings of the study across the sectors, there is a possibility of some more explication.

The main purpose of this article is to explore the feasibility of the existing SSCM frameworks in Indian smart manufacturing sector. The study also assumes that the manufacturing managers and executives may have the complete understanding on the existing sustainable manufacturing frameworks and a chance to executing proper suitable framework in the respective manufacturing organization.