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The present study aims to verify whether there is a positive (negative) role being played by the institutional investors on the loss-making companies' performance.

The authors employ panel data regression and two-step system generalised method of moments (SYS-GMM) to test the above objective.

The empirical results clearly show that no positive relation is found between institutional investors and loss-making companies' performance.

The findings of the study might have significant implications for firms to improve the firms' operational performance [return on assets (ROA)]. Also, the firm's financial performance [return on equity (ROE)] could be improved by increasing profitability which will reflect in the share prices of the firms whereby the performance can build the investors' confidence over the firm. Market performance (Tobin's Q) could be increased by providing more attractive offers and discounts to customers to capture the business opportunities available in the market.

The overall findings might have for reaching implications in the manufacturing sector with regard to allowing (disallowing) institutional investors.

The results of the study may help both companies and institutional investors.

This is the maiden attempt to study whether loss-making companies could be positively (negatively) impacted by the arrival of sophisticated institutional investors [foreign institutional investors (FIIs) and domestic institutional investors (DIIs)]. Further, this study is largely different from previous studies in terms of using new variables which are related to firm characteristics and valuation multiples. Further, seeing if the institutional investors tend to enhance the firm performance is curious.

The COVID-19 situation affected the whole landscape of retailing in India and around the world. However, some businesses have used the pandemic-related difficulties into opportunities. E-tailing is one of the ways that helped people in India to continue shopping their essential products and choosing their luxury products without making any physical visits during the lockdown. This research understands the current situation through an observation study and suggests the e-tailing model suitable during the COVID-19 and beyond.

We used secondary data to make the observational study. We also conducted two case studies and interviews with grocery shops and an automotive company.

This research suggests a simple collaborative e-tailing model combining all supply chain players to reduce people’s movement, timely delivery and enhanced service to meet customers demand during the lockdown period.

This paper has considered two real cases for discussion and also obtained information from public domain. The proposed model has been discussed with the case companies, and it hoped to support business planning for online services.

Experiencing more frequent, system-wide disruptions, such as pandemics and geopolitical conflicts, supply chains can be largely destabilized by a lack of materials, services or components. Supply chain resilience (SCRES) constitutes the network ability to recover after and survive during such unexpected events. To enhance the understanding of SCRES as a system-wide quality, this study tests a comprehensive SCRES model with data from multiple industries.

The study proposes a theoretical framework conceptualizing SCRES as system quality, extending the classical proactive/reactive taxonomy by multiple system states consisting of the supply system properties, behaviors and responses to disruptions. Underlying hypotheses were tested using an online survey. The sample consists of 219 responses from German industries. Maximum likelihood structural equation modeling (ML-SEM) and moderation analysis were used for analyzing the survey data. The study was particularly designed to elaborate on supply chain theory.

Two pathways of parallel SCRES building were identified: proactive preparedness via anticipation and reactive responsiveness via agility. Both system responses are primarily built simultaneously rather than successively. The present study further provides empirical evidence on the central role of visibility and velocity in achieving comprehensive SCRES, while flexibility only exerts short-term support after a disruption. The study additionally points to potential “spillover effects” such as the vital role of proactive SCRES in achieving reactive responsiveness.

The present study confirms and expands existing theories on SCRES. While stressing the multidimensionality of SCRES, it theorizes the (inter-)temporal evolution of a system and offers practical guidelines for SCRES building in various industrial contexts. It thus supports the transformation toward more resilient and viable supply chains, contributing to the increasing efforts of middle-range theory building to achieve an overarching theory. The study also points to potential future research avenues.

The author develops a bilateral Nash bargaining model under value uncertainty and private/asymmetric information, combining ideas from axiomatic and strategic bargaining theory. The solution to the model leads organically to a two-tier stochastic frontier (2TSF) setup with intra-error dependence. The author presents two different statistical specifications to estimate the model, one that accounts for regressor endogeneity using copulas, the other able to identify separately the bargaining power from the private information effects at the individual level. An empirical application using a matched employer–employee data set (MEEDS) from Zambia and a second using another one from Ghana showcase the applied potential of the approach.

Showrooming presents a complex and evolving challenge to retail managers, as it signifies the emergence of new forms of exchange rules. The purpose of this research is to investigate how factors responsible for information search and evaluation affect showrooming and also consider the consumer mindset as a moderator.

This research undertakes three experimental designs to investigate how the push (i.e. assortment size), pull (i.e. price discount), and mooring (i.e. sunk cost) factors influence consumers' showrooming intention. Specifically, consumers' maximizing tendency plays the role of moderator.

The results reveal that push, pull, and mooring factors are significantly related to consumers' showrooming intention. Furthermore, the findings show that maximizers have higher showrooming intention than satisficers in the context of the push, pull, and mooring factors.

By integrating the push-pull-mooring framework and the maximizing mindset theory, this research proposes a novel research model and the empirical testing results support six hypotheses. The findings add to the body of knowledge in showrooming behavior by taking consumer mindset into account. The results also provide implications for practitioners to develop their retail strategies.

The study examines the influence of various congruity factors (economic, safety and Health, hedonic, and functional) on tourists' satisfaction with sharing economy-based services.

A single cross-sectional design is used in the study, with 513 travellers surveyed directly at three different tourist destinations in India. The partial least squares-structural equation modelling approach is used to analyse the data.

The research shows that congruity factors significantly influence tourists' satisfaction with sharing economy-based services. Moreover, satisfaction is strongly associated with recommendation intention for these services and customer loyalty. Additionally, the intention to recommend directly leads to increased customer loyalty.

The results offer pertinent insights for stakeholders in the sharing economy, including tourism marketers, policymakers and corporations. Aligning services with the identified congruity factors can enhance user satisfaction, boost recommendation rates and foster long-standing customer loyalty.

This study stands out for its thorough investigation into how congruity factors influence tourist satisfaction within the context of the sharing economy. Additionally, by focusing on specific demographic differentiators, such as age (gen Z or old gen) and gender (male or female), the study provides a nuanced understanding that enriches the existing body of knowledge.

This study aims to examine the influence of collectivism, brand prestige and brand identification on green brand loyalty among Generation Z and older generation travellers in India. Loyal consumers are essential for the long-term success of hotels.

A single cross-sectional design is used in the study, with a total of 277 Generation Z and 299 Older generation travellers surveyed directly at three different tourist destinations in India. The PLS-SEM approach is used to analyse the data.

The study’s findings showed that among Generation Z travellers, factors such as collectivism, brand identification and brand prestige play a significant role in shaping green brand loyalty towards hotels. Furthermore, we noted that collectivism directly influences brand prestige and identification. Conversely, for the older generation, the study did not substantiate the impacts of collectivism on green brand loyalty nor the relationship between brand identification and green brand loyalty.

This study uniquely contributes to the literature by revealing generational differences in the influence of collectivism and brand factors on green brand loyalty within the Indian hotel industry.

本研究探讨了集体主义、品牌声望和品牌认同对印度 Z 世代和老一代旅行者绿色品牌忠诚度的影响。 忠诚的消费者对于酒店的长期成功至关重要。

该研究采用单一横断面设计, 直接在印度三个不同的旅游目的地对总共 277 名 Z 世代和 299 名老一代旅行者进行了调查。 PLS-SEM 方法用于分析数据。

研究结果表明, 在 Z 世代旅行者中, 集体主义、品牌认同和品牌声望等因素在塑造酒店绿色品牌忠诚度方面发挥着重要作用。 此外, 我们注意到集体主义直接影响品牌声誉和认同。 相反, 对于老一代人来说, 该研究没有证实集体主义对绿色品牌忠诚度的影响, 也没有证实品牌认同与绿色品牌忠诚度之间的关系。

这项研究揭示了集体主义和品牌因素对印度酒店业绿色品牌忠诚度影响的代际差异, 对文献做出了独特的贡献。

Este estudio examina la influencia del colectivismo, el prestigio de la marca y la identificación de la marca en la lealtad a la marca ecológica entre la Generación Z y los viajeros de la generación anterior en la India. Los consumidores leales son esenciales para el éxito a largo plazo de los hoteles.

En el estudio se utiliza un diseño transversal único, con un total de 277 viajeros de la Generación Z y 299 de la Generación Mayor fueron encuestados directamente en tres destinos turísticos diferentes en la India. El enfoque PLS-SEM se utiliza para analizar los datos.

Los hallazgos del estudio mostraron que entre los viajeros de la Generación Z, factores como el colectivismo, la identificación de marca y el prestigio de la marca juegan un papel importante en la configuración de la lealtad hacia las marcas ecológicas hacia los hoteles. Además, observamos que el colectivismo influye directamente en el prestigio y la identificación de la marca. Por el contrario, para la generación mayor, el estudio no corroboró los impactos del colectivismo en la lealtad a la marca verde, ni la relación entre la identificación de marca y la lealtad a la marca verde.

Este estudio contribuye de manera única a la literatura al revelar diferencias generacionales en la influencia del colectivismo y los factores de marca en la lealtad a las marcas ecológicas dentro de la industria hotelera india.

The COVID-19 pandemic has affected around 216 countries and territories worldwide and more than 2000 cities in India, alone. The smart cities mission (SCM) in India started in 2015 and 100 smart cities were selected to be initiated with a total project cost of INR 2031.72 billion. Smart city strategies play an important role in implementing the measures adopted by the government such as the issuance of social distancing regulations and other COVID-19 mitigation strategies. However, there is no research reported on the role of smart cities strategies in managing the COVID-19 outbreak in developing countries.

This paper aims to address the research gap in smart cities, technology and healthcare management through a review of the literature and primary data collected using semi-structured interviews.

Each city is unique and has different challenges, the study revealed six key findings on how smart cities in India managed the COVID-19 outbreak. They used: Integrated Command and Control Centres, Artificial Intelligence and Innovative Application-based Solutions, Smart Waste Management Solutions, Smart Healthcare Management, Smart Data Management and Smart Surveillance.

This paper contributes to informing policymakers of key lessons learnt from the management of COVID-19 in developing countries like India from a smart cities’ perspective. This paper draws on the six Cs for the implications directed to leaders and decision-makers to rethink and act on COVID-19. The six Cs are: Crisis management leadership, Credible communication, Collaboration, Creative governance, Capturing knowledge and Capacity building.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

This paper aims to prove the following hypothesis Problem Statement: HYPOTHESIS (1) User Experience collection of mobile applications can be done using the Crowdsourcing mechanism; (2) User Experience collection of mobile applications are influenced by the mindset of Crowdmembers, culture/ethnicity/social background, ease of interface use and rewards, among other factors.

The authors of this paper, did a literature review first to find if Crowdsourcing was applicable and a used method to solve problems in Software Engineering. This helped us to narrow down the application of Crowdsourcing to the Requirements Engineering-Usability (User Experience) collection. User experience collection of two Malayalam language-based mobile applications, AarogyaSetu and BevQ was done as the next step. Incorporating findings from Study I, another study using AarogyaSetu and Manglish was launched as Study II. The results from both cases were consolidated and analyzed. Significant concerns relating to expectations of Crowd members with User Experience collection were unraveled and the purpose of Study was accomplished.

(1) Crowdsourcing is and can be used in Software Engineering activities. (2) Crowd members have expectations (motivating factors) of User Interface and other elements that enable them to be an effective contributor. (3) An individual’s environment and mindset (character) are influential in him becoming a contributor in Crowdsourcing. (4) Culture and social practices of a region strongly affects the crowd-participating decision of an individual.

This is purely self-done work. The value of this research work is two-fold. Crowdsourcing is endorsed significant in Software Engineering tasks, especially in User Experience collection of mobile applications. Two, the Crowd service requesters can be careful about designing the questionnaire for Crowdsourcing. They have to be aware and prepared to meet the expectations of the Crowd. This can ensure the active participation of potential contributors. Future researchers can use the results of this work to base their research on similar purposes.