Search results

Using data from Twitter, the purpose of this paper is to assess the coping behaviour and reactions of social media users in response to the initial days of the COVID-19-related lockdown in different parts of the world.

This study follows the quasi-inductive approach which allows the development of pre-categories from other theories before the sampling and coding processes begin, for use in those processes. Data was extracted using relevant keywords from Twitter, and a sample was drawn from the Twitter data set to ensure the data is more manageable from a qualitative research standpoint and that meaningful interpretations can be drawn from the data analysis results. The data analysis is discussed in two parts: extraction and classification of data from Twitter using automated sentiment analysis; and qualitative data analysis of a smaller Twitter data sample.

This study found that during the lockdown the majority of users on Twitter shared positive opinions towards the lockdown. The results also found that people are keeping themselves engaged and entertained. Governments around the world have also gained support from Twitter users. This is despite the hardships being faced by citizens. The authors also found a number of users expressing negative sentiments. The results also found that several users on Twitter were fence-sitters and their opinions and emotions could swing either way depending on how the pandemic progresses and what action is taken by governments around the world.

The authors add to the body of literature that has examined Twitter discussions around H1N1 using in-depth qualitative methods and conspiracy theories around COVID-19. In the long run, the government can help citizens develop routines that help the community adapt to a new dangerous environment – this has very effectively been shown in the context of wildfires in the context of disaster management. In the context of this research, the dominance of the positive themes within tweets is promising for policymakers and governments around the world. However, sentiments may wish to be monitored going forward as large-spikes in negative sentiment may highlight lockdown-fatigue.

The psychology of humans during a pandemic can have a profound impact on how COVID-19 shapes up, and this shall also include how people behave with other people and with the larger environment. Lockdowns are the opposite of what societies strive to achieve, i.e. socializing.

This study is based on original Twitter data collected during the initial days of the COVID-19-induced lockdown. The topic of “lockdowns” and the “COVID-19” pandemic have not been studied together thus far. This study is highly topical.

Deep learning (DL) is on the rise because it can make predictions and judgments based on data that is unseen. Blockchain technologies are being combined with DL frameworks in various industries to provide a safe and effective infrastructure. The review comprises literature that lists the most recent techniques used in the aforementioned application sectors. We examine the current research trends across several fields and evaluate the literature in terms of its advantages and disadvantages.

The integration of blockchain and DL has been explored in several application domains for the past five years (2018–2023). Our research is guided by five research questions, and based on these questions, we concentrate on key application domains such as the usage of Internet of Things (IoT) in several applications, healthcare and cryptocurrency price prediction. We have analyzed the main challenges and possibilities concerning blockchain technologies. We have discussed the methodologies used in the pertinent publications in these areas and contrasted the research trends during the previous five years. Additionally, we provide a comparison of the widely used blockchain frameworks that are used to create blockchain-based DL frameworks.

By responding to five research objectives, the study highlights and assesses the effectiveness of already published works using blockchain and DL. Our findings indicate that IoT applications, such as their use in smart cities and cars, healthcare and cryptocurrency, are the key areas of research. The primary focus of current research is the enhancement of existing systems, with data analysis, storage and sharing via decentralized systems being the main motivation for this integration. Amongst the various frameworks employed, Ethereum and Hyperledger are popular among researchers in the domain of IoT and healthcare, whereas Bitcoin is popular for research on cryptocurrency.

There is a lack of literature that summarizes the state-of-the-art methods incorporating blockchain and DL in popular domains such as healthcare, IoT and cryptocurrency price prediction. We analyze the existing research done in the past five years (2018–2023) to review the issues and emerging trends.

Usage of updated technology is continuously empowering customer relationship management (CRM) to be convenient and user friendly, where customers are kept engaged with knowledge and information. This enables them on decision-making and managing their portfolio, especially in mutual fund investments. To improve toward a positive decision, certain quality related variables needed to be considered. Thus, this study aims to estimate the mediation effect of relationship quality and outcome (RQO) between CRM and investment decision-making in mutual funds (MFD).

The descriptive study adopted the constructs from existing empirical literatures to conceptualize the model with three higher order constructs with 12 dimensions. Survey method is used, and with a structured questionnaire, a total of 323 mutual fund investors were approached using nonprobability criterion sampling technique, of which 262 relevant responses were considered for estimating the structural model. Smart PLS was used to establish the relationship of the constructs.

The result emphasizes a significant direct and indirect relationship indicating that investors are more inclined to MFD through technology-enabled CRM and RQO plays a vital role in explaining the direct relationship between CRM and MFD. The results of the study are in-line with the existing literature.

The study highlights that financial institutions must focus not only on technological diffusion but also needs to ensure quality service by providing knowledge and information during every access of transactions by customers, making them independent and confident during investments.

This study indicates how capacity efficiency, which is a part of service productivity, can be managed without affecting the outcome efficiency by incorporating technology in the place of human interaction during relationship acquiring and retaining process.

The purpose of this paper is to classify the total quality management (TQM) practices and position them based on their importance in the implementation of TQM in steel industries for obtaining improved business performance and customer satisfaction.

The study undergoes a deep literature review to recognise the TQM practices necessary for the implementation of TQM in the steel industry. The identified 17 practices were grouped under three activities such as “breakthrough activities”, “improvement activities” and “routine operational and maintenance activities” with experts’ suggestion. The positioning of 17 TQM practices and 3 activities of TQM was done using an analytical hierarchy process (AHP) technique as per their relevance in TQM implementation.

The findings of the research show that though all the 17 practices and 3 activities of TQM play a vital role in the successful implementation of TQM in steel industries, breakthrough activities have gained more weightage as compared to the other two activities. Top management commitment, quality system and continuous improvement are relatively more important than the other practices.

The research has limitation analogous to the AHP technique. The technique considers all the sub-sets of each nod to be independent from each other, whereas in real world this does not stand true.

The study positions the TQM practices and activities on the basis of their relevance in TQM implementation in the steel industry, thus helping the managers in understanding which practice has to give how much importance for TQM implementation in steel industries. By following the practices according to the weights, the managers can achieve the results desired from TQM implementation.

The study gives the implementation priorities of TQM practices in the steel industry. To the best knowledge of researcher, it is the first study in the steel sector for TQM implementation. The lacuna was reported by the literature, and the researcher has made an attempt to fill that gap.

Due to its impact on business performance total quality management (TQM) has gained a lot of importance by businessmen, managers, practitioners, and research scholars over the last 20 years. Therefore, the purpose of this paper is to critically assess the literature on TQM and find out the areas where future research is required.

To achieve this purpose the articles published in the last 20 years were studied in a systematic way and a snapshot of the same was prepared in the tabular format with points such as year and journal of publication, application and country, statistical method used, and findings of the study such as practices and impact of TQM. After identifying the practices and impact of TQM a quality tool “Pareto Analysis” was applied on them for development of the model.

The findings provide the practices of TQM and its impact on the performance of a business. The gaps from the literature have been identified and areas for future research have been suggested. On the basis of the findings a generalized framework of TQM has been suggested which can be applicable irrespective of the sector.

The research will help academicians and future researchers to have a clear understanding of TQM in different rosters.

Ample literature is available on TQM but in the best knowledge of authors no study has taken place to integrate the reviews and findings of 102 research papers of the last two decades.

The purpose of this paper is to identify the inter-partner dynamics-based enablers of joint venture (JV) competitiveness. In addition, this paper models the interactions among identified enablers/factors to project the strength of their relationship with JV competitiveness.

ISM- and total interpretive structural modeling (TISM)-based fuzzy TISM approach has been used to examine the interactions and strength of interactions among identified enablers of JV competitiveness.

The analysis concludes that inter-partner dynamics-based enablers, such as partner fit, power symmetry and trust, have strong driving power and low dependence power and are at the lowest level of hierarchy in fuzzy TISM model. Variables like collaborative communication, organizational learning and absorptive capacity are linkage variables and they have high dependence as well as driving power and they lie in the second level of fuzzy TISM hierarchy. Strategic flexibility is found to have high dependence power and has weak driving power. The outcome variable JV competitiveness found to have zero driving power and highest dependence power.

The findings have implications for practitioners and policy makers. JVs may achieve competitiveness by managing identified enablers (inter-partner dynamics).

Present paper is one among the few efforts that address the issue of JV competitiveness (post-formation of JV). Methodologically also, this study is one among few initial efforts of using modified fuzzy TISM to explore and understand the linkage among enablers and outcome variables. Modified fuzzy TISM process carries out transitivity checks along with the successive pair-wise comparisons and simplifies the fuzzy TISM approach.

The purpose for this paper is to determine the various dimensions of employer brand in the IT sector of India and analyse their impact on the final intent of the candidates to join an organisation. A five-factor employer brand (EB) model and three-factor intent to join (ITJ) model have been tested for reliability and validity through confirmatory factor analysis (CFA). A structural model is empirically tested with EB as independent variable and ITJ as dependent variable through structured equation modelling.

The items for the independent variable EB are generated with the help of literature and semi-structured interviews with final-year student placement coordinators of B.Tech and MCA, studying in central, state and deemed universities of India. For ITJ, the variables are adopted from the literature and confirmed through CFA in the Indian context. Structured equation modelling has been used to analyse the relationship between EB and ITJ.

Five dimensions of EB were explored as growth and development opportunity, company’s reputation, acceptance and belongingness, work–life balance and ethics and CSR. ITJ was found to be composed of intent to pursue, employer’s attractiveness and employer’s reputation. EB has been found to be an effective contributor to ITJ.

The current study has been conducted in the IT sector, and other sectors have not been included. The universities considered for the study were limited to central, state and deemed universities of India. Apart from intention to join, there are other attitudinal and behavioural aspects that have not been included in the current study.

This study gives empirical evidence on EB to be an important antecedent of ITJ from the perspective of prospective employees of a developing nation.