Search results

Stock market has always been lucrative for various investors. But, because of its speculative nature, it is difficult to predict the price movement. Investors have been using both fundamental and technical analysis to predict the prices. Fundamental analysis helps to study structured data of the company. Technical analysis helps to study price trends, and with the increasing and easy availability of unstructured data have made it important to study the market sentiment. Market sentiment has a major impact on the prices in short run. Hence, the purpose is to understand the market sentiment timely and effectively.

The research includes text mining and then creating various models for classification. The accuracy of these models is checked using confusion matrix.

Out of the six machine learning techniques used to create the classification model, kernel support vector machine gave the highest accuracy of 68%. This model can be now used to analyse the tweets, news and various other unstructured data to predict the price movement.

This study will help investors classify a news or a tweet into “positive”, “negative” or “neutral” quickly and determine the stock price trends.

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.

Reliability and maintainability estimation of any system depends on the identification of the best-fitted probability distribution of failure and repair rates. The parameters of the best-fitted probability distribution are also contributing significantly to reliability estimation. In this work, a case study of load haul dump (LHD) machines is illustrated that consider the optimization of failure and repair rate parameters using two well established metaheuristic approaches, namely, genetic algorithm (GA) and particle swarm optimization (PSO). This paper aims to analyze the aforementioned points.

The data on time between failures (TBF) and time to repairs (TTR) are collected for a LHD machine. The descriptive statistical analysis of TBF & TTR data is performed, trend and serial correlation tested and using Anderson–Darling (AD) value best-fitted distributions are identified for repair and failure times of various subsystems. The traditional methods of estimation like maximum likelihood estimation, method of moments, least-square estimation method help only in finding the local solution. Here, for finding the global solution two well-known metaheuristic approaches are applied.

The reliability of the LHD machine after 60 days on the real data set is 28.55%, using GA on 250 generations is 17.64%, and using PSO on 100 generations and 100 iterations is 30.25%. The PSO technique gives the global best value of reliability.

The present work will be very convenient for reliability engineers, researchers and maintenance managers to understand the failure and repair pattern of LHD machines. The same methodology can be applied in other process industries also.

In this case study, initially likelihood function of the best-fitted distribution is optimized by GA and PSO. Reliability and maintainability of LHD machines evaluated by the traditional approach, GA and PSO are compared. These results will be very helpful for maintenance engineers to plan new maintenance strategies for better functioning of LHD machines.

Based on self-determination theory, the paper investigates the link between environmentally specific transformational leadership (ETFL) and subjective well-being (SWB) of employees working in an energy intensive industry. The paper also aims to examine pro-environmental behavior (PEB) as a mediator mechanism between ETFL and SWB relationship.

An empirical research design was used for the analysis. In total, 293 respondents working in different energy intensive Indian companies filled the questionnaire for the study. The data were analyzed using structural equation modeling SMART-PLS 3 software which is a graphical user interface for variance-based structural equation modeling using the partial least squares path modeling method. Two stages of analysis were conducted: the measurement model and the structural model.

The results indicate that (1) ETFL has a significant positive impact on PEB. (2) PEB is positively correlated to SWB. (3) ETFL has a significant impact on SWB. (4) The underlying mechanism process of ETFL and SWB relationship is explained by mediation of PEB.

Overall, the study insinuates that PEB act as underlying mechanism in environmental leadership and SWB of employees. The study further suggests that the management should promote green leaders for enhancing the overall SWB of the managements' employees especially in energy intensive sector. Doing so helps the managers to enhance managers' environmental conscious behavior for inducing SWB of employees.

The study contributes to novel findings in existing literature particularly to determine ETFL and PEB as predictors of SWB especially in energy intensive sector. The study also leads the ways for organizations by promoting green conscious leaders, which stimulates PEB among employees that ultimately contributes to SWB of employees. Furthermore, the research methodology can be used in other studies for variation of contextual variables such as size of the industry, developed or underdeveloped country and manufacturing or service industry.

The purpose of this study is to analyze the potential mediating role of employee engagement in the relationship between gratitude and subjective well-being (SWB) of employees working in the information technology (IT) sector in India. The study investigated a moderated mediation model for gratitude and SWB, treating employee engagement as a mediator and gender as a moderator.

Data was collected from 162 professionals working IT sector in India. Process Macro, AMOS and IBM SPSS 22 were used to analyze the mediation and moderation effects.

The results depicted that employee engagement fully mediates the positive association between gratitude practice and the SWB of employees as well as the demographic variable; gender also demonstrated a full moderation effect between them.

This research may be one of the few studies from the Indian context that explore whether gratitude practiced by employees working in the IT sector can play a significant role in impacting their SWB. Past research models had not introduced employee engagement’s indirect impact on the examined variables.

The earth is under massive stress due to current level of consumption which has crossed the sustaining capacity of our planet. Thus, the need of the hour is to promote sustainable production and consumption. The purpose of this study is to identify the basic barriers of sustainable consumer behaviour which are hindering the adoption of sustainable consumption.

This article is divided into two parts. The first part consists of a literature review based on 128 articles (1995 to 2020), which are spread over a period of 25 years. Based on the literature review, nine barriers of sustainable consumer behaviour were identified and put into three categories. In the second part, fuzzy analytical hierarchy process has been used to know about the relative weight of each barrier so that benchmarking/prioritising of basic barriers of sustainable consumer behaviour can be done.

This article identifies critical barriers affecting the acceptance of sustainable electronic products. High price, a perception of no environmental impact, no benefit in personal image, lesser use by family and friends, lack of awareness about the products etc. emerged as the potential barriers which need prime attention. The relative weight of each of these barriers has also been arrived at in this article which is expected to be beneficial for policymakers to focus upon important barriers. Impact of many of these barriers can be reduced through innovative approaches and solutions.

This article will be helpful in future research in the field of sustainable consumer behaviour. Through the understanding of the barriers of sustainable consumer behaviour, companies, governments and industries can take suitable initiatives by modifying the policies and practices to reduce the impact of these barriers so that consumer behaviour can be made more sustainable.

The current article tries to identify the critical barriers to adoption of sustainable electronic products by the consumers. An extensive literature review, expert suggestions and consumer survey have been adopted to identify nine barriers. Although, multiple researches have been done in the field of sustainable consumer behaviour and adoption of sustainable electronic products, there is no research article which solely focuses on implementing Fuzzy analytical hierarchical process (AHP) approach to rank the barriers faced by consumers for adoption of sustainable electronic products. It has been concluded that high price of sustainable electronic products is the most critical barrier in adoption of sustainable consumer behaviour. Moreover, the relative ranking obtained with the help of Fuzzy AHP can be used by policymakers and organisations to promote and implement sustainability in consumer behaviour.

This project aims to examine how entrepreneurial orientation and succession planning among Palestinian family businesses positively affects their associated growth potential, considering the mediating role of innovation and the moderation effect of geographic location. Leveraging ethnic entrepreneurship theory, the authors compare these types of enterprises in the USA with their counterparts in Palestine.

This cross-sectional quantitative research analyzes data collected from October through December 2022. 180 Palestinian family-owned firms completed a survey; 90 companies were located in Palestine, while the other 90 were in the USA. Structural equation modeling analysis was conducted using Smart-PLS4. The interrelations of the conceptual framework were examined via path analysis and bootstrapping techniques.

The authors found a statistically significant positive effect of entrepreneurial orientation on Palestinian family business growth; the authors’ results concurrently indicated succession planning did not affect growth within the authors’ selected population. The authors also discovered innovation mediates the relationship between orientation and growth, and business location appears to moderate this relationship. The authors’ research indicates geography appears to favor Palestinian family-owned companies in the USA, where the authors found opportunity-driven immigrant entrepreneurs benefit from the structured business systems in a highly-developed country.

Given the current situation in Palestine, it is essential to understand the potential contribution that Palestinian family-owned businesses globally can make to reconstruct the country’s local economy. The next few years will be critical in figuring out how innovative thinking can boost the region’s recovery and increase Palestinian-based family companies’ ability to engage in sustainable entrepreneurship with reinvestment support from its diaspora. Therefore, it is important to have research that identifies factors that could improve these businesses’ continued performance and growth potential. This study also aids in further understanding the defining characteristics of Palestinian-owned family firms, enhancing general theories related to entrepreneurship among ethnic and diasporic groups.

The purpose of this study was to carry out the analysis of impact resistance for aluminum hybrid laminates and polymer matrix composites reinforced with glass and carbon fibers. Damage modes and damages process under varied impact energies are also presented and discussed.

The subject of examination were fiber metal laminates – FMLs (Al/CFRP and Al/GFRP). The samples were subjected to low-velocity impact by using a drop-weight impact tester. The specimens after impact were examined using non-destructive and destructive inspection techniques.

The hybrid laminates are characterized by higher resistance to impact in comparison to the conventional laminates. The delaminations between composite layers as well as the delaminations on metal/composite interface and lateral cracks are the prevailing type of destruction mechanisms. No significant relationships between metal volume friction coefficient vs response to the impact were recorded for the hybrid laminates under tests.

The understanding of impact behavior of FMLs is particularly important for selecting these materials and their designing, in damage tolerance philosophy aspect in aerospace industry as well as in searching the methods of predicting of FML hybrid materials resistance to impact. The test results might be useful for the validation of simulations using numerical methods.

The paper presents the impact resistance of new hybrid laminates for aerospace applications. The identification of damage character and failure mechanisms as well as the relationships between damage and impact responses of aluminum/carbon and aluminum/glass hybrid laminates were estimated.

Purpose: This chapter aims to unveil the challenges of adopting and using banking chatbots in India and identify the challenges of Chat Generative Pre-trained Transformer (ChatGPT) for future banking.

Need for the study: Unveiling the challenges of chatbots and ChatGPT in the banking industry in India is crucial to understand the limitations and areas of improvement to enhance customer experience, ensure data security, and maintain regulatory compliance.

Methodology: The researchers conducted a narrative review systematically summarising and analysing existing literature on chatbots and ChatGPT, providing a comprehensive overview of the challenges faced in the industry.

Findings: The authors identify perceived risk, platform quality, connectivity and infrastructure, data privacy and security, user education and acceptance, existing legacy systems, and regulatory guidelines as the challenges of adopting chatbots. Additionally, the findings reveal that the challenges posed by ChatGPT in future banking include the potential reduction of traditional banking jobs, linguistic diversity, data privacy and security, ethical considerations and bias mitigation, explainability and accountability, integration with existing banking systems, and user trust and acceptance. However, implementing these new technologies also presents opportunities for individuals with unique human skills, such as critical thinking, empathy, and creativity, which are difficult to replace with technology.

Practical implications: By minimising the challenges of ChatGPT and chatbots, the banking industry could achieve improved customer service, cost efficiency, automation of routine tasks, and 24/7 availability, leading to enhanced customer satisfaction and operational efficiency in the banking industry. Additionally, these artificial intelligence (AI) tools enable data-driven insights, personalised experiences, scalability, and efficient handling of large customer volumes, contributing to better decision-making and enhanced customer engagement.