Management Studies - Publications

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Browsing Management Studies - Publications by Author "Bellamkonda, Raja Shekhar"
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    A text mining analysis of online reviews of Indian hotel employees
    ( 2021-01-01) Chittiprolu, Vinay ; Singh, Swati ; Bellamkonda, Raja Shekhar ; Vanka, Sita
    Based on the premise of Market-focused Human Resource Management we propose that organizations should attend to employee voice. To this end, online reviews have emerged as a significant source of key information. We performed our analysis on (n = 2751) Glassdoor reviews of 22 hotel chains in India. We employed text mining tools to identify determinants of employee motivation and dissatisfaction. Organizational culture, career growth opportunities, and flexibility-motivated employees. Poor work–life balance, office politics, and high attrition rate de-incentivized employees. Further, the regression analysis of numerical ratings revealed that compensation and work–life balance are hygiene factors; career opportunities and cultural values emerged as dominant predictors of overall employee satisfaction. Practical, policy, and theoretical implications of these findings are discussed.
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    A text mining analysis of online reviews of Indian hotel employees
    ( 2021-01-01) Chittiprolu, Vinay ; Singh, Swati ; Bellamkonda, Raja Shekhar ; Vanka, Sita
    Based on the premise of Market-focused Human Resource Management we propose that organizations should attend to employee voice. To this end, online reviews have emerged as a significant source of key information. We performed our analysis on (n = 2751) Glassdoor reviews of 22 hotel chains in India. We employed text mining tools to identify determinants of employee motivation and dissatisfaction. Organizational culture, career growth opportunities, and flexibility-motivated employees. Poor work–life balance, office politics, and high attrition rate de-incentivized employees. Further, the regression analysis of numerical ratings revealed that compensation and work–life balance are hygiene factors; career opportunities and cultural values emerged as dominant predictors of overall employee satisfaction. Practical, policy, and theoretical implications of these findings are discussed.
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    Effect of student perceived service quality on student satisfaction, loyalty and motivation in Indian universities: Development of HiEduQual
    ( 2016-01-01) Annamdevula, Subrahmanyam ; Bellamkonda, Raja Shekhar
    Purpose: This paper attempts to develop and validate a service quality instrument called HiEduQual to measure the perceived service quality of students in higher education institutions. This paper aims to propose a structural model by examining the theoretical and empirical evidences on the relationships between students’ perceived service quality (SPSQ), students’ satisfaction (SSt), students’ loyalty (SL) and students’ motivation (SM). Design/methodology/approach: The paper uses survey research design to gather data regarding attitudes of students about quality of service, satisfaction, motivation and loyalty from seven public universities in India and tests the relationships between these variables using structural equation modeling. Findings: The paper identifies a model with six-structured dimensions containing 23 items for HiEduQual. It proved the direct positive effect of the perceived service quality of students on satisfaction, loyalty and motivation. The paper also supports the partial and complete mediation role of students’ satisfaction between perceived service quality of students, their loyalty and motivation toward services being provided by the universities. The competing Model 1 (M1) with partial mediation role of students’ satisfaction between students’ perceived service quality, loyalty and motivation was proved as the best among the alternative models. Research limitations/implications: The paper developed and tested a new measurement instrument that covers all the service aspects experienced by the student as primary customer in higher education. Further studies can also measure service quality of the universities in the perspective of other key stakeholders. The authors would recommend studying other possible antecedents which would have influence on satisfaction motivation and loyalty. Practical implications: The findings suggested that it would be worthwhile for university leaders to make proper allocation of resources, to provide better educational services including support services and facilities. It is believed that this paper has a significant competence for engendering more precise applications related to quality of services, especially concerning students’ satisfaction, loyalty and motivation. Social implications: The changing nature and need of higher education services and increase in competitive intensity necessitates higher performance levels in the Indian higher education (universities). These can only be achieved through a better understanding of the expectations of students and the importance placed by them on aspects such as teaching, administrative services, academic facilities, campus infrastructure, support services and internationalization. The paper identified that student perceived service quality is a key antecedent to student satisfaction, motivation and loyalty, which conveys that service quality is an important construct. Originality/value: Previous studies have primarily focused on the relationship between service quality, satisfaction and loyalty. Along with the above, this paper includes students’ motivation and assesses the effect of service quality and satisfaction on motivation which was not previously used in services marketing research, especially in higher education sector. Higher education service holds some unique features like customers’ (student) cognitive participation in the service process, requirements of the students to be fulfilled by different parties and long-term continuous services. All these features require student participation. The results indicate that quality of academic and non-academic services play a vital role in motivating students to perform better in their academics.
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    Heritage hotels and customer experience: a text mining analysis of online reviews
    ( 2021-01-01) Chittiprolu, Vinay ; Samala, Nagaraj ; Bellamkonda, Raja Shekhar
    Purpose: In business, online reviews have an economic impact on firm performance. Customers’ data in the form of online reviews was used to understand the appreciation and service complaints written by previous customers. The study is an analysis of the online reviews written by the customers about Indian heritage hotels. This study aims to understand the dimensions of service appreciation and service complaints by comparing positive- and negative-rated reviews and find the patterns in the determinants of the satisfaction and dissatisfaction of the customers. Design/methodology/approach: A total of 23,643 online reviews about heritage hotels were collected from the TripAdvisor website by using a Web crawler developed in Python. A total of 1000 reviews were randomly selected for further analysis to eliminate the bandwagon effect. Unsupervised text mining techniques were used to analyze reviews and find out the interesting patterns in text data. Findings: Based on Herzberg two-factor theory, this study found satisfied and dissatisfied determinants separately. The study revealed some common categories discussed by satisfied and dissatisfied customers. The factors which satisfy the customers may also dissatisfy the customers if not delivered properly. Satisfied customers mentioned about tangible features of the hotel stay, which includes physical signifiers, traditional services, staff behavior and professionalism and core products (rooms, food). However, most of the customers complained about intangible service problems, such as staff attitude, services failure, issues with reservation and food, value for money and room condition. The results are contradicting with commercial hotels-based studies owing to the unique services provided by heritage hotels. Practical implications: The dimensions for satisfaction and dissatisfaction among customer of heritage hotels provide marketers to understand the real emotion and perception of the customers. As these dimensions were extracted through text mining of the reviews written by the customer of heritage hotels, the results would certainly give better insights to the hotel marketers. Originality/value: The study is a rare attempt to study online reviews of customers on heritage hotels through a text mining approach and find the patterns in the behavior and the determinants of satisfaction and dissatisfaction of customers.
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    Impact of AI and robotics in the tourism sector: a critical insight
    ( 2020-01-01) Samala, Nagaraj ; Katkam, Bharath Shashanka ; Bellamkonda, Raja Shekhar ; Rodriguez, Raul Villamarin
    Purpose: The purpose of the present article is to highlight the role of Artificial Intelligence (AI) and Robotics in the tourism industry. The various technologies being integrated to improve the service and customer experience in tourism. The expected changes and challenges in tourism in the future are focused in this paper. Design/methodology/approach: A systematic study on the emerging technologies of AI and Robotics applied in the tourism sector is presented in the form of a viewpoint. Findings: AI certainly enhances tourism experiential services however cannot surpass the human touch which is an essential determinant of experiential tourism. AI acts as an effective complementary dimension to the future of tourism. With the emergence of artificial travel intelligence, it is simpler to make travel arrangements. AI offers travel services that are automated, customized and insightful. AI allows travelers to learn about their behaviors, interests to inclinations and provide a personalized experience. Gone are the days to consult a travel agent, meet him physically and indulge in an endless chain of troubling phone calls to inquire about travel arrangements. Practical implications: Tourism marketing to see a positive and improved change that will enhance the tourists’ overall experience due to the application of AI and Robotics. New emerging technologies like chatbots, virtual reality, language translators, etc. can be effectively applied in Travel, Tourism & Hospitality industry. Originality/value: The present viewpoint discusses the application and role of AI and Robotics with the help of relevant industry examples and theory. The present paper highlights the different technologies being used and will be used in the future.
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    Longitudinal analysis versus cross-sectional analysis in assessing the factors influencing shoppers’ impulse purchase behavior – Do the store ambience and salesperson interactions really matter?
    ( 2021-07-01) Katakam, Bharath Shashanka ; Bhukya, Ramulu ; Bellamkonda, Raja Shekhar ; Samala, Nagaraj
    Numerous studies in the marketing literature focused on consumer behavior in general, but relatively few studies have examined Impulse purchase behavior (IPB). Although few studies examined IPB, the vast majority of the studies were conducted using the cross-sectional design. These studies suffer from certain limitations like random measurement error, common method bias, causality & validity-related issues that are inherently associated with the cross-sectional design. Despite these limitations, very few studies have examined the IPB using the longitudinal design. Multilevel structural equation modeling (ML-SEM) is conducted in the study to analyze the longitudinal data for examining the changes in the causal effects of the factors influencing the shoppers' IPB over a period of time. Additionally, structural equation modeling (SEM) is conducted to examine changes in the causal effects of the factors influencing IPB at each time point of data collection. Drawing upon the stern's model and stimulus-organism-response model, the study examines the causal effects of the factors influencing the IPB. The results of ML-SEM indicate significant fluctuations in the factors influencing IPB over time. Similarly, the results of SEM indicates that few factors (like store ambience and salesperson interactions) have shown a significant influence on IPB in the initial time points (i.e., during the initial store visits of shoppers), but became insignificant over a period of time in their subsequent store visits. The findings suggest that the store crowd, secondary customers influence, and in-store promotions show a significant influence on the IPB, compared to the store ambience and salesperson interactions.
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    RAILQUAL: A multiple item scale for evaluating railway passenger service quality and satisfaction
    ( 2017-01-01) Maruvada, Devi Prasad ; Bellamkonda, Raja Shekhar
    It was found that leading models and instruments in the area of service quality tend to be based on exploratory factor analysis and have not been informed by advances in measurement theory, particularly co-variance-based structural equation models. The diverse nature of requirements of stake holders in railways makes it extremely difficult to decide upon what constitutes quality in railway passenger services. Hence, identification of common minimum quality items suitable for all passengers will help design the system and there by improve passenger satisfaction. To address this issue, we applied the recent advances in measurement theory to the dataset and compared two different modelling methods namely exploratory factor analysis and confirmatory factor analysis. Based on the psychometric scale development approaches, this research conceptualised, constructed, refined, and tested a multi-item scale 'RAILQUAL' that examined key factors influencing railway passenger service quality. Through qualitative and quantitative studies in three phases a 18-item, six-dimensional 'RAILQUAL' model was developed. RAILQUAL is a measuring instrument for service quality and passenger satisfaction in Indian railways which can be applied across other railways also worldwide with some minor modifications locally.
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    The effects of service quality on student loyalty: the mediating role of student satisfaction
    ( 2016-01-01) Annamdevula, Subrahmanyam ; Bellamkonda, Raja Shekhar
    Purpose: Student loyalty in higher education sector helps college administrators to establish appropriate programs that promote, establish, develop and maintain successful long-term relationships with both current and former students. The purpose of this study is to propose the use of mediation model that links service quality and student loyalty via student satisfaction and test the direct and indirect effects of service quality on student loyalty with the mediation role of student satisfaction. Design/methodology/approach: The study used survey research design and collected data from three oldest state universities in the state of Andhra Pradesh in India to find the relationships between service quality, student satisfaction and student loyalty in higher education sector using structural equation modeling. Findings: This study tested the proposed research model and proved the mediator role of student satisfaction between service quality and student loyalty. Service quality has been found to be an important input to student satisfaction. The result also shows that while university provides no basis for differentiation among the constructs, age and gender play a major role in determining the different perceptions of students about the constructs investigated. Research limitations/implications: The study focuses on student satisfaction, of which service quality is an important antecedent. Identification of other variables, besides service quality, is crucial to contribute to the overall student satisfaction. Similarly, it is just as critical to identify the other elements like value, image or institution reputation which may have direct impact on service loyalty. It would be more precise when the studies also consider the opinion of the students before joining the institute based on word of mouth of passed-out students and after finishing the course. Longitudinal studies to collect predictor and criterion variables before and after the course would be much stronger. Practical implications: A clearer understanding of the relationship between service quality, satisfaction and loyalty that helps ensure the management to take better strategies to concentrate and improve the performance is aided by this study. It is interesting to note that the student loyalty is primarily affected by age and gender. This type of analysis helps to identify the target students who have high potential of defection. Social implications: Higher education and their respective institutions seek to enhance socio-cultural and economic development to promote active citizenship by inculcating ethical values among students. The Indian higher education institutions are facing enormous issues related to quality in education. The changing nature and need of higher education services and an increase in competitive intensity necessitates higher performance levels in the realm of Indian higher education (universities). These can be achieved through a thorough understanding of the expectations of students and the importance placed by them on aspects found by the study such as teaching, administrative services, support services, hostel facilities, library and lab facilities and internationalization. Originality/value: Previous studies have proved the mediation role of satisfaction between service quality and loyalty in marketing literature, but no significant studies have empirically tested the same in higher education sector. The service quality measurement in higher education is complex because of some unique features like customers’ (student) cognitive participation in the service process, the needs of the students being fulfilled by different parties, long-term and continuous services. The study contributes to the existing field of knowledge by providing support for the contention that student satisfaction performs a mediating role in the link between service quality and student loyalty in higher education sector.
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    Using artificial intelligence-based models to predict the risk of mucormycosis among COVID-19 survivors: An experience from a public hospital in India
    ( 2022-03-01) Syed-Abdul, Shabbir ; Babu, A. Shoban ; Bellamkonda, Raja Shekhar ; Itumalla, Ramaiah ; Acharyulu, G. V.R.K. ; Krishnamurthy, Surya ; Ramana, Y. Venkat Santosh ; Mogilicharla, Naresh ; Malwade, Shwetambara ; Li, Yu Chuan Jack
    Introduction: India reported a severe public health challenge not only due to the COVID-19 outbreak but also the increasing number of associated mucormycosis cases since 2021.This study aimed at developing artificial intelligence based models to predict the risk of mucormycosis among the patients at the time of discharge from hospital. Methods: The dataset included of 1229 COVID-19 positive patients, and additional 214 inpatients, COVID-19 positive as well as infected with mucormycosis. We used logistic regression, decision tree and random forest and the extreme gradient boosting algorithm. All our models were evaluated with 5-fold validation to derive a reliable estimate of the model error. Results: The logistic regression, XGBoost and random forest performed equally well with AUROC 95.0, 94.0, and 94.0 respectively. The best accuracy and precision (PPV) were 0.91 ± 0.026 and 0.67 ± 0.0526, respectively achieved by XGBoost, followed by logistic regression. This study also determined top five variables namely obesity, anosmia, de novo diabetes, myalgia, and nasal discharge, which showed positive impact towards the risk of mucormycosis. Conclusion: The developed model has the potential to predict the patients at high risk and thus, consequently initiating preventive care or aiding in early detection of mucormycosis infection. Thus, this study, holds potential for early treatment and better management of patients suffering from COVID-19 associated mucormycosis.
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    Using artificial intelligence-based models to predict the risk of mucormycosis among COVID-19 survivors: An experience from a public hospital in India
    ( 2022-03-01) Syed-Abdul, Shabbir ; Babu, A. Shoban ; Bellamkonda, Raja Shekhar ; Itumalla, Ramaiah ; Acharyulu, G. V.R.K. ; Krishnamurthy, Surya ; Ramana, Y. Venkat Santosh ; Mogilicharla, Naresh ; Malwade, Shwetambara ; Li, Yu Chuan Jack
    Introduction: India reported a severe public health challenge not only due to the COVID-19 outbreak but also the increasing number of associated mucormycosis cases since 2021.This study aimed at developing artificial intelligence based models to predict the risk of mucormycosis among the patients at the time of discharge from hospital. Methods: The dataset included of 1229 COVID-19 positive patients, and additional 214 inpatients, COVID-19 positive as well as infected with mucormycosis. We used logistic regression, decision tree and random forest and the extreme gradient boosting algorithm. All our models were evaluated with 5-fold validation to derive a reliable estimate of the model error. Results: The logistic regression, XGBoost and random forest performed equally well with AUROC 95.0, 94.0, and 94.0 respectively. The best accuracy and precision (PPV) were 0.91 ± 0.026 and 0.67 ± 0.0526, respectively achieved by XGBoost, followed by logistic regression. This study also determined top five variables namely obesity, anosmia, de novo diabetes, myalgia, and nasal discharge, which showed positive impact towards the risk of mucormycosis. Conclusion: The developed model has the potential to predict the patients at high risk and thus, consequently initiating preventive care or aiding in early detection of mucormycosis infection. Thus, this study, holds potential for early treatment and better management of patients suffering from COVID-19 associated mucormycosis.