Sujata Joshi

@siu.edu.in

Professor
Symbiosis Institute of Digital and Telecom Management (SIDTM), Symbiosis International (Deemed University)



                 

https://researchid.co/sujata2023

Dr Sujata Joshi is Professor of Marketing and Strategy with an experience of 26 plus years. Currently working with Symbiosis Institute of Digital & Telecom Management, Symbiosis International (Deemed University), her research areas include Consumer-Behaviour, Branding, Customer-Engagement, Customer-Experience, Strategy.
She has published many papers in national and international journals in these areas and also won the best paper awards for several papers presented in international conferences. She was awarded the Winner of Emerald Literati Award 2019 for Highly Recommended Paper published in Information Technology & People Journal (ABDC “A” listed).
She has undertaken marketing related consulting assignments in the areas of Customer experience, VAS, Retailer Loyalty, and B2B sales for telecom companies. Currently she is doing an assignment on Customer engagement with an organization in the consulting domain and another assignment with an IT company in the area of Customer Experience

EDUCATION

Phd, SET, MBA, Mcom, Bcom

RESEARCH INTERESTS

Consumer behaviour, Customer experience, Customer engagement, Applications of technology in marketing, Branding, Strategy

60

Scopus Publications

Scopus Publications

  • Sustainable Hydroponics: Embedding IoT and Efficient Energy Generation and Distribution Algorithms
    Menachem Domb, Viraj Hudli, and Sujata Joshi

    Springer Nature Switzerland

  • Examining the nexus of relationship marketing and patient loyalty: The mediation and moderation analysis
    Abhijeet Chavan, Abhijit Mohanty, and Sujata Joshi

    Asia Pacific Academy of Science Pte. Ltd.
    Studies on association amongst relationship marketing and loyalty of patients are still trying to establish its presence in the healthcare management. Also, the variables impacting this association are still in exploratory phases. The research is being carried out for determining the nature of that relationship and also examining mediating and moderating effect of different variables. Data collected through various Scopus and other online databases have been used for this study. Literature review has been carried out by dividing it into three parts, relationship marketing, patient loyalty and variables like patient satisfaction, age, gender, family size, health insurance, etc. Primary data was collected from 938 patients of private healthcare providers by administering questionnaire online as well as in person. PLS-SEM was applied to analyze the data, which was used after testing the instrument for reliability and validity. After Covid-19 pandemic everyone felt need of a good health insurance but in actual many people were not having a simple health insurance cover also. Hospitals will have a major role in promoting health insurance and in turn providing quality service, but their role is currently limited to processing of health insurance and nothing more. Model from the study will help understand organizations the strength and direction of relationship between patient loyalty and efforts taken for marketing along with impact of moderating and mediating variable on the same. Future research can look into the other variables that will moderate or mediate the relationship in turn will help not only healthcare organizations but insurance companies also to develop product or service according to requirement of relationship. Research focuses on impact of moderating effect of health insurance on association amongst marketing efforts taken for relationship marketing and loyalty of patients through patient satisfactions’ mediation which was not studied before this. The study aims to test the conceptual model of moderating impact of health insurance on association between relationship marketing, patient loyalty and patient satisfaction using SEM.

  • Examining Online Grocery Purchase Intentions Through an Extended TAM Framework: A Mediation Analysis Approach
    Kala Mahadevan, Krunal K. Punjani, and Sujata Joshi

    Associated Management Consultants, PVT., Ltd.

  • Impact of IoT on Banking and Financial Services
    Nikhilesh Kumar, Sujata Joshi, and Ali Saad Alwan

    AIP Publishing

  • Global Citizenship Education in Higher Institution - A Systematic Review of Literature
    Ashish Singh, Sujata Joshi, Haitham Abbas Khalaf, A. H. Radie, and Haydar Abdulameer Marhoon

    AIP Publishing

  • The Impacts of Chatbots on Customer Experience During the Covid-19 Pandemic in India
    Arpit Arya, Sujata Joshi, Raghda Salam Al. Mahdawi, and Ahmed Alkhayyat

    AIP Publishing

  • Word of Mouth’s (WOM) Impact on Students B-School Selection
    Prasad Shinde, Sujata Joshi, Talib Zeedan Taban, and Ahmed Alkhayyat

    AIP Publishing

  • Study of Social Media Marketing Factors on Customer Engagement - Zomato
    Sri Datta Kartaveerya Neti, Sujata Joshi, Ahmed Alkhayyat, and Mohammed S. Hamza

    AIP Publishing

  • Augmented Reality of Online and Physical Retailing: A Study of Applications and Its Value
    Samyak Deshbhratar, Sujata Joshi, Rabi N. H. Alwaali, Ali Raheem Saear, and Haydar Abdulameer Marhoon

    AIP Publishing

  • The Impact of Netflix Recommendation Engine on Customer Experience
    Devinder Barwal, Sujata Joshi, Ahmed J. Obaid, Azmi Shawkat Abdulbaqi, Shokhan M. Al-Barzinji, Ahmed Alkhayyat, Safa K. Hachem, and Muthmainnah

    AIP Publishing

  • Cloud computing in agriculture: a bibliometric and network visualization analysis
    Krunal K. Punjani, Kala Mahadevan, Angappa Gunasekaran, V. V. Ravi Kumar, and Sujata Joshi

    Springer Science and Business Media LLC

  • Predicting Stock Prices using Machine Learning Techniques: An Analysis of Historical Market Data
    Pratik Vispute, Joshi Sujata, and N.A. Natraj

    IEEE
    In this study, how deep learning methods are used to forecast stock values are examined. The study concentrates on analyzing past stock market data and predicting future stock prices using deep learning algorithms like, Long Short-Term Memory (LSTM) networks, a form of Recurrent Neural Network (RNN). In order to train and test the LSTM model, historical stock market data are gathered for a particular stock. When predicting stock prices using deep learning, a model is trained using historical stock market data, including previous stock prices and trade activity. In order to forecast future stock values, the model gains the ability to spot patterns and trends in the data.

  • Embedded Conversational AI, Chatbots, and NLP to Improve Healthcare Administration and Practices
    Ananya Singh, Sujata Joshi, and Menachem Domb

    IEEE
    Healthcare activities include many prevalent and unattended issues, calling for modernization and automation of its administrative and continuous care aspects. Physical consultation is expensive and time-consuming, and thus, the adoption of healthcare intelligent assistants like AI Chatbots is a crucial asset. Conversational AI (CAI) comprises AI-powered chatbots and virtual assistants, set to enhance healthcare efficiency and quality. This work discusses how CAI is revolutionizing healthcare with intelligent virtual assistants for doctors and the rest of staff. It focuses on three significant challenges faced by the healthcare industry viz patient engagement, structuring of unstructured data for better automation, and increased efficiency of the CAI agent for better business insights. It interlinks patient engagement, automation, and business insights with CAI. This research study provides a literature review of technological advancements in CAI agents designed to cope with challenges in the healthcare industry. This research study aims to provide solutions to doctors, nurses, and healthcare administration by solving everyday organizational challenges, improving their productivity using CAI agent capabilities.

  • Stress Detection and Monitoring Using Wearable IoT and Big Data Analytics
    Arnav Gupta, Sujata Joshi, and Menachem Domb

    Springer Nature Singapore

  • Evolution of Consumer Brand Engagement in Past Two Decades: A Systematic Analysis
    Pritha Nasery Ubgade and Sujata Joshi

    SAGE Publications
    In recent years, as new technologies have developed, consumer engagement with the brand has become a primary focus for many companies. As a result, in the last two decades, there has been a tremendous rise in the number of researches on consumer brand engagement due to the many innovative ways companies use for consumer engagement. This article analyses consumer brand engagement studied across the globe during the last two decades using advanced bibliometric analysis tools such as Biblioshiny and VOSviewer. By analysing a total of 981 research articles written from the year 2000 onwards in various journals indexed in Scopus. The study tries to map the literature on consumer brand engagement and its progress in terms of technological advancements. The results indicate that there has been a steady rise in the research conducted on consumer brand engagement since 2010. The United States is one of the major contributing countries in the number of studies on consumer brand engagement. As it is a multidisciplinary field, studies are conducted in different fields such as psychology, management, etc., which is observed from the journal-wise analysis. This study analyses that the introduction of newer technologies has direct impact on the number of researches conducted. Thus, the study provides relevant inputs to practitioners and academicians regarding further development on this topic.

  • Advancement of Digital Twin in Irrigation and Smart Farming
    Chiranjeet Barkakoti and Sujata Joshi

    IEEE
    Agriculture and food production has been immensely impacted by digitalization; paving the way for advanced data processing techniques and technologies possible in the field of agriculture. The aim of Smart farming is to extract information from agricultural entities to resolve issues and challenges faced with regard to rising demand, food security, and climate change. Digital Twin is a concept that has the potential to increase production and efficiency while reducing the use of energy and other materials. The potential for digital twins to succeed in sustainable agriculture is enormous. Since the agriculture sector is dynamic and complicated, it needs an advanced management system. The necessity for automatic and self-reliant agriculture set up at the initial level is critical due to the frequent occurrence of natural disasters like floods and diseases. Due to problems with soil-based systems such as erosion, heavy manual labor, water availability, and productivity issues, soilless agriculture is becoming more and more popular. Digital techniques are expected to increase the optimization of processes and assist in agricultural decision-making.

  • AI, IoT and Robotics in Smart Farming: Current Applications and Future Potentials
    Dharini Pal and Sujata Joshi

    IEEE
    Farming is the backbone of the Indian economy, with a critical role in its GDP growth. By 2050, the world’s population is expected to reach 9.7 billion, requiring a 70 percent increase in global food production. Traditional agricultural practices are becoming increasingly inefficient and unreliable because of several issues, including soil degradation, water stress, nutrition shortage, insufficient infrastructure linkages, post-harvest loss, and information asymmetry. However, detrimental climate change is one of the most severe concerns confronting this industry. Thus, smart farming or digital agriculture is now regarded as a far more sustainable practice as it includes the management of farms via edge cutting technologies like Internet of Things (IoT), robotics, Artificial Intelligence (AI), drones, big data etc. to maximize the quantity and quality of crops while minimizing the abundance of human work needed. It entails using sensors and automated irrigation procedures to monitor agricultural land, temperature, soil moisture, pH level, etc. Employing these technologies increases profitability, decreases waste, and preserves environmental quality. Although the smart agriculture business is expanding rapidly due to the world’s growing population and rising food consumption, this field still has a scope of academic literature. This paper aims to describe the different cutting-edge technologies currently being used in smart farming, their use cases, and their future directions. For this paper, a case study technique has been employed to study several technologies and their prospects in this endeavor. This study will be helpful to academicians and Government officials that can further help farmers to create a smart and more sustainable agriculture industry.

  • Applications of Metaverse in the Healthcare Industry
    Bhanu Bhatia and Sujata Joshi

    IEEE
    Healthcare has always been a very profitable and resource-intensive industry, and it is also one of the only ones that have never reached a digital transformation peak. A network of 3D virtual worlds centered on social interaction is known as a metaverse. As the world becomes more dependent on technology, more consideration must be given to both individual privacy and the solutions offered by cybersecurity companies. In the metaverse, patient data security is a challenge. High-tech wearables including glasses, gloves, sensors, and other wearables that can detect patients' vital signs are needed in order to fully utilize the metaverse in healthcare. Technology is constrained since the hardware is still pricey and cannot be accessed by many members of society. The health Metaverse framework primarily focuses on telemedicine and online health management, multimodal medical information standards, the fusion of medical and social data, and medical artificial intelligence. Additionally, it offers crucial inventive dynamism in surgical procedures, medical education, and the interactions between service providers and clients. Technology improvements, gamification of healthcare, patient privacy protection, and barriers stopping individuals from giving up reality are all obstacles. This technological advancement had a lot to offer the ailing medical infrastructure industry, including telemedicine services, virtual examinations employing VR, AR, and blockchain to assure secure data and financial transmissions, as well as a variety of other as-yet untapped possibilities. Data security, data interoperability, and laws are just a few of the difficulties that the healthcare sector in the metaverse will face. To address these issues, new infrastructure, standards, and procedures will be required. This study looks at a number of issues that affect the healthcare industry and how to address them when the metaverse enters the mainstream of healthcare.

  • Significance of Cyber Security of IoT devices in theHealthcare Sector
    Ashley Tuscano and Sujata Joshi

    IEEE
    In recent times, there has been a sudden spike in demand for IoT devices. Humans depend on IoT devices to ease their day-to-day activities, and hence their demand is increasing. But there is growing concern about the vulnerabilities of these devices with their booming growth. IoT devices are prone to cyber-attacks Even the healthcare sector doesn’t shy from the use of IoT devices. But, with the current vulnerabilities that they face, their liability is under question. The healthcare sector has been using IoT devices recently and hence there is a dearth of academic literature with respect to the study of cyber threats and their probable solutions for mitigation. Keeping this research gap in mind, this paper provides an overview of the cyber threats faced by IoT devices in the healthcare sector. More precisely, the paper discusses foreseeable threats and vulnerabilities and provides measures to mitigate or minimize them. This study hopes to provide healthcare providers with a view of the various types of cyber threats occurring due to the usage of IoT devices and how to curtail such risks. It has implications for the IoT infrastructure providers to understand probable frauds and help implement checks while deploying the infrastructure.

  • Impact of E-Service Quality Dimensions on Customer Satisfaction and Loyalty in Online Apparel Shopping in India
    Kala Mahadevan and Sujata Joshi

    Associated Management Consultants, PVT., Ltd.

  • A Review of Brand Anthropomorphism: Analysis of Trends and Research
    Pritha Nasery Ubgade and Sujata Joshi

    Associated Management Consultants, PVT., Ltd.

  • Understanding Leisure Vacation Travel Intention of Indian Vacationers Amidst Coronavirus Disease (COVID-19)
    Kshitija Pandey and Sujata Joshi

    Associated Management Consultants, PVT., Ltd.
    This study aimed to elicit the critical factors influencing Indian domestic vacationers' travel intention for leisure vacation destination choice amidst COVID-19. The study proposed a new model based on the theory of planned behaviour (TPB), expanding it by adding contextual variables like perceived risk, perceived knowledge of COVID-19, and information search behaviour. The study used a quantitative approach using online social media platforms and emails of 312 respondents to analyze and test the hypotheses using IBM SPSS and AMOS tools. The results indicated that physical and functional risk negatively influenced attitude;whereas, psychological risk negatively influenced travel intention. Perceived knowledge of COVID-19 significantly influenced travel intention. Attitude strongly mediated subjective norms, perceived behavioral control, and perceived knowledge of COVID-19 to travel intention. This explains the strong implications for travel destination marketers for marketing safer destination choices to vacationers. © 2022, Associated Management Consultants Pvt. Ltd.. All rights reserved.

  • Omnichannel retailing: a bibliometric and network visualization analysis
    Kala Mahadevan and Sujata Joshi

    Emerald
    PurposeThe purpose of this paper is to review the extant research literature on omnichannel retailing and map the research trends in this field through a bibliometric analysis and network visualization exercise.Design/methodology/approachThis paper employs bibliometric analysis techniques on research literature retrieved from the Scopus and Web of Science databases over the period 2013–2020 and assesses indicators such as research production and citation trends, top contributing authors, countries, journals and organizations through tools offered by the Scopus/Web of Science databases as well as Biblioshiny. A network visualization analysis of patterns such as keyword co-occurrences and co-authorship linkages between contributing countries has been investigated through the use of VOSviewer.FindingsThe bibliometric analysis indicates that research in this field is currently dominated by USA and China with Germany and UK also being key contributors. The analysis has indicated that the field of omnichannel retailing straddles multiple domains such as logistics, distribution, operations and consumer behavior, thereby offering significant future scope for research linking omnichannel retailing with these subject areas.Originality/valueThis study maps the structure of research done in the field of omnichannel retailing and outlines the key contributors in terms of authors, journals and organizations that can serve as an input for future research. The study also identifies possible avenues for future research in the knowledge domain of omnichannel retailing.

  • Emerging Trends and Scope of Healthcare Analytics
    Arnav Gupta and Sujata Joshi

    IEEE
    The Healthcare vertical is highly dependent on excessive volumes of data both structured and unstructured. Despite having the wealth of data, there is a lack of actionable insights as data is overly complex to understand and fragmented. Healthcare analytics is coming up as a transformation in the healthcare industry which will improve the process and practises of decision making in the healthcare field and enhance preventative treatment and diagnostics. (Objective) This research reviews the use of analytics in healthcare with major focus on the current scenario, and its implementation challenges in healthcare settings. It includes a study of the different technologies being currently adopted in healthcare such as big data, predictive and prescriptive analytics to generate precise and informed predictions. A narrative form of literature review approach has been adopted for this study wherein white papers, research papers, articles, blogs etc. have been referred to, studied, and then analysed. Implications: The proposed conceptual framework will help healthcare organizations, professionals, academicians researching big data in healthcare to understand how big data will help arrive at improved healthcare decisions and improve their operational efficiency and performance. Originality. The paper proposes a conceptual framework for using big data analytics for improved decision making and efficiency in operations and performance and will help in understanding the latest trends and implementation of AI, NLP in the patient care and diagnostics medicine.

  • Predictive Analytic Techniques for enhancing marketing performance and Personalized Customer Experience
    Sakshi Gupta and Sujata Joshi

    IEEE
    Marketing has always been seen as more of an art than a science. Marketing analytics has mainly demonstrated marketing's commercial impact by evaluating awareness, interest, and campaign results. However, as new markets are opening due to e-commerce and digitization, having the correct approaches, models, and tools for analyzing the quintillion bytes of data produced regularly is critical. Predictive data analytics is a method that can help with this new challenge as it is a development of previous data analytics models that predicts what will happen in the future by analyzing past data, detecting trends, and using that information to make forecasts about the industry's general trajectory. ML and AI are used to fuel predictive analytics' models and discoveries. These models dictate what and when a user sees across the board, from customer service to social media to FinTech; the technology is highly accurate, making it a valuable tool for firms in various industries that deal with terabytes of data. This research paper aims to discuss the predictive analytics models for enhancing marketing performance and personalized customer experience. It proposes that consumers are ready for a new journey in which predictive analytics is a tool for endless options, and information that is curated in a personalized way and explores how predictive analytics models can accurately determine consumer preferences and add a cognitive component to the otherwise traditionally human-powered and automated tasks.

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