JTMGE Vol. 14 No. 2 (October 2023)

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https://demodspace.chitkara.edu.in/handle/123456789/320

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Browsing JTMGE Vol. 14 No. 2 (October 2023) by Title

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    Analytical Study of Automatic Speech Recognition and Linked Profile as a Tool for Effective Advertising
    (Chitkara University Publications, 2023-10-15) Ruchika Jeswal; Ruchi Jain; Shreya Tripathi
    Background: Automatic speech recognition, more commonly known as voice search, and linked profiles have emerged as powerful tools for advertising. Voice search allows users to perform searches using their voice instead of typing, while linked profiles enable users to connect their various online accounts to create a comprehensive profile. These technologies offer new opportunities for advertisers to reach their target audience in a more personalized and effective way. Purpose: This study sheds light on the potential impact that voice assistants have on consumer brands. It also intends to study the impact of LinkedIn profiles as a key effective advertising tool. Methods: The data was collected from a sample size of 100 customers. It was analyzed using correlation and regression analysis. Results: The results of the study reveal that voice-linked search is fast becoming a focal point in marketing because of its swift adoption and disruptive potential in creating buying dynamics. Conclusions: Correlation analysis helps conclude that there is a moderately positive correlation between age and usage patterns of voice search. The results suggest that effective advertising through voice search and linked profiles requires a deep understanding of the target audience, their interests, and their behavior. Advertisers must also develop creative and engaging ad content that aligns with the user’s intent and preferences. Additionally, they must carefully consider the context in which the ad is presented, as voice-based advertising may feel intrusive if not executed properly.
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    Analyzing the Drivers of Customer Chatbot Adoption in the Banking Industry
    (Chitkara University Publications, 2023-10-15) Manoj Govindaraj; Ravishankar Krishnan; Jenifer Lawrence
    Background: The integration of Artificial Intelligence (AI) chatbots into various industries has become a significant trend, with the banking sector being one of the key adopters. AI chatbots are designed to simulate human conversation, offering automated responses to customer queries. Their use in the banking industry aims to streamline customer service and improve efficiency. However, understanding the factors that influence customers’ willingness to use chatbot services remains crucial for banks in optimizing these technologies. Factors such as perceived usefulness, ease of use, trust, privacy concerns, and customer satisfaction play vital roles in determining the acceptance of chatbot services in banking. Purpose: The purpose of this research is to identify and analyze the factors that influence customer intention to use chatbots in banks. By investigating these factors, the study seeks to provide banks with actionable insights to improve their chatbot services, enhance customer engagement, and increase customer satisfaction. The research also aims to assess the role of various technological aspects such as the chatbot interface, content, safety, and convenience in shaping customer decisions to adopt this technology. Methods: This study employs a quantitative research approach, utilizing a structured questionnaire to gather data from a sample of 250 bank customers. The questionnaire assesses several key factors, including perceived usefulness, perceived ease of use, trust, privacy concerns, and customer satisfaction. The collected data is then analyzed using statistical techniques, including regression analysis and structural equation modeling (SEM), to test the Technology Acceptance Model (TAM) and examine the relationships between the identified factors. Results: The analysis reveals significant relationships between customer intention to use chatbot services and factors such as perceived usefulness, trust, and ease of use. Customers’ satisfaction with the interface, content, and security of the chatbot also plays a critical role in their willingness to adopt this technology. The study confirms that perceived convenience and safety strongly influence customers’ decision to engage with AI-driven chatbots in banks. Conclusions: The findings of this research provide valuable insights into the factors affecting customer acceptance and intention to use chatbots in the banking sector. Financial institutions can use these insights to tailor their chatbot services, ensuring they address customer concerns related to trust, security, and ease of use. The results also highlight the importance of designing user-friendly interfaces and ensuring the safety of customer data. By understanding these factors, banks can improve customer satisfaction, foster trust, and promote the adoption of AI-driven services, benefiting both customers and service providers in the long term.
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    Consumer Skepticism and Trust in Influencer Marketing: A Cross-Platform Analysis of Mobile and Web Users
    (Chitkara University Publications, 2023-10-15) Bharti Shukla
    Background: Grounded on the differences created among various platforms, this research on consumer skepticism and trust in influencer marketing analyzes how such differences impact consumer perceptions and purchase decisions. Purpose: This study aims to understand various dynamics of influencer marketing by comparing its impact across different platforms. Methods: Using an online survey as a source of data, the attitude of consumers towards products endorsed by influencers is determined both on Instagram and YouTube. Results indicate that mobile users are more favorable toward influencer marketing while web users tend to be more skeptical, especially when the endorsements seem too commercial in nature. Results: A chi-square test highlighted a significant relationship between the type of platform utilized and consumer trust in the recommendations received from influencers. Conclusion: The study revealed that there is not as uniform a relationship between a type of platform and the level of trust placed in the influencer recommendations, and study results indicate implications for brands to better manage their marketing efforts across disparate online platforms.
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    Direct Benefit Transfer (DBT) in India: A Strategic Assessment of Technology-Driven Welfare Delivery
    (Chitkara University Publications, 2023-10-15) Priyanka Sharma; Shefali Sharma; Barsha Rani
    Background: The Direct Benefit Transfer (DBT) initiative in India has emerged as a pivotal reform in public welfare management by leveraging digital infrastructure to enhance the efficacy, transparency, and accountability of subsidies and benefit distribution. Purpose: The purpose of this study is to evaluate the efficacy of DBT in improving welfare delivery, examine its evolution, enablers, and challenges, and propose strategic recommendations for its optimization. Methods: A comprehensive literature review utilizing peer-reviewed articles and policy reports on direct benefit transfer (DBT) in India is conducted. The key findings are systematically synthesized, and public value theory is applied to evaluate DBT’s impact on public service delivery. Subsequently, a SWOT analysis is employed to examine the program’s strengths, weaknesses, opportunities, and threats, providing managerial insights for enhancing public welfare through DBT in India. Results: DBT has significantly enhanced transparency, reduced fraudulent activities, and promoted financial inclusion through the JAM trinity. However, exclusion errors, limited rural banking infrastructure, and digital literacy barriers hinder its broader impact. Strategic opportunities exist in the implementation of emerging technologies and public-private collaborations to expand DBT’s reach and efficacy. Conclusion: DBT exerts a substantial influence on the transformation of public welfare in India through the promotion of social equity and economic empowerment. The results suggest policy actions to optimize the program’s effectiveness, including improving the process of identifying beneficiaries, broadening initiatives to enhance digital literacy, and utilizing cutting-edge technologies such as artificial intelligence and blockchain to achieve superior welfare outcomes.
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    Fostering Dynamic Learner engagement in the Era of Blended learning- A Systematic Review
    (Chitkara University Publications, 2023-10-15) Rajiv Saini; Shuchi Dawra
    Background: Blended learning, which integrates traditional face-to-face education with digital and online elements, has gained prominence in recent years as an efficient method to enhance student learning experiences. But it is worth noting that the completion rate of online and blended learning courses continues to be rather low, as indicated by research. Purpose: The purpose of the current study is to understand the intellectual discussion around dynamic learner engagement in the era of blended learning so that the areas of improvement can be earmarked and suitable strategies can be made to bridge the gap between the expectations of the learners and the actual delivery of the course. Method: A systematic review was conducted using a replicable search strategy. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 (PRISMA 2020) statement guided this study. Many keyword combinations were searched for in the title, keyword, and abstract fields, according to the search criteria to arrive at the final corpus of papers. Result: The study found that in order to cultivate learners’ engagement in blended learning, it is imperative to investigate the various aspects that impact learning engagement. Through a comprehensive analysis of existing scholarly literature, the present study has successfully discovered a range of both internal and external elements that exert a significant influence on the level of engagement exhibited by learners. Active participation by students and innovative pedagogical tactics emerged as significant aspects in a blended learning format, allowing designers and educators to provide targeted support for students. Conclusion: The study provides clear insights for instructors into the various methods to strategize around in the blended learning environment format so that they can design better pedagogical tactics and increase learner engagement through active student participation.
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    Identifying the Dimensions of ‘Technology Derived Value Proposition’ in Apartment Purchase Behavior: An Exploratory Factor Analysis Approach
    (Chitkara University Publications, 2023-10-15) Devyani Sharma; Sandeep Singh
    Background: Real estate decision-making is inherently multi-dimensional, increasingly shaped by the pervasive role of technology in modern consumption patterns. Technology-driven value propositions (TDVPs) have gained prominence due to their impact on customer behaviors, especially in apartment purchasing decisions. Despite existing literature on data-driven marketing, gaps remain in understanding TDVP dimensions and their measurement validity within the real estate context. Purpose: This study aims to identify and validate the factors influencing technology-derived value propositions in apartment purchasing behavior through exploratory factor analysis (EFA). It integrates work behavior and technology-driven marketing insights to establish a comprehensive assessment of the phenomenon. Methods: A structured questionnaire, informed by theoretical frameworks and expert opinions, was administered to a diverse sample of 425 participants (179 females, 246 males). Using principal component analysis with varimax rotation, EFA identified latent dimensions of TDVPs. Reliability and validity assessments of measurement items were conducted via SPSS to ensure data adequacy and factor dimensionality. Results: Twelve key factors were identified as contributors to TDVPs in real estate decision-making. These included market orientation, AI-induced biases, customer work behavior, builder technology usage, and credit availability, among others. The analysis revealed significant correlations between these factors and their influence on shaping customer decisions, supported by high sample adequacy (KMO = 0.845) and significant Bartlett’s Test results (p < 0.001). Conclusion: The study highlights the critical dimensions of TDVPs in apartment purchase behavior, emphasizing their theoretical and practical implications. It underscores the transformative role of technology in shaping consumer decisions and offers validated measurement constructs for further research and application in real estate marketing strategies. Future studies could explore additional dimensions, such as augmented reality and machine learning, to further refine the understanding of TDVPs.
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    Impact of AI and Machine Learning on Supply Chain Optimization in Developing Economies
    (Chitkara University Publications, 2023-10-15) Neha Soni
    Background: Emerging economies face various unsolved issues that limit supply chain development, such as inefficiency and unhealthy competition, lack of transparency, and an underdeveloped technological infrastructure. Purpose: This paper describes how Artificial Intelligence and Machine Learning can solve these problems with the help of supply chain management in various areas. In developing economies, Artificial Intelligence and Machine Learning are transforming supply chain management, offering exceptional opportunities for optimization. In this paper, the innovative potential of AI and Ml is explored, as how this technology enhances supply chain efficiency, minimizing operational cost and optimizing decision-making in resource bottleneck environments. There are so many unique challenges in developing economies that impact supply chain performance such as gaps in infrastructures, partial access to data, and irregular market conditions. Methods: A literature review of recent studies and reports on AI and ML applications. This paper discusses the concept of the supply chain, artificial intelligence, and machine learning, and recent applications of Artificial Intelligence and Machine Learning in the processes of the supply chain, it analyses critical constraints to adoption, skill gaps, investment hurdles as well as technological readiness. Results: AI and Machine learning-driven technologies are strengthening organizations in various fields to better presume trends in the market such as decreasing the lead time, enhancing the level of inventory, probabilistic analytics, and market forecasting. This paper studies the continuous with a discussion of how supply chain optimization using AI and ML might promote long-term economic growth in developing nations and policy recommendations to encourage wider usage of these technologies. Conclusion: In the end, Artificial Intelligence and Machine learning are critical instruments for enhancing supply chain competitiveness and resilience in the face of external economic challenges. Even though there are some hurdles such as technological adaptation and infrastructural requirements, the implementation of AI and ML can help improve supply chain efficiency and enable economic growth in emerging economies.