Emerging Technologies in Insurance Sector: Evidence from Scientific Literature

Zia, Adil; Kalia, Prateek

doi:10.1108/978-1-80262-605-620221004

Cited by 6 publications

(4 citation statements)

References 53 publications

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“…Consequently, reducing the store's OT is a viable alternative for minimizing contact between individuals. The fast clearance at the billing counters during COVID-19 reduces the likelihood of covid exposure (Sharma et al , 2021); this is possible with the use of electronic money and the adaption of artificial intelligence which is very popular in the insurance sector (Zia and Kalia, 2022), as it does not consume time for exchanging cash (Matić et al , 2020).…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, to minimize contact from person to person, limiting the store operating time (OT) is another practical option. The fast clearance at the billing counters during COVID-19 reduces the chances of covid exposure (Sharma et al, 2021); this is possible with the use of electronic money and the adaption of artificial intelligence which is very popular in the insurance sector (Zia and Kalia, 2022), as it does not consume time for exchanging cash and change (Mati c et al, 2020). Therefore, during the COVID-19 pandemic, this factor has been included to explain the SE and the following hypothesis has been formulated to test the impact of OT on PPSE: H3.…”

Section: Operating Time (Ot)mentioning

confidence: 99%

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COVID-19 pandemic shopping experience in Saudi Arabia: scale development and validation for Muslim customers

Zia

Kalia²

2023

JIMA

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Purpose This study aims to, first, propose a valid and reliable scale to document the COVID-19 Pandemic Shopping Experience (CPSE) and, second, determine the impact of its variables on the postpurchase shopping experience (PPSE). Design/methodology/approach For scale development, published studies were scanned and the variables were shortlisted. These shortlisted variables were validated by 52 faculties from four universities in Saudi Arabia. Data were collected from 318 respondents to purify the CPSE Scale. In Study 2, a path analysis was performed on a sample of 354 respondents to determine the individual impact of each variable on PPSE. Findings A total of 14 items were found to be aligned under four variables, social distance (SD), shop hygiene, operational time and entertainment venues. SD was found to have the greatest influence on PPSE, followed by operational time and shop hygiene. Practical implications This research has important implications for retailers to initiate changes in store layout so that they can implement social distancing by physically marking stickers on the floors and by placing barricading on billing counters. Store hygiene can be ensured by making sanitizers and hand gloves available at the entry points, periodically cleaning the floor and sanitizing the premises. Rationing the operating time proved to be an effective tool to minimize the exposure time, thereby limiting consumers' time inside the store. Originality/value To the best of the authors’ knowledge, this is the first study to propose a full-scale measure of the customer shopping experience (SE) during a pandemic. This scale can be generalized to measure SE in similar situations.

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Section: Resultsmentioning

confidence: 99%

Section: Operating Time (Ot)mentioning

confidence: 99%

COVID-19 pandemic shopping experience in Saudi Arabia: scale development and validation for Muslim customers

Zia

Kalia²

2023

JIMA

Self Cite

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“…Some previous literature has examined how wearables improve customer satisfaction and engagement (Seth & Gulati, 2022). Moreover, systematic literature reviews have found that social media is the most frequently cited modern technology (Zia & Kalia, 2022). Other work has found that the insurance sector has begun to develop big data solutions based on blockchain in the contexts of fraud prevention and risk assessment (Trivedi & Malik, 2022).…”

Section: Big Data Analytics In Insurance Companiesmentioning

confidence: 99%

Practices and barriers for big data projects

Terlizzi,

De Oliveira,

Francisco

2024

GeP

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The adoption of big data analytics is increasing in every major industry, demanding investments in new projects, technologies, architectures, and processes to allow the integration of big data platforms with legacy systems; however, many organizations have failed to incorporate it effectively into their decision-making processes and project benefits have not been adequately captured. This study aims to further investigate how a big data analytics project can be implemented in insurance companies. A case study was conducted on one of the largest insurance companies in Brazil with interviews and document analysis. The study identified five main practices that were adopted to successfully implement a big data analytics project (implement automatic autoscaling alerts, use specialized big data tools, integrate the platform with legacy systems, comply with privacy legislation, and ensure the documentation of technical architecture using business process modeling), as well as four barriers that prevent its proper adoption (complexity of access to multicloud data sources, high processing requirements of unstructured data analysis, failure to attend to business necessities at the right time, and project delays brought by bureaucratic interdepartmental processes); some of these have not previously been identified. Finally, an action plan to address these issues is presented.

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“…In light of digital data, insurance businesses can rapidly respond to customer needs and concentrate more on value-added services. To keep insurance firms from becoming unresponsive to these technological advancements, AI technologies also help with efficient client communication, cost reduction, developing new goods and services and improving the competitive climate (Zia and Kalia, 2022).…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence service agents: a silver lining in rural India

Ravi

Vedapradha²

2023

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PurposeThe study aims to examine the impact of an artificial intelligent service agent (AISA) on customer services to the rural population provided by KAYA, Kotak Life's AI-enabled insurance chatbot avatar that offers quality insurance services.Design/methodology/approachMulti-stage cluster sampling method was adopted to collect the responses from the 707 customers across the rural population of southern states of India. SPSS V.2 and Smart PLS 4 were used to apply simple percentage analysis, multiple linear regression analysis, and structural equation modeling (SEM) to validate the hypothesis. The dependent variables are economic performance and market performance based on the independent variables: efficiency, security, availability, enjoyment and contact.FindingsThe study revealed that efficiency and security are the highest predictors and the most influencing variables in predicting the economic and market performance of the insurance companies in determining the quality of service when rendered through AISA among the customers. Efficiency, security, availability, contact and enjoyment are the critical dimensions of AISA. It has a more significant impact on quality service (claim processing) to the rural population. It improves the economic and market performance among the insurance companies and the rural population.Originality/valueCustomers need convenience when making claims. Even little challenges might lead to stress and unhappiness, depending on the situation. Restrictions on where customers can file claims may not be the most outstanding service insurance firms can offer, given rising travel and commuting costs and widening geographical borders. Customers value proactive communication from service providers about the status of their insurance claims.

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Emerging Technologies in Insurance Sector: Evidence from Scientific Literature

Cited by 6 publications

References 53 publications

COVID-19 pandemic shopping experience in Saudi Arabia: scale development and validation for Muslim customers

COVID-19 pandemic shopping experience in Saudi Arabia: scale development and validation for Muslim customers

Practices and barriers for big data projects

Artificial intelligence service agents: a silver lining in rural India

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