The Impact of Artificial Intelligence on Branding

Varsha, P S; Akter, Shahriar; Kumar, Amit; Gochhait, Saikat; Patagundi, Basanna

doi:10.4018/jgim.20210701.oa10

Cited by 63 publications

(22 citation statements)

References 135 publications

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“…Moreover, previous research has indicated that users' perceptions of quality are among the theoretical foundations of technology acceptance (Varsha et al, 2021), in this case, good quality services and AI. Future research initiatives could undertake a more comprehensive exploration of the interplay between 'customer' and 'data,'…”

Section: Future Research Agendamentioning

confidence: 99%

“…Over the last few decades, the human race has faced a new revolution, the Fourth Industrial Revolution (Faruk et al, 2021). Technical innovations in various fields have attracted great interest from researchers, in fields such as marketing (Abhishek & Srivastava, 2021; Chintalapati & Pandey, 2021; Davenport et al, 2020; Jarek & Mazurek, 2019; Randhawa et al, 2016; Vlačić et al, 2021), business‐to‐business marketing models (Chen et al, 2016; Chen, Jiang, et al, 2022; Han et al, 2021; Jiang, 2021; Kim & Moon, 2021; Saura et al, 2021), branding (Cheng & Jiang, 2021; Varsha et al, 2021), product management (Duong et al, 2022; Haefner et al, 2021) and the employment of social media/influencers for promotional purposes (Abhishek & Srivastava, 2021; Dwivedi et al, 2021; Voorveld et al, 2018; Ye et al, 2021; Zahay, 2021). Artificial intelligence is a flourishing phenomenon that is attracting intense interest from the public, companies and researchers.…”

Section: Introductionmentioning

confidence: 99%

“…Researchers have recognized the potential of AI in this area of marketing; however, the academic literature in the field shows that, while companies have developed intentions to use AI as part of their customer engagement strategies, this has not yet become a reality (Paschen et al, 2019). Some studies used conceptual frameworks to explain the effects of AI on customer behaviours (Hagen et al, 2020; Mariani et al, 2022; Miao et al, 2022; Perez‐Vega et al, 2021; Yau et al, 2021), or briefly referred to the subject in literature analyses of non‐specific fields, such as marketing (Chintalapati & Pandey, 2021; Varsha et al, 2021; Verma et al, 2021). No previous study has comprehensively investigated the literature to establish how AI might be used to improve customer and advertisement engagement (Han et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Impact of artificial intelligence on customer engagement and advertising engagement: A review and future research agenda

Suraña‐Sánchez,

Aramendia‐Muneta

2024

Int J Consumer Studies

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This study, through a bibliometric analysis, aims to provide increased knowledge of the evolution and effects of artificial intelligence over the last 30 years in customer engagement and advertising engagement. Articles were gathered from three databases by using combinations of keywords (artificial intelligence, customer engagement, advertising engagement, marketing, machine learning, etc.). A set of inclusion/exclusion criteria were then applied to obtain the final sample. The final sample was made up of 190 peer‐reviewed articles. Three separate analyses were performed to test the sample. A performance analysis identified the articles' years of publication, contributions per country and the performance/output of the relevant journals. A data analysis created 10 clusters; these are examined in depth, and provide explanations of the evolution of the relevant scientific production. The study's findings offer a wide perspective of research undertaken to date, and identify possible research gaps. This research contributes to the marketing field by bridging a gap, through undertaking a bibliometric analysis, in the research about the impact of artificial intelligence on customer and advertising engagement over the years 1991 through 2022. It offers scholars and researchers ideas for future research.

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Section: Future Research Agendamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Impact of artificial intelligence on customer engagement and advertising engagement: A review and future research agenda

Suraña‐Sánchez,

Aramendia‐Muneta

2024

Int J Consumer Studies

View full text Add to dashboard Cite

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“…The complexity of detecting and averting cyberattacks is growing as we move to a post-pandemic world that has normalized new organizational models, such as working from home. The complexity is exacerbated by the advent of technologies such as artificial intelligence (Akter et al ., 2021; Varsha et al ., 2021), the Internet of Things (Huang et al ., 2021; Peng et al ., 2021), blockchain ( Teoh, 2022) and other highly interoperable systems (Lee et al ., 2021; Xanthidis and Xanthidou, 2021). Not surprisingly, disruptions and shortages due to the COVID-19 pandemic that revealed weaknesses in inter-organizational systems forced increased focus on the resilience of supply chains, resulting in a plethora of research on the topic (Tukamuhabwa et al ., 2015).…”

Section: Literature Reviewmentioning

confidence: 99%

Information systems security resilience as a dynamic capability

Goel

Russell

Williamson

et al. 2023

JEIM

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PurposeWhile the idea of the resilience of information systems security exists, there is a lack of research that conceptualizes, defines and specifies a way to measure it as a dynamic capability. Drawing on relevant cybersecurity and dynamic capabilities literature, this study aims to define Information Systems Security Resilience (ISSR) as a “dynamic capability of a firm to respond to, and recover from, a security attack” and test it as a new construct.Design/methodology/approachThe authors employ a methodology including multiple phases to develop and test this construct of ISSR. The authors first interview senior managers from various organizations to establish the face validity of the construct; then develop and analyze a pilot survey for internal validity and reliability; and finally, design and deploy a field survey to test and externally validate the construct.FindingsThe authors conceptualize and define the construct of ISSR as a dynamic capability, develop a scale for its measurement and test it in a pilot and field survey. The construct is valid, and the measurement tool works. It demonstrates that resilience is something that is done, rather than had. As a capability, organizations need to track and measure ISSR, which is what this tool provides the ability to do.Originality/valueThis research contributes to the information systems and cybersecurity literature and offers valuable insights for organizations to manage their security effectively.

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“…UPS and FedEx developed blockchain pilot projects to enhance their logistics systems. Furthermore, AI and big data technologies can be leveraged to analyze on-chain data and provide near real-time analytics and recommendations to relevant stakeholders through customized dashboards (Rabah, 2018;Sultana et al, 2021;Varsha et al, 2021).…”

Section: The Iot and Blockchain Technologiesmentioning

confidence: 99%

Mitigating information asymmetry in inventory pledge financing through the Internet of things and blockchain

Lixin

Zhang

et al. 2021

JEIM

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PurposeRisks resulted from asymmetric information have become crucial barriers for commercial banks to implement supply chain finance (SCF) – mainly the inventory pledge financing (IPF). At the same time, online financial service providers (OFSPs) are emerging as strong competitors in the SCF market. As a result, commercial banks need to update their traditional SCF business models and alleviate their over-dependence on OFSPs.Design/methodology/approachThe authors employ a multi-case-study method to investigate how the Internet of things (IoT) and blockchain technologies can be jointly leveraged to mitigate SCF risks. In-depth interviews were conducted to depict the business models and their novel ecosystem to reinforce traditional banks' ability in SCF services.FindingsFrom the perspective of information asymmetry, the authors categorize IPF risks into three groups based on the principal-agent theory: collateral, warehousing and liquidity risk. The findings suggest that IoT can primarily improve traditional banks' information acquisition ability, and blockchain can facilitate credible information transformation, enabling banks to acquire knowledge from collaterals. Besides, the e-platform in the new architecture increases banks' involvement in the supply chain and builds a fair network to curtail warehousing risks. The employment of smart contracts and collaborative mechanism ensure process and outcome control in mitigating liquidity risks.Originality/valueThe research contributes to the literature by confirming the role of emerging technologies in reducing information asymmetry risks. Besides, the findings provide valuable insights for practitioners to promote effective practices and approaches in IPF.

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The Impact of Artificial Intelligence on Branding

Cited by 63 publications

References 135 publications

Impact of artificial intelligence on customer engagement and advertising engagement: A review and future research agenda

Impact of artificial intelligence on customer engagement and advertising engagement: A review and future research agenda

Information systems security resilience as a dynamic capability

Mitigating information asymmetry in inventory pledge financing through the Internet of things and blockchain

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