Beyond the post: an SLR of enterprise artificial intelligence in social media DOI Creative Commons

Luis-Alfonso Maldonado-Canca,

Ana María Casado Molina, Juan-Pedro Cabrera-Sánchez

et al.

Social Network Analysis and Mining, Journal Year: 2024, Volume and Issue: 14(1)

Published: Nov. 20, 2024

Abstract This study explores the impact of artificial intelligence (AI) on brand communication within corporate social networks, analyzing its benefits, ethical and technical challenges, proposing responsible implementation strategies enriched with new theoretical contributions. To achieve this, a systematic literature review (SLR) was conducted based SPAR-4-SLR methodology by Paul et al. (2021), using 57 studies from Scopus Web Science over past six years. approach complemented recommendations Kitchenham Charters (2007) to ensure rigor thoroughness in analysis. The reveals that transforms interactions networks enabling effective personalization, optimizing customer experience, enhancing satisfaction. Benefits include precise segmentation, predictive analytics, service optimization through chatbots. However, significant challenges also emerge, such as data privacy, algorithmic bias, lack transparency AI models. necessity for practices regulations foster user trust mitigate risks associated digital is emphasized.

Language: Английский

Reinventing Influence of Artificial Intelligence (AI) on Digital Consumer Lensing Transforming Consumer Recommendation Model DOI
Bhupinder Singh, Christian Kaunert,

Komal Vig

et al.

Advances in marketing, customer relationship management, and e-services book series, Journal Year: 2024, Volume and Issue: unknown, P. 141 - 169

Published: March 1, 2024

The rapid evolution of artificial intelligence (AI) has ushered in a transformative era the digital consumer landscape. In era, symbiotic relationship between technology and behavior undergone seismic shift, with emerging as force shaping experience. At this paradigm shift lies intricate interplay AI recommendation models, which have evolved from rudimentary algorithms to sophisticated systems capable personalization tailored engagement. This chapter delves into consumers, focusing specifically on profound influence reshaping models and, consequently, molding shopping decisions. study is driven by overarching objective comprehensively understanding impact decision-making processes contemporary consumers realm.

Language: Английский

Citations

62
Engaging consumers through artificially intelligent technologies: Systematic review, conceptual model, and further research DOI Creative Commons
Linda D. Hollebeek, Choukri Menidjel, Marko Sarstedt

et al.

Psychology and Marketing, Journal Year: 2024, Volume and Issue: 41(4), P. 880 - 898

Published: Jan. 4, 2024

Abstract While consumer engagement (CE) in the context of artificially intelligent (AI‐based) technologies (e.g., chatbots, smart products, voice assistants, or autonomous cars) is gaining traction, themes characterizing this emerging, interdisciplinary corpus work remain indeterminate, exposing an important literature‐based gap. Addressing gap, we conduct a systematic review 89 studies using Preferred Reporting Items for Systematic reviews and Meta‐Analyses (PRISMA) approach to synthesize AI‐based CE literature. Our yields three major CE, including (i) Increasingly accurate service provision through ; (ii) Capacity (co)create consumer‐perceived value , (iii) CE's reduced effort their task execution . We also develop conceptual model that proposes antecedents personal, technological, interactional, social, situational factors, consequences consumer‐based, firm‐based, human‐AI collaboration outcomes. conclude by offering pertinent implications theory development future research questions derived from proposed CE) practice reducing costs brand/firm interactions).

Language: Английский

Citations

50
Consumers' continuance intention towards metaverse-based virtual stores: A multi-study perspective DOI
Debarun Chakraborty, Aruna Polisetty, Nripendra P. Rana

et al.

Technological Forecasting and Social Change, Journal Year: 2024, Volume and Issue: 203, P. 123405 - 123405

Published: April 18, 2024

Language: Английский

Citations

36
Artificial Intelligence (AI) in Tourism DOI
Seden Doğan,

İlayda Zeynep Niyet

Emerald Publishing Limited eBooks, Journal Year: 2024, Volume and Issue: unknown, P. 3 - 21

Published: Jan. 22, 2024

Artificial Intelligence (AI) has revolutionised the tourism industry, offering personalised experiences and streamlining operations. AI provides customised recommendations for travellers through data analysis machine learning, making their journeys more meaningful. It also improved efficiency automated processes, chatbots enhanced security measures. AI's ability to analyse large volumes of enables organisations make data-driven decisions target marketing strategies effectively. One most notable contributions in is its offer recommendations. By analysing vast travel history, preferences online behaviour, systems can provide tailored suggestions destinations, accommodations, activities dining options. This level customisation enhances overall experience, it relevant satisfying individual travellers. greatly operational within sector. Chatbots, powered by natural language processing, are increasingly being deployed hotels, airlines agencies instant customer support assistance. These answer queries, handle booking reducing waiting times enhancing satisfaction. In addition, facial recognition technology allows quick accurate identity verification at airports, hotels other travel-related facilities. improves with a seamless efficient experience. As advances, we expect play prominent role augmented reality, voice virtual assistants, further experience facilitating interactions. conclusion, transformed industry providing recommendations, improving efficiency, measures enabling destination management.

Language: Английский

Citations

17
Anthropomorphic generative AI chatbots for enhancing customer engagement, experience and recommendation DOI
Aman Kumar, Amit Shankar, Abhishek Behl

et al.

Journal of Consumer Marketing, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 31, 2025

Purpose This research focuses on developing and testing a conceptual model that explores customer behavioural responses (engagement, experience recommendation) towards generative artificial intelligence (AI)-enabled chatbots. It highlights the significant influence of anthropomorphic characteristics in enhancing perceptions competence warmth, further perceived authenticity. In addition, this study aims to investigate how need for social interactions moderates these relationships. Design/methodology/approach used self-administered questionnaire distributed Prolific Academic gather data from 282 eligible participants worldwide. uses structural equation modelling approach answer questions. Findings The findings reveal AI-enabled chatbots are positively associated with competence. Moreover, show warmth significantly Furthermore, results highlight authenticity is engagement, recommendation. Finally, illustrate impact moderated by interaction. Originality/value enriches AI literature guides organizations understanding consumer leveraging contributes response theory as investigates user intentions influenced their level characteristics.

Language: Английский

Citations

2
The adoption of metaverse in the retail industry and its impact on sustainable competitive advantage: moderating impact of sustainability commitment DOI
Rabab Ali Abumalloh, Mehrbakhsh Nilashi, Keng‐Boon Ooi

et al.

Annals of Operations Research, Journal Year: 2023, Volume and Issue: 342(1), P. 5 - 46

Published: Sept. 27, 2023

Language: Английский

Citations

40
How do consumers react to chatbots' humorous emojis in service failures DOI
Dewen Liu, Yiliang Lv, Weidong Huang

et al.

Technology in Society, Journal Year: 2023, Volume and Issue: 73, P. 102244 - 102244

Published: April 11, 2023

Language: Английский

Citations

38
Investigating the impact of artificial intelligence on human resource functions in the health sector of China: A mediated moderation model DOI Creative Commons
Muhammad Farrukh Shahzad, Shuo Xu, Waliha Naveed

et al.

Heliyon, Journal Year: 2023, Volume and Issue: 9(11), P. e21818 - e21818

Published: Nov. 1, 2023

Artificial intelligence (AI) is rapidly transforming the way human resources (HR) functions are carried out in health sector of China. This study aims to scrutinize impact artificial on resource operating healthcare through technological awareness, social media influence, and personal innovativeness. Additionally, this examines moderating role perceived risk between awareness functions. An online questionnaire was administered professionals China gather data from 363 respondents. Partial least squares structural equation modeling (PLS-SEM), a statistical procedure, implemented investigate hypothesis projected model The research findings reveal that significantly influences Furthermore, moderates relationship have important implications for HR practitioners policymakers sectors China, who can leverage technologies optimize improve organizational performance. However, its adoption needs be carefully planned managed reap full benefits transformative technology.

Language: Английский

Citations

30
Influence of perceived value on omnichannel usage: Mediating and moderating roles of the omnichannel shopping habit DOI Creative Commons
Neeru Sharma, Johra Kayeser Fatima

Journal of Retailing and Consumer Services, Journal Year: 2023, Volume and Issue: 77, P. 103627 - 103627

Published: Nov. 24, 2023

Today, most retail profits are driven by customers' habitual buying behaviour. However, there is a lack of comprehensive theoretical understanding regarding how omnichannel habit affects perceived value and usage. This study uses customer theory to investigate the various roles shopping (as antecedent, mediator moderator) in retail. To achieve this goal, survey data from 512 shoppers Australia was analysed using partial least squares method with SmartPLS software version 3. The findings confirm that plays significant as mediator, moderator relationship between Additionally, reveals positive impact factors such security privacy, seamless experience, personalisation, social communications. research expands upon examining complex relationships aspects habit, For marketers looking strengthen buying, suggests prioritising promoting communications, offering personalised services. Recognising integral influence on perception usage, should adopt cohesive strategy for communicating their propositions target customers across multiple channels. approach can ultimately boost

Language: Английский

Citations

24
Decoding Gen Z: AI's influence on brand trust and purchasing behavior DOI Creative Commons
Cristóbal Rodolfo Guerra-Tamez, Keila Kraul Flores,

Gabriela Mariah Serna-Mendiburu

et al.

Frontiers in Artificial Intelligence, Journal Year: 2024, Volume and Issue: 7

Published: March 4, 2024

This study focuses on the role of AI in shaping Generation Z's consumer behaviors across fashion, technology, beauty, and education sectors. Analyzing responses from 224 participants, our findings reveal that exposure, attitude toward AI, accuracy perception significantly enhance brand trust, which turn positively impacts purchasing decisions. Notably, flow experience acts as a mediator between trust These insights underscore critical developing influencing choices among Z, offering valuable implications for marketers an increasingly digital landscape.

Language: Английский

Citations

11