Consumer engagement with self-driving cars: a theory of planned behavior-informed perspective

Hollebeek, Linda D.; Menidjel, Choukri; Clark, Moira; Sigurðsson, Valdimar

doi:10.1108/apjml-08-2022-0654

Cited by 5 publications

(5 citation statements)

References 74 publications

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“…First, expected AI benefits refer to consumers' anticipated benefit of (using) specific AI technologies (Bertrandias et al, 2021; Kumar et al, 2021). Key constructs examined in this area include perceived usefulness (Moriuchi, 2019), perceived ease‐of‐use, expected value (McLean et al, 2021), trust (Mostafa & Kasamani, 2022), distrust (McLean et al, 2021), attitude toward the AI (Hlee et al, 2022), anticipated AI gratifications (Lin & Wu, 2023), and perceived behavioral control (Hollebeek, Menidjel, et al, 2022), among others. Consumers, thus, tend to assess upfront what they expect to get (vs. give) in their AI interactions (Zeithaml, 1988).…”

Section: Resultsmentioning

confidence: 99%

Engaging consumers through artificially intelligent technologies: Systematic review, conceptual model, and further research

Hollebeek,

Menidjel,

Sarstedt

et al. 2024

Psychology and Marketing

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

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Section: Resultsmentioning

confidence: 99%

Engaging consumers through artificially intelligent technologies: Systematic review, conceptual model, and further research

Hollebeek,

Menidjel,

Sarstedt

et al. 2024

Psychology and Marketing

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“…Both an exact method and a heuristic approach are provided for solving the distribution path problem in domestic logistics. Particle swarm, annealing, and hillclimbing algorithms were recently shown [17]. It was Toshihide academics who first employed the evolutionary algorithm to study the path optimization problem using time frames.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, a Lagrangian interpolation method is proposed. If the index in year , then the index in year is calculated as: (17) In this paper, type interpolation polynomial is used to prevent the occurrence of Runge oscillation. Classification is to unify the indices with similar meaning and a high degree of correlation into one category.…”

Section: Preliminary Preparation Of the Indexmentioning

confidence: 99%

Economic Growth Forecast Model Urban Supply Chain Logistics Distribution Path Decision Using an Improved Genetic Algorithm

Al Moteri,

Khan,

Alojail

2023

MJCS

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The proposed way of estimating the associated macroeconomic index based on a supply chain network is a novel strategy that might give useful insights for firms and organizations trying to enhance their supply chain logistics operations. To aid in this process, the suggested technique utilizes multiple regression analysis and adaptive extreme learning machine models to determine the relative importance of each indicator in the supply chain logistics decision-making process. Firms may be able to save expenses and boost economic value by employing enhanced genetic algorithms and mathematical modeling to develop a logistics distribution model for urban supply chains based on reconstructive genetic algorithms. It is possible that the paper's study of the issues plaguing the current distribution of logistics in metropolitan areas would prove valuable to organizations that are seeking to improve their own supply chain logistics procedures. This paper appears to take a thorough strategy that might assist assure the accuracy of the forecasting method by preprocessing macroeconomic indicators using imputation, classification, and other approaches to generate a time series consistency model. Some novel ways that may shed light on logistics in the supply chain include using a two-dimensional discrete mesh structure built on wireless sensors to depict the national economic development scenario and using a coding matrix to convey the extent of economic growth. A thorough and all-encompassing way that might aid organizations in making better judgments about their supply chain logistics strategies is to employ multiple regression analysis, an adaptive extreme learning machine model, and other approaches to examine the effect degree of each key indicator. Experiments demonstrating excellent and steady prediction accuracy of the algorithm model are encouraging and point to the possible usefulness of the suggested forecasting approach. The paper's contributions, including its analysis of supply chain logistics cost accounting, determination of the basic path optimization, improvement of the genetic algorithm, and design of the mathematical model of supply chain logistics distribution path decision, all look promising in terms of their potential to help businesses cut costs and boost economic value. The positive outcome of the successful design of the mathematical model demonstrates the possible efficacy of the suggested technique.

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“…Moreover, earlier studies have investigated the effects of CE on purchase intentions by incorporating hope and compulsion as the independent variables (Eisingerich et al, 2019; Hollebeek et al, 2022). The incorporation of psychological variables has resulted in increased CE.…”

Section: Literature Reviewmentioning

confidence: 99%

Gamification in financial service apps to enhance customer experience and engagement

Raza,

Rehmat,

Ishaq

et al. 2023

J of Consumer Behaviour

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The purpose of this research is twofold: (1) to examine the impact of gamification on user experiences, customer engagement, and purchase intentions in the financial industry and (2) to examine how immediate rewards vs. delayed rewards prompt different consumer behaviors in Pakistan's mobile banking (m‐banking) industry. The data for study 1 was collected from 340 m‐banking customers using a structured, survey‐based questionnaire, while study 2 collected data from 161 customers using an experimental‐based study. The structural equation modeling results show that gamification elements are positively related to user experiences and customer engagement, and customer engagement mediates the relationship between gamification and purchase intentions. Moreover, the results of the experimental‐based study show that immediate rewards more strongly influence user experiences, customer engagement, and purchase intentions. For theoretical implications, this study extends the scant literature on gamification in the m‐banking industry. For managerial implications, this research highlights the essential elements of gamification to increase customer engagement and purchase intentions in the m‐banking sector.

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Consumer engagement with self-driving cars: a theory of planned behavior-informed perspective

Cited by 5 publications

References 74 publications

Engaging consumers through artificially intelligent technologies: Systematic review, conceptual model, and further research

Engaging consumers through artificially intelligent technologies: Systematic review, conceptual model, and further research

Economic Growth Forecast Model Urban Supply Chain Logistics Distribution Path Decision Using an Improved Genetic Algorithm

Gamification in financial service apps to enhance customer experience and engagement

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