Measuring Online Sensory Consumer Experience: Introducing the Online Sensory Marketing Index (OSMI) as a Structural Modeling Approach

Hamacher, Kevin; Buchkremer, Rüdiger

doi:10.3390/jtaer17020039

Cited by 13 publications

(10 citation statements)

References 89 publications

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“…Finally, customers associate services or products with their current use state and sensory appeal [28]. The technological sensory appeal includes features that stimulate vision, sound, smell, taste, or touch [29].…”

Section: Customer Value and Digital Transformationmentioning

confidence: 99%

Enhancing Digital Presence for Maximizing Customer Value in Fast-Food Restaurants

et al. 2023

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Digital transformation has altered the way customers interact with restaurants. As a result, digital transformation has had an enormous impact, changing restaurant customer value. Therefore, this research aims to develop a dynamic and sustainable method for creating customer value in digital transformation. This study suggested digital dynamic capabilities and digital customer orientation as a process model (i.e., sensing, seizing, transforming, and refining) to develop digital transformation and create a dynamic customer value. We chose fast-food restaurants in Egypt to examine the proposed model using a qualitative approach of semi-structured interviews with fast-food managers and hospitality experts. The findings indicated that fast-food restaurants’ different digital transformation capabilities and tools (e.g., value innovation, SWOT analysis, artificial intelligence, new technology selection criteria, digital maturity, building several digital platforms, and gathering and analyzing customers’ online reviews) help them create customer value. However, interviewees highlighted how well-suited the suggested digital dynamic capabilities are to serve as drivers of digital transformation in fast-food restaurants and create a dynamic customer value. This research expands the dynamic capabilities theory by adding digital customer orientation (i.e., proactive and reactive to customer feedback) to develop digital transformation and create customer value in fast-food restaurants. This research provides fast-food restaurant managers with an in-depth explanation of how to implement the digital dynamic capabilities model for executing digital transformation and developing a new dynamic customer value.

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Section: Customer Value and Digital Transformationmentioning

confidence: 99%

Enhancing Digital Presence for Maximizing Customer Value in Fast-Food Restaurants

et al. 2023

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“…Sensory marketing, on the other hand, targets consumers' five senses using sensory appeal (Hamacher & Buchkremer, 2022). It creates contents that use the sense of sound, sight, smell, touch, and taste.…”

Section: Type Of Artificial Intelligence Toolsmentioning

confidence: 99%

“…Self‐service technologies, such as automated teller machines (ATMs) or self‐service desks at airports, allow users to obtain a service without interacting with service employees (Kelly et al, 2019). Sensory marketing, on the other hand, targets consumers' five senses using sensory appeal (Hamacher & Buchkremer, 2022). It creates contents that use the sense of sound, sight, smell, touch, and taste.…”

Section: Literature Reviewmentioning

confidence: 99%

Artificial intelligence in marketing: A meta‐analytic review

Mehta¹,

Jebarajakirthy

Maseeh

et al. 2022

Psychology and Marketing

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This study synthesizes the artificial intelligence literature into a Meta-analytic framework based on the theory of reasoned action and the unified theory of acceptance and use of technology 2, and examines concrete relationships between the constructs of this framework. This meta-review also performed a moderation analysis to investigate the possible reasons for inconsistent findings across studies.The findings suggest that three methodological moderators (sample type, gender dominance, and publication type), and one contextual moderator (level of country's technology advancement) lead to inconsistencies in the relationships between study constructs. Academically, this review synthesizes the artificial intelligence literature and resolves inconsistencies in the literature and also adds constructs to both theory of reasoned action and the unified theory of acceptance and use of technology 2.

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“…Deloitte also put forward in the survey report, “In our January 2020 survey, only 20% of respondents who subscribed to a streaming video service had cut a service in the previous 12 months, but by October, 46% had cut at least one in just the previous six months”. Therefore, optimizing the algorithm of the recommendation system is crucial for the streaming video platforms to win more subscribers and retain them ( Hamacher & Buchkremer, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Movie recommendation model based on probabilistic matrix decomposition using hybrid AdaBoost integration

Zhang

et al. 2023

PeerJ Computer Science

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In recent years, recommendation systems have already played a significant role in major streaming video platforms.The probabilistic matrix factorization (PMF) model has advantages in addressing high-dimension problems and rating data sparsity in the recommendation system. However, in practical application, PMF has poor generalization ability and low prediction accuracy. For this reason, this article proposes the Hybrid AdaBoost Ensemble Method. Firstly, we use the membership function and the cluster center selection in fuzzy clustering to calculate the scoring matrix of the user-items. Secondly, the clustering user items’ scoring matrix is trained by the neural network to improve the scoring prediction accuracy further. Finally, with the stability of the model, the AdaBoost integration method is introduced, and the score matrix is used as the base learner; then, the base learner is trained by different neural networks, and finally, the score prediction is obtained by voting results. In this article, we compare and analyze the performance of the proposed model on the MovieLens and FilmTrust datasets. In comparison with the PMF, FCM-PMF, Bagging-BP-PMF, and AdaBoost-SVM-PMF models, several experiments show that the mean absolute error of the proposed model increases by 1.24% and 0.79% compared with Bagging-BP-PMF model on two different datasets, and the root-mean-square error increases by 2.55% and 1.87% respectively. Finally, we introduce the weights of different neural network training based learners to improve the stability of the model’s score prediction, which also proves the method’s universality.

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Measuring Online Sensory Consumer Experience: Introducing the Online Sensory Marketing Index (OSMI) as a Structural Modeling Approach

Cited by 13 publications

References 89 publications

Enhancing Digital Presence for Maximizing Customer Value in Fast-Food Restaurants

Enhancing Digital Presence for Maximizing Customer Value in Fast-Food Restaurants

Artificial intelligence in marketing: A meta‐analytic review

Movie recommendation model based on probabilistic matrix decomposition using hybrid AdaBoost integration

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