The Evolution of Marketing in the Context of Voice Commerce: A Managerial Perspective

Mari, Alex; Mandelli, Andreina; Algesheimer, René

doi:10.1007/978-3-030-50341-3_32

Cited by 29 publications

(40 citation statements)

References 53 publications

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“…That is due to their ability to collect data systematically and silently over time [5]. As such, VAs can be conceptualized as interaction decision aid tools that promise fast, repeatable and lowcost decision-making combined with an increased level of accuracy, achieved through network effects and feedback loops [8,20,33].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…During product search on Alexa, the interaction flow with VAs changes whether the user wants to purchase in a product category for the first time, or repurchase a product in the same category [33]. In the first case, the dynamic dialog begins with an active decision by the user who needs to determine whether to search for a brand name (exact match), e.g.…”

Section: Product Choice During Voice Shoppingmentioning

confidence: 99%

“…Purchasing data was collected using a third-party Alexa application 'Swiss Shopping', developed by the authors following systematic machine behavior observations [48,33]. This Alexa 'skill' replicates the native voice shopping process in terms of flow, structure, and tone of the interaction, giving the user the feeling of dealing directly with Alexa throughout the entire task.…”

Section: Experimental Designmentioning

confidence: 99%

“…Voice assistants register the fastest development rate for a new technological medium, even above smartphones and tablets [8]. The term 'voice assistant' refers to conversational agents having the ability to self-improve their understanding of the interlocutor and context [33]. Using a combination of AI techniques, such as automatic speech recognition and natural language understanding, VAs can naturally converse with users, contextually elaborate requests, and dynamically expand their knowledge while learning from mistakes.…”

Section: Introductionmentioning

confidence: 99%

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The Role of Trusting Beliefs in Voice Assistants during Voice Shopping

Mari¹,

Algesheimer²

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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Artificial intelligence-based voice assistants (VAs) such as Amazon Alexa deliver personalized product recommendations in order to match consumers' needs. The use of voice assistants for shopping purposes incorporates elements of risk affecting when and how they are considered trusted relationship partners. In this uncertain environment, it is unclear 'when' voice assistants are capable of gaining trust and 'how' the development of such a trusted relationship affects decisions. This research explores the effect of trusting beliefs towards voice assistants on decision satisfaction through the indirect effect of consideration set size (n. of options), in the context of voice shopping. Findings of an individual-session online experiment (N = 180) show a positive direct effect of trust on customer's satisfaction and a mediating role of set size, confirming consumers' bias towards default choices. This study highlights the consequences of trust in AI-enabled voice assistants for decision-making during utilitarian purchases.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Product Choice During Voice Shoppingmentioning

confidence: 99%

Section: Experimental Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Role of Trusting Beliefs in Voice Assistants during Voice Shopping

Mari¹,

Algesheimer²

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

Self Cite

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“…Recently, there has been a notable advancement in the field of voice assistants 1 . Consequently, voice assistants are being deployed heavily in lucrative domains such as e-commerce (Mari et al, 2020), customer service (Cui et al, 2017) and healthcare (Mavropoulos et al, 2019). There are various ecommerce based voice assistants available in the market, including Amazon's Alexa voice shopping (Maarek, 2018) and Walmart's on Google assistant/Siri 2 .…”

Section: Introductionmentioning

confidence: 99%

Improving Intent Classification in an E-commerce Voice Assistant by Using Inter-Utterance Context

Sharma¹

2020

Proceedings of the 3rd Workshop on E-Commerce and NLP

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In this work, we improve the intent classification in an English based e-commerce voice assistant by using inter-utterance context. For increased user adaptation and hence being more profitable, an e-commerce voice assistant is desired to understand the context of a conversation and not have the users repeat it in every utterance. For example, let a user's first utterance be 'find apples'. Then, the user may say 'i want organic only' to filter out the results generated by an assistant with respect to the first query. So, it is important for the assistant to take into account the context from the user's first utterance to understand her intention in the second one. In this paper, we present our approach for contextual intent classification in Walmart's e-commerce voice assistant. It uses the intent of the previous user utterance to predict the intent of her current utterance. With the help of experiments performed on real user queries we show that our approach improves the intent classification in the assistant.

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Consumer engagement with AI‐powered voice assistants: A behavioral reasoning perspective

Açikgöz

Perez‐Vega

Okumuş

et al. 2023

Psychology and Marketing

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This study draws upon Behavioral Reasoning Theory and the Technology Acceptance Model to investigate consumer engagement with AI‐powered voice assistants. The study creates a theoretical model to examine the effects of reasons for and reasons against using voice assistants. This research exemplifies attitudes towards using voice assistants and willingness to provide personal information as key constructs. The current study tests data from 491 voice assistant users via mTurk, and we utilize a multimethod analysis scheme including the partial least squares technique and the fuzzy set qualitative comparative analysis approach to provide an assessment of the proposed model. Findings indicated that while privacy cynicism has a negative impact upon the attitude towards using voice assistants, the countervailing values of trust, perceived usefulness, and ease of use have off‐setting positive impact. The study also highlights the moderating role of habit on the behavioral mechanisms driving consumer engagement via willingness to provide privacy information. This research advances the emerging literature on voice assistants with respect to privacy‐related factors driving consumer engagement.

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The Evolution of Marketing in the Context of Voice Commerce: A Managerial Perspective

Cited by 29 publications

References 53 publications

The Role of Trusting Beliefs in Voice Assistants during Voice Shopping

The Role of Trusting Beliefs in Voice Assistants during Voice Shopping

Improving Intent Classification in an E-commerce Voice Assistant by Using Inter-Utterance Context

Consumer engagement with AI‐powered voice assistants: A behavioral reasoning perspective

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