Value Co-Creation in Smart Services: A Functional Affordances Perspective on Smart Personal Assistants

Knote, Robin; Janson, Andreas; Söllner, Matthias; Leimeister, Jan Marco

doi:10.2139/ssrn.3923706

Cited by 20 publications

(10 citation statements)

References 145 publications

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“…Perceptions of VA intelligence reflect whether people believe the VA despite being non-human -possesses the ability to understand input and deliver suitable output (McLean et al, 2021), which likely impacts their evaluations. This point is also consistent with work that indicates that 'smart characteristics' of VAs may be linked to affordances, in turn impacting users' evaluations (Knote et al, 2020). Both technology research (e.g.…”

Section: Source Effectssupporting

confidence: 84%

How communications by AI-enabled voice assistants impact the customer journey

Grewal

Guha

Schweiger

et al. 2022

JOSM

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PurposeArtificial intelligence–enabled voice assistants (VAs), such as Amazon's Alexa, Google Assistant, and Apple's Siri, are available in smartphones, smart speakers, and other digital devices and channels. Use of these VAs is growing rapidly and are expected to significantly impact purchase intentions. This article focuses on how the communications enabled and provided by these VAs influence VA evaluations and usage intentions, contingent on the stage of the customer journey.Design/methodology/approachThis paper builds from work on VAs, work on artificial intelligence (AI) and work on communications, to offer a comprehensive and up-to-date understanding of how VA evaluations and usage intentions may be impacted by the communications from VAs, contingent on the stage of the customer journey.FindingsThis paper proposes a model for VA enabled communications impact VA evaluations. It builds from work on VAs, AI, communications, and customer journey management. In the proposed model, VA evaluations are not only impacted by source, message and recipient characteristics (per prior communication models), but also by (1) VA/AI specific features, like perceptions of humanness and perceptions of artificiality, and (2) stage of the customer journey.Practical implicationsThis paper provides guidance to firms, as regards how VA communications may influence VA evaluations and usage intentions. As an initial conjecture, (1) increasing perceptions of humanness, (2) decreasing perceptions of artificiality (3) a better fit between communications style (e.g. abstract vs concrete), and request type (e.g. transactional vs informational) (4) a better fit between VA communications (e.g. information vs banter), and consumer perceptions of the VA (servant vs partner) and (5) a better fit between VA communications and the stage of the customer journey may positively influence VA evaluations and VA usage intentions.Originality/valueThis paper provides a fresh look at the impact of VA communications, clarifying how such communications impact VA evaluations and usage intentions at various stages of the customer journey.

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Section: Source Effectssupporting

confidence: 84%

How communications by AI-enabled voice assistants impact the customer journey

Grewal

Guha

Schweiger

et al. 2022

JOSM

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“…The co-creation process is influenced by the consumer and service provider paradigms and the involved tools and components of engaging in it (Knote et al, 2021). In addition, the evolution of technological media, including broadband, digital TV, and fifth-generation mobile services, create a scope where consumers and stakeholders can engage without the physical presence (Payne et al, 2021).…”

Section: Criteria Affecting the Co-creation Procedures In Professiona...mentioning

confidence: 99%

Co-Creation Framework in Professional Service Context: Toward a Praxis Model

Ahmed

Nabi

Misbauddin³

et al. 2022

Khulna Univ. Stud.

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Co-creation in service is essential through facilitating the coordination between customers and service providers. A holistic understanding of value co-creation in professional services can ensure the improvement of service experience. The objective of this paper is to form a praxis model of value co-creation in professional services. The authors performed a Systematic Review of Literature (SRL) by analyzing 113 research documents and articles on co-creation literature within the year range 1996-2021. The study assimilated the key considerations of co-creation in the professional service context. These led to the formation of a conceptual framework. This study identifies the roles of the actors in value co-creation and outlines the procedures for involving the customers. The paper also provides the inhibitors and enablers of co-creation. The study makes contribution to the existing literature by delineating the criteria and factors for co-creation in the professional service context. The praxis model of co-creation developed here could also be beneficial for the practitioners in improving service design.

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“…Some examples of SPAs are Siri (Apple), Alexa (Amazon), and Assistant (Google). These technologies are highly intelligent and interactive, which can be useful for learning [10]. In addition, SPA technology will be powered by more than 870 million devices by 2022 [11].…”

Section: Introductionmentioning

confidence: 99%

Educational Data Mining to Support Programming Learning Using Problem-Solving Data

et al. 2022

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Computer programming has attracted a lot of attention in the development of information and communication technologies in the real world. Meeting the growing demand for highly skilled programmers in the ICT industry is one of the major challenges. In this point, online judge (OJ) systems enhance programming learning and practice opportunities in addition to classroom-based learning. Consequently, OJ systems have created a large number of problem-solving data (solution codes, logs, and scores) archives that can be valuable raw materials for programming education research. In this paper, we propose an educational data mining framework to support programming learning using unsupervised algorithms. The framework includes the following sequence of steps: (i) problem-solving data collection (logs and scores are collected from the OJ) and preprocessing; (ii) MK-means clustering algorithm is used for data clustering in Euclidean space; (iii) statistical features are extracted from each cluster; (iv) frequent pattern (FP)-growth algorithm is applied to each cluster to mine data patterns and association rules; (v) a set of suggestions are provided on the basis of the extracted features, data patterns, and rules. Different parameters are adjusted to achieve the best results for clustering and association rule mining algorithms. For the experiment, approximately 70,000 realworld problem-solving data from 537 students of a programming course (Algorithm and Data Structures) were used. In addition, synthetic data have leveraged for experiments to demonstrate the performance of MK-means algorithm. The experimental results show that the proposed framework effectively extracts useful features, patterns, and rules from problem-solving data. Moreover, these extracted features, patterns, and rules highlight the weaknesses and the scope of possible improvements in programming learning.

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Value Co-Creation in Smart Services: A Functional Affordances Perspective on Smart Personal Assistants

Cited by 20 publications

References 145 publications

How communications by AI-enabled voice assistants impact the customer journey

How communications by AI-enabled voice assistants impact the customer journey

Co-Creation Framework in Professional Service Context: Toward a Praxis Model

Educational Data Mining to Support Programming Learning Using Problem-Solving Data

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