About this Research Topic

Abstract Submission Deadline 16 November 2022
Manuscript Submission Deadline 16 January 2023

In an ever-changing market, we are confronting a growing demand for using AI-enabled analytics and communication/recommendation/service delivery tools to tackle the emerging challenges, inclusive of but not limited to, e.g., how to use contactless technologies in service delivery in the post-pandemic era, how to adopt humanless interaction in highly customized communication, how to leverage the hidden/to-be revealed preferences from platforms of all kinds to apply sensible recommendations.

Notably, behind the emerging practices remain gaps in theorization and methodologies, which call for diversified research to break the disciplinary silos and integrate diversified domain knowledge and methods. From one side, social science researchers are using AI to understand human behaviors yet need more effort to renew the existing framework to better address digitalized customer experiences featured by all high-techs. For example, In the 1990s, service management scholars developed the service quality framework, which contains five dimensions- tangibility, reliability, responsiveness, assurance, and empathy. However, the classic framework and related wisdom have not yet boomed in the tide of machine-learning optimization. On the other side, methodological evolution has been super active but failed to address the updated demands to address the research depth in CS and AI-field vs. Width of practices from different angles of a spectrum. For example, in the conventional CS area, AI is featured in its miracle of computing efficiency (outcome-based) to replace human roles in customer inquiries and recommendations vs. the effectiveness of customer interaction (process-based) which needs inputs in verbal and nonverbal intelligence.

We are therefore inspired by the silo-breaking and domain-merging responsibility and would like to cast a light on a new frontier in AI research – we are aiming to call for works to revolutionalize the framework of AI-enabled technologies with other disciplines, for example, neuroscience science, psychology and behavior, business and service management, and other humanity and social sciences. (Briefly echo the framework of service marketing since the 1990s for the service delivery and service quality- tangibility, reliability, responsiveness, assurance, and empathy).

We’re interested in receiving submissions featured by one of the approaches or applications below, although any submission related to the contribution of AI to digital behavior and the service economy will be welcomed:
1. Machine learning of customers’ demonstrated preferences of heterogeneous nature or using multi-level, cross-platform data.
2. Cross-disciplinary approaches combined with neuro-network and methods/perspectives featured by social sciences such as psychological studies.
3. Machine-learning using multi-modal data.
4. NLP for sentiment analysis.
5. Enhancing the customer’s perceived empathy of service chatbot via NLP -sentiment analysis via using verbal or audio information.
6. Humanoid robots in customer delivery for customer intimacy and trust.
7. Mechanism of adopting multi-modal techniques in capturing customer emotion.
8. Integration of customers’ revealed prior preferences (i.e., Bayesian preferences) from other platforms or data pipelines to enhance the accuracy of product recommendations.

Keywords: AI-enabled analytics, Digital Behavior, Service Economy, Machine learning, Multi-modal data, NLP, Data analytics, Bayesian preferences


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

In an ever-changing market, we are confronting a growing demand for using AI-enabled analytics and communication/recommendation/service delivery tools to tackle the emerging challenges, inclusive of but not limited to, e.g., how to use contactless technologies in service delivery in the post-pandemic era, how to adopt humanless interaction in highly customized communication, how to leverage the hidden/to-be revealed preferences from platforms of all kinds to apply sensible recommendations.

Notably, behind the emerging practices remain gaps in theorization and methodologies, which call for diversified research to break the disciplinary silos and integrate diversified domain knowledge and methods. From one side, social science researchers are using AI to understand human behaviors yet need more effort to renew the existing framework to better address digitalized customer experiences featured by all high-techs. For example, In the 1990s, service management scholars developed the service quality framework, which contains five dimensions- tangibility, reliability, responsiveness, assurance, and empathy. However, the classic framework and related wisdom have not yet boomed in the tide of machine-learning optimization. On the other side, methodological evolution has been super active but failed to address the updated demands to address the research depth in CS and AI-field vs. Width of practices from different angles of a spectrum. For example, in the conventional CS area, AI is featured in its miracle of computing efficiency (outcome-based) to replace human roles in customer inquiries and recommendations vs. the effectiveness of customer interaction (process-based) which needs inputs in verbal and nonverbal intelligence.

We are therefore inspired by the silo-breaking and domain-merging responsibility and would like to cast a light on a new frontier in AI research – we are aiming to call for works to revolutionalize the framework of AI-enabled technologies with other disciplines, for example, neuroscience science, psychology and behavior, business and service management, and other humanity and social sciences. (Briefly echo the framework of service marketing since the 1990s for the service delivery and service quality- tangibility, reliability, responsiveness, assurance, and empathy).

We’re interested in receiving submissions featured by one of the approaches or applications below, although any submission related to the contribution of AI to digital behavior and the service economy will be welcomed:
1. Machine learning of customers’ demonstrated preferences of heterogeneous nature or using multi-level, cross-platform data.
2. Cross-disciplinary approaches combined with neuro-network and methods/perspectives featured by social sciences such as psychological studies.
3. Machine-learning using multi-modal data.
4. NLP for sentiment analysis.
5. Enhancing the customer’s perceived empathy of service chatbot via NLP -sentiment analysis via using verbal or audio information.
6. Humanoid robots in customer delivery for customer intimacy and trust.
7. Mechanism of adopting multi-modal techniques in capturing customer emotion.
8. Integration of customers’ revealed prior preferences (i.e., Bayesian preferences) from other platforms or data pipelines to enhance the accuracy of product recommendations.

Keywords: AI-enabled analytics, Digital Behavior, Service Economy, Machine learning, Multi-modal data, NLP, Data analytics, Bayesian preferences


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Topic Editors

Loading..

Topic Coordinators

Loading..

articles

Sort by:

Loading..

authors

Loading..

views

total views article views article downloads topic views

}
 
Top countries
Top referring sites
Loading..

Share on

About Frontiers Research Topics

With their unique mixes of varied contributions from Original Research to Review Articles, Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author.