Rise of the Machines?
Customer Engagement through Automated Service Interactions, Special issue of Journal of Service Research; Deadline 15 Nov 2018
Journal of Service Research
Special Section Call for Papers:
“Rise of the Machines?: Customer Engagement through Automated Service Interactions”
Linda D. Hollebeek, PhD, Associate Professor, Montpellier Business School/NHH Norwegian School of Economics
Tor W. Andreassen, PhD, Professor/Director, Center for Service Innovation, NHH Norwegian School of Economics
David E. Sprott, PhD, Carson College of Business Senior Associate Dean/Boeing Scott and Linda Carson Chair Professor of Marketing, Washington State University
The service sector has seen significant developments in recent decades, including the increasing adoption of automated (computerized) forms of interactivity in service delivery, customer relationship management, and back-end processing activities (e.g. automated booking systems; Ostrom et al. 2015; Bitner et al. 2000). Each of these trends reflects the adoption of particular emerging technologies, including service robotics or intelligent assistants (Teixeira et al. 2017; Van Doorn et al. 2017), which are predicted to be increasingly adopted in service-based customer-firm interactions (Kumar et al. 2016). While automization has traditionally implied a degree of standardization of service processes and/or offerings (Kurzweil 2005), automated service interactions in today’s world offer a growing opportunity for service personalization, while simultaneously capitalizing on the benefits of service automation (e.g. enhanced service efficiency, reduced variability; Glas et al. 2017; Rabbitt et al. 2015). Not surprisingly, various commentators acclaim the vast future potential for engaging customers through automated service interactions (e.g. Foster et al. 2017; Hollebeek et al. 2017). For example, IBM (2017) predicts that by 2020, 85% of customer-firm interactions will be conducted via computerized technologies, without human involvement. Customer engagement enabled through automated customer-to-machine interactions is thus forecast to increase, and may even replace specific forms of traditional customer-to-employee service interactions altogether (Shi et al. 2015; Singh et al. 2017).
Various examples exist in today’s marketplace that illustrate firms’ increasing use of automated service interactions. For example, the Henn-na Hotel in Nagasaki, Japan is the world’s first hotel to be entirely staffed by humanoid robots, which take the physical likeness of humans (and in some cases, animals) in service delivery (Scassellati 2002). Customers’ engagement with the hotel’s frontline service delivery staff, including receptionists and waiters, room service personnel and gym operators occur via automated interactions deploying service robots (Singh et al. 2017; Murison 2016). Further illustrations include hotels using robots to deliver room service (e.g. Aloft Cupertino, Residence Inn, Holiday Inn), Royal Caribbean’s Quantum of the Seas’ Bionic Bar with robot bartenders, KLM’s Spencer that offers customer service at Schiphol Airport (Cabibihan et al. 2014; Murison 2016), McDonald’s automated kiosks, and IBM’s Watson or Apple’s Viv (acquired by Samsung; Kharpal 2016). In each of these examples, automated interactions can reliably assist customers with low-level service tasks. A key benefit of automated interactions is freeing up employees’ valuable time that can be applied to more core, or complex, service activities, thus generating cost savings (Kumar et al. 2016; Wood 2016). Similarly, non-humanoid technologies including self-driving cars, chatbots, and electronically enabled passage (e.g. at international airports) are increasingly used to facilitate service delivery (Blut et al. 2016; Narla 2013). Thus, a clear trend is emerging whereby firms adopt automated interactions that facilitate the development of customers’ engagement with their offerings. Initial academic research suggests that firms adopting such technology will be able to achieve more personalized, effective and/or efficient service execution (Erden and Jonkman 2012), thereby contributing to firm-based value creation (Van Doorn et al. 2017; Rifkin 2014). However, this work is in its infancy and further scholarly attention is required.
Despite its acclaimed benefits, however, sceptics question the long-term effects of increasingly (or fully) automated customer-firm interactions on ensuing actor (e.g. customer/employee) outcomes, including engagement, satisfaction, social capital, and wellbeing (Anderson and Ostrom 2015; Brodie et al. 2011). For example, automated interactions may spawn limited customer preference or acceptance (Castro-González et al. 2016), lack a personal touch (e.g. through limited frontline service assistant communication, empathy or ability to detect or respond appropriately to customer sarcasm or different linguistic dialects; Giebelhausen et al. 2014), or incur an increasing reliance on electrical power supply. Automated interactions are also more susceptible to being hacked, causing potential security or privacy issues (Van Doorn et al. 2017), and may raise job security concerns (e.g. lower-skilled service workers’ jobs that are being increasingly replaced by automated forms of service provision; Frey and Osborne 2017). Therefore, whether automated service interactions’ net effect on customers’ and other actors’ wellbeing will be positive (e.g. consumers’ enhanced problem-solving skills through interacting with intelligent agents), negligible, or negative (e.g. actors’ loss of self-esteem/value through one’s intellectual capabilities becoming increasingly under-utilized, or obsolete, in automated service interactions) remains an open question. These are important micro- and macro-level issues that remain unexplored and undocumented in the literature, which need to be addressed to fully leverage the opportunities of automated service interactions to firms and customers.
In response to this identified research gap, this Special Section seeks to explore ways in which automated service interactions engage customers and create customer and firm value. We invite submissions that investigate key drivers, dynamics, outcomes, and challenges associated with engaging customers through automated service interactions. We welcome conceptual, methodological and empirical contributions from researchers deploying diverse methods and grounded in various service research traditions. Papers considered for the Special Section may focus on topics including, but not limited to, the following:
- How can firms successfully automate specific parts of their service interactions to augment their employees’ capabilities, and enhance overall service productivity?
- Which types of service companies (e.g. small/large, B2C/B2B) are most likely to benefit from adopting automated service interactions, and what are the respective success factors and challenges characterizing these interactions?
- How do consumers respond to automated interactions with particular service firms?
- Which are the key customer-based (e.g. personality), service-related (e.g. resource availability), particular interaction technology (e.g. service robot)-related, and environmental (e.g. market-based) factors that optimize customer/firm value ensuing from automated interactions?
- What are the key pros (e.g. fewer mistakes) and cons (e.g. impersonal nature) of technology-enabled customer engagement? Do these vary by context and service setting?
- Under what firm/market-based conditions are the intended benefits of automated service interactions best achieved?
- How can technology-enabled customer engagement be leveraged to drive customer purchases and loyalty throughout the customer journey, and how does it fit within the firm’s relationship marketing program?
- What ratio of customer-to-employee/customer-to-machine interactions optimizes customer engagement, purchases and loyalty for specific service offerings?
- Are automated service interactions more effective for retaining existing, or attracting new, customers?
- In which particular service tasks/activities do automated service interactions create optimal value?
- What are the best design elements to include in automated service interactions to ensure favorable customer responses to the new technology?
- What is the return-on-investment (ROI) of particular automated service interactions? What factors influence such ROI?
- How can various emerging technologies, including the Internet of Things, smart devices, or wearables, be integrated with automated service interactions to create optimal value?
- Do service firms’ traditional strategies or tactics require rethinking to thrive in environments characterized by increasingly automated service interactions?
- How does machine learning affect technology-enabled customer engagement?
All manuscripts must strictly follow the guidelines of the Journal of Service Research, which are available at:
The closing date for submissions is November 15, 2018 for expected publication in late 2019. Manuscripts must be submitted through the Journal of Service Research website:
When submitting your manuscript, please ensure to select the correct Special Section from the drop-down menu on the Manuscript Submission page.
Queries can be directed at the Special Section Guest Editors:
Linda D. Hollebeek, PhD
Montpellier Business School/Norwegian School of Economics
Tor W. Andreassen, PhD
Professor/Director, Center for Service Innovation
NHH Norwegian School of Economics
David W. Sprott, PhD
Carson College of Business Senior Associate Dean/WSU Boeing Scott and Linda Carson Professor of Marketing
Washington State University
Anderson, Laurel and Amy L. Ostrom (2015), “Transformative Service Research: Advancing Our Knowledge about Service and Well-Being,” Journal of Service Research, 18(3), 243-249.
Bitner, Mary Jo, Stephen W. Brown and M. Meuter (2000), “Technology Infusion in Service Encounters,” Journal of the Academy of Marketing Science, 28(1), 138-149.
Blut, Markus, Cheng Wang,and Klaus Schoefer (2016), “Factors Influencing the Acceptance of Self-Service Technologies: A Meta-Analysis,” Journal of Service Research, 19(4), 396-416.
Brodie, Roderick, Linda Hollebeek, Biljana Juric and Ana Ilic (2011), “Customer Engagement: Conceptual Domain, Fundamental Propositions, and Implications for Research,” Journal of Service Research, 14(3), 252-272.
Cabibihan, J., M. Williams and R. Simmons (2014), “When Robots Engage Humans,” International Journal of Social Robotics, 3(6), 311-313.
Castro-González, A., H. Admoni, and B. Scassellati (2016), “Effects of Form and Motion on Judgments of Social Robots Animacy, Likeability, Trustworthiness and Unpleasantness,” International Journal of Human-Computer Studies, 90, 27-38.
Erden, Mustafa S. and Jochem A. Jonkman (2012), “Physical Human-Robot Interaction by Observing Actuator Currents,” International Journal of Robotics & Automation, 27(3), 1.
Foster, Mary Ellen, Andre Gaschler and Manuel Giulian (2017), “Automatically Classifying User Engagement for Dynamic Multi-Party Human-Robot Interaction,” International Journal of Social Robotics, Online First, 1-16.
Frey, Carl B. and Michael A. Osborne (2017), “The Future of Employment: How Susceptible Are Jobs to Computerisation?,” Technological Forecasting and Social Change, 114(Jan), 254-280.
Giebelhausen, Michael D., Stacey G. Robinson, Nancy J. Sirianni and Michael K. Brady (2014), “Touch versus Tech: When Technology Functions as a Barrier or a Benefit to Service Encounters,” Cornell University School of Hotel Administration, Available at: http://scholarship.sha.cornell.edu/cgi/viewcontent.cgi?article=1651&context=articles.
Glas, Dylan F., Kanae Wada, Masahiro Shiomi, Takayuki Kanda, Horoshi Ishiguro and Norihiro Hagita (2017), “Personal Greetings: Personalizing Robot Utterances Based on Novelty of Observed Behavior,” Journal of Service Research, 9(2), 181-198.
Hollebeek, Linda, Rajendra K. Srivastava and Tom Chen (2017), “S-D Logic-Informed Customer Engagement: Integrative Framework, Revised Fundamental Propositions, and Application to CRM,” Journal of the Academy of Marketing Science, DOI: 10.1007/s11747-016-0494-5.
IBM (2017), “10 Reasons Why AI-Powered, Automated Customer Service Is the Future,” April 25, Available at: https://www.ibm.com/blogs/watson/2017/04/10-reasons-ai-powered-automated-customer-service-future/.
Kharpal, A. (2016), “Samsung Buys the AI Assistant Made by the Creators of Apple’s Siri,” Oct 6, Available at: http://www.cnbc.com/2016/10/06/samsung-buys-viv-the-ai-assistant-made-by-the-creators-of-apples-siri.html.
Kumar, V., Ashutosh Dixit, Rajshekar Javalgi, and Mayukh Dass (2016), “Research Framework, Strategies, and Applications of Intelligent Agent Technologies (IATs) in Marketing,” Journal of the Academy of Marketing Science, 44(1), 24-45.
Kurzweil, R. (2005), The Singularity Is Near: When Humans Transcend Biology, New York, NY: Viking.
Narla, Siva R.K. (2013), “The Evolution of Connected Vehicle Technology: From Smart Devicesto Smart Cars to… Self-Driving Cars,” ITE (Institute of Transportation Engineers) Journal, 83(7), 22-26.
Murison, Mahal (2016), “Are Robots the Future of the Travel Industry?,” March 19, Available at: https://www.travelshift.com/robots-travel-industry-future/.
Ostrom, Amy L., A. Parasuraman, David E. Bowen, Lia Patrício and Christopher A. Voss (2015), “Service Research Priorities in a Rapidly Changing Context,” Journal of Service Research, 18(2), 127-159.
Rabbitt, Sarah, Alan Kazdin and Brian Scassellati (2015), “Integrating Socially Assistive Robotics into Mental Healthcare Interventions: Applications and Recommendations for Expanded Use,” Clinical Psychology Review, 35, 35-46.
Rifkin, Jeremy (2014), The Zero Marginal Cost Society: The Internet of Things, The Collaborative Commons, and the Eclipse of Capitalism, New York, NY: Palgrave Macmillan Trade.
Scassellati, Brian (2002), “Theory of Mind for a Humanoid Robot,” Autonomous Robots, 12, 13-24.
Singh, Jagdip, Michael Brady, Todd Arnold, Tom Brown, Detelina Marinova, Ko de Ruyter, Ming-Hui Huang, Matthew L. Meuter, andGoutarn Challagalla (2017), “Getting Smart: Learning from Technology-Empowered Frontline Interactions,” Journal of Service Research, 20(1), 29-42.
Shi, Chao, Masahiro Kanda, Takayuki Ishiguro,andNorihiro Hagita (2015), “Measuring Communication Participation to Initiate Conversation in Human-Robot Interaction,” International Journal of Social Robotics, 7(5), 889-910.
Teixeira, Jorge G., Lia Patricio, Ko-Hsun Huang, Raymond P. Fisk, Leonel Nobrega, and Larry Constantine (2017), “The MINDS Method: Integrating Management and Interaction Design Perspectives for Service Design,” Journal of Service Research, 20(3), 240-258.
Van Doorn, Jenny, Martin Mende, S. Noble, John Hulland, Amy Ostrom, Dhruv Grewal and A. Petersen, (2017), “Domo Arigato Mr. Roboto: Emergence of Automated Social Presence in Organizational Frontlines and Customers’ Service Experiences,” Journal of Service Research, 20(1) 43-58
Wood, Lamont (2016), “Service Robots: The Next Big Productivity Platform,” PricewaterhouseCoopers. Available at: http://usblogs.pwc.com/emerging-technology/service-robots-the-next-big-productivity-platform/.