Future Service Technologies: Business models, Analytics, and Experience
Special issue call for papers from Journal of Services Marketing
Deadline: January 30th, 2018
Technology has transformed how individuals, firms and organizations function today. The developments will have far-reaching global impact on performance, competitiveness, and resilience for all industries. The future service technologies at the intersection of people, data and intelligent machines will provide opportunities for growth based on innovative business models, informed decision making, and transformed behaviour. These intelligent connected technologies such as virtual reality applications, artificial intelligence, text mining, augmented reality, and Internet of Things, result in improved predictive capabilities, enhanced and assisted experiences, and real-time actionable insights.
Whether one looks at B2C or B2B service settings, information technology, big data metrics & analytics and new technology-enabled delivery platforms are taking center stage (Huang and Rust, 2013; Rust and Huang, 2014; Hartmann et al., 2016; Kunz et al., 2017). Furthermore, in the context of the introduction and increased usage of service technologies, service marketing problems are also gaining significant research attention in disciplines such as computer science and information systems (e.g. Amin et al., 2017; Coussement et al., 2017; Hou et al., 2017). This highlights the need for transdisciplinary service research (Gustafsson et al., 2016), maintaining a strong service-theoretic focus, whilst pushing the boundaries in terms of the analytics tools researchers develop and deploy to provide solutions for service managers.
This is particularly the case in the context of service analytics (Bijmolt et al., 2010; Wang et al., 2010), an area spanning everything from B2C CRM metrics, to cutting-edge B2B IoT metrics, and the IT infrastructure (hardware and software) supporting the measurement, collection and reporting of such metrics, such as cloud-based software infrastructure (Demirkan and Parts, 2013). Service analytics are now also literally being brought to life, with the advent of artificially intelligent cognitive agents such as IBM Watson (IBM, 2016).
Customer experience (CX) in the contemporary service context, primarily concerns the streamlining, integration and measurement of all customer interactions with a brand or organisation (Klaus, 2014; MSI, 2016). This encompasses new trends ranging from chatbots (Chakrabarti and Luger, 2015), powered by advanced artificial intelligence systems to deliver refined, on-demand customer service, to social media analytics (Fan and Gordon, 2014) for monitoring large-scale customer feedback in real time. Moreover, customers increasingly orchestrate their experiences and everyday activities with the support of technology (Heinonen et al., 2010). This includes for example augmented reality applications in retailing, education, and health care contexts that enable individuals to comprehend their surrounding environment in a completely new way.
These trends in-turn translate into new implications for service managers as well as individual customers. In fact, the sheer speed of technological advancement shaping today’s services necessitates the reimagining of existing service science frameworks, into ultra-adaptive lenses through which academics can help managers diagnose and solve real-world problems and maintain customer-centricity. At the same time and in the same way researchers can help customers (self-)diagnose and solve problems that will be beneficial for their customer everyday lives.
For service researchers, these developments require new research questions to be asked around how to best make use of new service analytics tools to create business value and to measure and enhance customer experience (Ordenes et al., 2014; Keiningham et al., 2017), and for some research questions to be re-asked (e.g. technology readiness, Walczuch et al., 2007; engagement and big data, Kunz et al. 2017, quantifying e-service quality, Carlson and O’cass, 2010).
This special issue aims to foster and initiate a research tradition in the service research field that focus on the rapidly changing technological environment in the service context. This special issue calls for research on future service technologies with specific focus on the metrics and analytics, i.e. type of information emerging from the technologies, and experience and use of these technologies. These aspects are explored from the perspective of customer and managerial decision making. Submissions can be conceptual or empirical in nature. Non-exhaustive list of topics to be covered in this special issue include:
• New CRM metrics
• Social media analytics
• Text mining
• Virtual and Augmented Reality
• Industrial internet and Internet of Things (IoT)
• Machine Learning and Artificial Intelligence (AI)
• Data dash boarding and visualization
• New platforms and data access
• New service business models
• Wearables and sensor metrics for consumer decision making
• Wearables and sensor metrics for managerial organisational decision making
• Real time usage of metrics and data analytics provided by new technologies
All manuscripts submitted must not have been published, accepted for publication, or be currently under consideration elsewhere.
Manuscripts should be submitted in accordance with the author guidelines available on the journal home page at http://www.emeraldgrouppublishing.com/products/journals/author_guidelines.htm?id=jsm
All submissions should be made via the ScholarOne online submission system and should be made to the special issue which is identified on the submission site.
Expected publication: Volume 33 (Issue 1) 2019
30 January 2018 – deadline for submissions
Please direct any further inquiries to the editors, listed below.
Guest Editor Contact Details
Dr. Werner Kunz
Digital Media Lab
University of Massachusetts Boston
Dr. Kristina Heinonen
Centre for Relationship Marketing and Service Management (CERS)
Hanken School of Economics, Helsinki
Dr. Jos Lemmink
School of Business and Economics
Dr. Ben Lucas
Brightlands Business Intelligence and Smart Services Institute
School of Business and Economics at Maastricht University
Amin, A., Anwar, S., Adnan, A., Nawaz, M., Alawfi, K., Hussain, A., & Huang, K. (2017). Customer churn prediction in the telecommunication sector using a rough set approach. Neurocomputing, 237, 242-254.
Bijmolt, T. H., Leeflang, P. S., Block, F., Eisenbeiss, M., Hardie, B. G., Lemmens, A., & Saffert, P. (2010). Analytics for customer engagement. Journal of Service Research, 13(3), 341-356.
Carlson, J., & O'Cass, A. (2010). Exploring the relationships between e-service quality, satisfaction, attitudes and behaviors in content-driven e-service web sites. Journal of Services Marketing , 24 (2), 112-127.
Chakrabarti, C. and Luger, G.F., 2015. Artificial conversations for customer service chatter bots: Architecture, algorithms, and evaluation metrics. Expert Systems with Applications, 42(20), pp.6878-6897.
Coussement, K., Lessmann, S., & Verstraeten, G. (2017). A comparative analysis of data preparation algorithms for customer churn prediction: A case study in the telecommunication industry. Decision Support Systems, 95, 27-36.
Demirkan, H., & Parts, D. (2013). Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in the cloud. Decision Support Systems , 55 (1), 412-421.
Fan, W. and Gordon, M.D., 2014. The power of social media analytics. Communications of the ACM, 57(6), pp.74-81.
Gustafsson, A., Högström, C., Radnor, Z., Friman, M., Heinonen, K., Jaakkola, E., & Mele, C. (2016). Developing service research paving the way to transdisciplinary research. Journal of Service Management , 27 (1), 9-20.
Hartmann, PM, Hartmann, PM, Zaki, M., Zaki, M., Feldmann, N., Feldmann, N., ... & Neely, A. (2016). Capturing value from big data-a taxonomy or data-driven business models used by start-up firms. International Journal of Operations & Production Management , 36 (10), 1382-1406.
Heinonen, K., Strandvik, T., Mickelsson, K-J., Edvardsson, B., Sundström, E., and Andersson, P. (2010): A Customer Dominant Logic of Service, Journal of Service Management, 21 (4) 531-548
Hou, R., Wu, J., & Du, H. S. (2017). Customer social network affects marketing strategy: A simulation analysis based on competitive diffusion model. Physica A: Statistical Mechanics and its Applications, 469, 644-653.
Huang, M. H., & Rust, R. T. (2013). IT-related service: A multidisciplinary perspective. Journal of Service Research, 16(3), 251-258.
IBM (2016). Using Watson to improve customer service. from: https://www.ibm.com/blogs/watson/2016/04/using-watson-improve-customer-…
Keiningham, T., Ball, J., Benoit, S., Bruce, H. L., Buoye, A., Dzenkovska, J., ... & Zaki, M. (2017). The interplay of customer experience and commitment. Journal of Services Marketing, 31(2).
Klaus, P., 2014. Measuring customer experience: How to develop and execute the most profitable customer experience strategies. Springer.
Kunz, W., Aksoy, L. Bart, Y., Heinonen, K., Kabadayı, S., Ordenes, FV, ... & Theodoulidis, B. (2017). Customer engagement in a big data world. Journal of Services Marketing , 31 (2).
Ordenes, F., Theodoulidis, B., Burton, J., Gruber, T and Zaki, M. (2014). Analyzing Customer Experience Feedback Using Text Mining: A Linguistics-Based Approach. Journal of Service Research.
Rust, R. T., & Huang, M. H. (2014). The service revolution and the transformation of marketing science. Marketing Science, 33(2), 206-221.
Walczuch, R., Lemmink, J., & Streukens, S. (2007). The effect of service employees’ technology readiness on technology acceptance. Information & Management, 44(2), 206-215.
Wang, C., Akella, R., & Ramachandran, S. (2010, October). Hierarchical service analytics for improving productivity in an enterprise service center. In Proceedings of the 19th ACM international conference on Information and knowledge management (pp. 1209-1218). ACM.