Fabian Hunke, Gerhard Satzger and Tuure Tuunanen
This research investigates the systematic reuse of service concept elements within modular service design, aiming to offer actionable insights into effective conceptualization of…
Abstract
Purpose
This research investigates the systematic reuse of service concept elements within modular service design, aiming to offer actionable insights into effective conceptualization of services and extending methodological underpinnings to enhance the approach of service design.
Design/methodology/approach
Employing a design science research approach, this study investigates the intentional and targeted reuse of service concept elements for modular service design. It develops four general design principles and applies them in a real-world context to demonstrate and evaluate the purposeful integration of service concept elements.
Findings
This research reveals the efficacy of reusing service concept elements for modular service design, highlighting the benefits of this approach in conceptualizing new services. It theorizes generalizable design knowledge by formalizing four design principles that allow to underpin the reuse of service concept elements.
Originality/value
This research contributes to service design literature by providing actionable insights into the systematic reuse of service concept elements, particularly within the framework of modular service design. We develop and test general design principles and, specifically, apply them for analytics-based digital services.
Details
Keywords
Lucas Baier, Niklas Kühl, Ronny Schüritz and Gerhard Satzger
While the understanding of customer satisfaction is a key success factor for service enterprises, existing elicitation approaches suffer from several drawbacks such as high manual…
Abstract
Purpose
While the understanding of customer satisfaction is a key success factor for service enterprises, existing elicitation approaches suffer from several drawbacks such as high manual effort or delayed availability. However, the rise of analytical methods allows for the automatic and instant analysis of encounter data captured during service delivery in order to identify unsatisfied customers.
Design/methodology/approach
Based on encounter data of 1,584 IT incidents in a real-world service use case, supervised machine learning models to predict unsatisfied customers are trained and evaluated.
Findings
We show that the identification of unsatisfied customers from encounter data is well feasible: via a logistic regression approach, we predict dissatisfied customers already with decent accuracy—a substantial improvement to the current situation of “flying blind”. In addition, we are able to quantify the impacts of key service elements on customer satisfaction.
Research limitations/implications
The possibility to understand the relationship between encounter data and customer satisfaction will offer ample opportunities to evaluate and expand existing service management theories.
Practical implications
Identifying dissatisfied customers from encounter data adds a valuable methodology to customer service management. Detecting unsatisfied customers already during the service encounter enables service providers to immediately address service failures, start recovery actions early and, thus, reduce customer attrition. In addition, providers will gain a deeper understanding of the relevant drivers of customer satisfaction informing future new service development.
Originality/value
This article proposes an extendable data-based approach to predict customer satisfaction in an automated, timely and cost-effective way. With increasing data availability, such AI-based approaches will spread quickly and unlock potential to gain important insights for service management.