Tiina Kanninen, Esko Penttinen, Markku Tinnilä and Kari Kaario
The purpose of this paper is to examine what kinds of capabilities are required by process industry companies as they move toward servitization. The authors proceed in two steps…
Abstract
Purpose
The purpose of this paper is to examine what kinds of capabilities are required by process industry companies as they move toward servitization. The authors proceed in two steps. First, the authors explore the capabilities needed in servitization with a qualitative multiple case study. Second, the authors link the identified capabilities to the servitization steps that were derived from prior literature.
Design/methodology/approach
Based on earlier servitization literature, the authors build a five-step servitization model for industrial companies. Then, drawing on the empirical study consisting of three focus group sessions with three case companies and 20 interviews in 14 case companies, the authors identify 14 servitization capabilities and link them to the servitization steps.
Findings
The study reveals how dynamic capabilities are required in servitization. In contrast to operational capabilities, which are geared toward enabling firms to make a living in the present, dynamic capabilities extend or modify operational capabilities in response to market changes. Based on the empirical study, the authors were able to identify dynamic capabilities for all five steps of servitization: identification of current services and customer needs, determination of a service strategy, creation of new business models and pricing logics, improvements in capabilities, and, ultimately, management services as a separate function.
Research limitations/implications
The current study is exploratory in nature and the number of empirical observations is limited to 14 industrial companies operating in the process industry.
Practical implications
Most importantly, in servitization, companies need dynamic capabilities to transform their operating capabilities in sales and marketing as well as in quantifying and communicating the value created for customers.
Originality/value
The study is the first one to make a link between the capabilities needed and the various stages of servitization and also the first to study the specific context of process industry companies.
Details
Keywords
Tomasz Mucha, Sijia Ma and Kaveh Abhari
Recent advancements in Artificial Intelligence (AI) and, at its core, Machine Learning (ML) offer opportunities for organizations to develop new or enhance existing capabilities…
Abstract
Purpose
Recent advancements in Artificial Intelligence (AI) and, at its core, Machine Learning (ML) offer opportunities for organizations to develop new or enhance existing capabilities. Despite the endless possibilities, organizations face operational challenges in harvesting the value of ML-based capabilities (MLbC), and current research has yet to explicate these challenges and theorize their remedies. To bridge the gap, this study explored the current practices to propose a systematic way of orchestrating MLbC development, which is an extension of ongoing digitalization of organizations.
Design/methodology/approach
Data were collected from Finland's Artificial Intelligence Accelerator (FAIA) and complemented by follow-up interviews with experts outside FAIA in Europe, China and the United States over four years. Data were analyzed through open coding, thematic analysis and cross-comparison to develop a comprehensive understanding of the MLbC development process.
Findings
The analysis identified the main components of MLbC development, its three phases (development, release and operation) and two major MLbC development challenges: Temporal Complexity and Context Sensitivity. The study then introduced Fostering Temporal Congruence and Cultivating Organizational Meta-learning as strategic practices addressing these challenges.
Originality/value
This study offers a better theoretical explanation for the MLbC development process beyond MLOps (Machine Learning Operations) and its hindrances. It also proposes a practical way to align ML-based applications with business needs while accounting for their structural limitations. Beyond the MLbC context, this study offers a strategic framework that can be adapted for different cases of digital transformation that include automation and augmentation of work.