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The proliferation of (big) data provides numerous opportunities for service advances in practice, yet research on using data to advance service is at a nascent stage in the literature. Many studies have discussed phenomenological benefits of data to service. However, limited research describes managerial issues behind such benefits, although a holistic understanding of the issues is essential in using data to advance service in practice and provides a basis for future research. The purpose of this paper is to address this research gap.

“Using data to advance service” is about change in organizations. Thus, this study uses action research methods of creating real change in organizations together with practitioners, thereby adding to scientific knowledge about practice. The authors participated in five service design projects with industry and government that used different data sets to design new services.

Drawing on lessons learned from the five projects, this study empirically identifies 11 managerial issues that should be considered in data-use for advancing service. In addition, by integrating the issues and relevant literature, this study offers theoretical implications for future research.

“Using data to advance service” is a research topic that emerged originally from practice. Action research or case studies on this topic are valuable in understanding practice and in identifying research priorities by discovering the gap between theory and practice. This study used action research over many years to observe real-world challenges and to make academic research relevant to the challenges. The authors believe that the empirical findings will help improve service practices of data-use and stimulate future research.

Manufacturing firms delivering complex products and systems are increasingly offering advanced data-based services. Customers, however, are not always willing to adopt manufacturers’ advanced services, so manufacturers need knowledge of how to promote customers’ service readiness. The purpose of this paper is to further develop the concept of service readiness by proposing a framework for industrial customers’ organizational dimension of service readiness and by increasing the understanding of the conditions underpinning that service readiness.

This case study explores service readiness among customers of a manufacturer delivering complex systems and related services. Interviews were conducted within the company and among key customers as the potential users of those services.

Customers use versatile processes, engage multiple people and use different evaluation criteria when considering adoption of data-based services. The organizational component of service readiness involves requirements in the service context, supplier relations and organizational habits and culture. Actions are proposed for manufacturers to promote customers’ readiness for new services.

The research is limited through its qualitative design and case selection. Mapping of the organizational dimension of service readiness further develops the concept of service readiness and offers a framework for further research. This research offers novel understanding of organization-level service adoption to complement individual-centric technology adoption.

New knowledge is offered to manufacturing firms about customers’ challenges and requirements in adopting advanced services. This knowledge will help manufacturers to support customers and develop the activities of their own salespeople when introducing advanced services.

The findings expose the contents of the organizational dimension of customers’ service readiness. The study provides a more complete picture of service readiness and shows it to be a multilayered concept with interdependencies between its levels, between individuals in customer organizations and even between the manufacturer and the customer organization.

Digital twins (DTs) and other data-based solutions are gaining an increasing foothold in manufacturing business, whereas a mere physical product is often insufficient to satisfy all customers’ expectations. As a result, companies are seeking novel ways of value creation, and one exciting opportunity is the use of DTs in new business creation, where they can offer diverse possibilities for innovative businesses. This paper aims to examine the impacts and challenges of DTs on new business creation in the manufacturing industry.

This study used a qualitative research approach, which combined semistructured interviews and an iterative Delphi study as research methods. The participants for the interviews and Delphi study were from different sectors and roles in the manufacturing industry. Altogether, 10 interviewees from eight companies took part in the interviews, and the expert panel of the Delphi method contained 12 professionals.

The results of the study indicated that DT can significantly impact the business models of manufacturing companies. DT can enhance operations, offer cost savings and business growth and allow stakeholders to focus on core competencies while developing their businesses. Several challenges for leveraging DT were identified, such as data ownership, resource allocation, internal bureaucracy and the difficulty of demonstrating the actual value of data-based services to potential customers.

This paper provides a structured expert-led assessment of the potential impacts of DT utilization in the creation of new business opportunities.

To better understand the role of industrial big data in promoting digital transformation, the authors propose a theoretical framework of industrial big-data-based affordance in the form of an illustrative metaphor – what the authors call the “organizational drivetrain.”

This study investigates the effective use of industrial big data in the process of digital transformation based on the technology affordance–actualization theoretical lens. A software platform and services provider with more than 4,000 industrial enterprise clients in China was selected as the case study object for analyzing the digital affordance and actualization driven by industrial big data.

Drawing on a revelatory case study, the authors identify three affordances of industrial big data in the organization, namely developing data-driven customized projects, provisioning equipment-data-driven life cycle services, establishing data-based trust and determining affordance actualization actions driven by technology and market. In addition, the authors reveal the underlying drivetrain mechanisms to advance industrial big data affordance and actualization: stabilizing, enriching and pioneering.

This study builds a drivetrain model on digital transformation by industrial big data affordance actualization. The authors also provide practical implications that can help practitioners to implement digital transformation effectively and extract value from their investment.

The choice of a suitable data base for providing an information service is governed by factors of coverage, performance, and cost. The cost of the data base to subscribers is a known quantity, and the coverage is decided by the data base producers. This paper describes an investigation into the relative performance of the four major Chemical Abstracts Service magnetic tape data‐bases, Chemical Titles (CT), which contains the titles of citations only, Chemical Abstracts Condensates (CAC), which contains titles enriched with keyword phrases, Chemical‐Biological Activities (CBAC), and Polymer Science and Technology (POST), both of which contain full digests in addition to titles. The performance was measured in terms of the relative currency of the four data‐bases, and the retrieval efficiency of profiles searched against them. Fifty questions from industrial and government research organizations were used in the experiment. Search profiles corresponding to these questions were constructed for searching against each data‐base, output was assessed for relevance by users, and profile performance figures (precision and recall ratios) were calculated for each profile. The overall retrieval efficiency of profiles searched against data‐bases containing titles only, titles‐plus‐keywords, and titles‐plus‐digests, was calculated, and these results are presented.

In this chapter, the author begins by highlighting the main landmarks in Global Services. Additionally, the author analyzes the relevance of trade in services to key economic indicators such as GDP, employment, FDI, and international trade both at the international level and in the Latin American and Caribbean region.

Increasingly, dynamic market environments lead to growing complexity in manufacturing and pose a severe threat for the competitiveness of manufacturing companies. Systematic guidance to manage this complexity, especially in the context of Industry 4.0 and the therewith rising trends such as digitalization and data-driven production optimization, is lacking. To address this deficit a case-based reasoning (CBR) system for providing knowledge about managing complexity in Industry 4.0 is presented.

First, the explicit knowledge representation for managing complexity in IT-based manufacturing is introduced. Second, the CBR process step to retrieve knowledge from an artificially composed case base with in total 70 cases of data-based complexity management in the context of Industry 4.0 is set out. Third, knowledge transfer alongside several maturity levels of information technology capabilities of manufacturing systems for reuse in new problem scenarios is introduced.

The paper comprises the conceptual approach for designing a CBR system to support data-based complexity management in manufacturing systems. Furthermore, the appropriateness of the CBR system to provide applicable knowledge for reducing and managing complexity in corporate practice is shown.

The presented research results are evaluated in the course of an embedded single case study and may therefore lack generalizability. Future research to test and enhance the appropriateness of the developed CBR system will strengthen the research contribution.

The paper provides a novel approach to systematically support knowledge transfer for data-based complexity management by transferring the well-known and established methodology of CBR to the rising application domain of manufacturing systems in the context of Industry 4.0.

One of the leading factors that shape product and service delivery are data collected in databases and other repositories maintained by companies. The transformation of such data into knowledge and wisdom may constitute a new source of income. This paper aims to explore how small/medium-sized enterprises (SMEs) advance their business models (BMs) around data to handle data-driven products and how this contributes to their innovativeness and performance.

To investigate the phenomenon, the as-is BM of a multinational SME was mapped and its limitations were revealed through a qualitative study. The BM canvas was used. Then the data-driven approach was innovated within the facility management (FM) industry, where a high volume of operational and sensor-based data being collected creates added value in terms of new data-based products.

A data-driven business model (DDBM) blueprint for the FM industry that supports the need to complement service-driven operations with the data-driven approach is delivered. Enhanced BM equips a facility manager with additional managerial tools that enable decreasing property utilization costs and opens up new opportunities for generating revenue. This paper drafts the way to evolve from service to data-driven business and point out the attitudes that managers should adopt to promote and implement DDBM.

The DDBM constitutes a guideline that supports FM organizations in focusing their activities and resources on generating business value from data and monetizing data-driven products.

The research expands knowledge regarding BMs and their evolution. The gap regarding the DDBM innovation within the FM industry is filled.

Access to personal data is key to many of the most successful recent business models. These models rely on individuals outside of traditional organizational boundaries as their product, content providers, and customers. The topic of organizational boundaries is central to organizational research, and these models raise questions about the permeability of these new forms’ boundaries. Herein I elaborate on data-based business models, the organizational field that has emerged around data governance issues, and the institutions that have formed around it at different stages, by various actors. I also explore the interplay of institutional field and organizational boundaries, to identify how field-level issues influence the permeability of organizational boundaries.

The purpose of this paper is to develop a total interpretive structural modelling (TSIM) of strategic factors related to performance management in the context of Indian Telecom Service Providers taking dual perspectives in account, i.e. enterprise perspective and subscribers’ perspective.

An evolving performance management framework, i.e. flexible strategy game-card has been taken as the basis to develop the strategic performance management model, where strategic factors have been identified through thematic content analysis and the hierarchical structure of factors has been developed using TISM.

Two total interpretive structural models have been developed, one for enterprise-related strategic factors, and another for subscriber-related strategic factors. These hierarchical structures portray the leading and lagging factors of performance, and showcase that financial performance indicators are the lagging indicators.

This study has implications for both practitioners and academics. For practitioners, it provides an indicative list of strategic factors relevant for Indian telecom companies, which drive better financial results as well as a list of strategic factors that subscribers consider while assessing the performance of telecom operators. For academics, the methodology used provides a mechanism to conduct an exploratory study by identifying the variables of interest and highlighting their interactions through hierarchical structures.

The proposed strategic performance management models developed through qualitative modelling technique is a new effort altogether in the context of Indian telecom operators’. Capturing subscribers’ perspective of firm's performance is neglected in existing performance management literature, and this study makes a contribution in this regard.