Elsevier

Decision Support Systems

Volume 57, January 2014, Pages 1-10
Decision Support Systems

A complexity theory approach to IT-enabled services (IESs) and service innovation: Business analytics as an illustration of IES

https://doi.org/10.1016/j.dss.2013.07.005Get rights and content

Highlights

  • Complexity theory is suitable for understanding IES (e.g., business analytics service) and IES innovation.

  • IES can be effectively conceptualized as complex adaptive systems (CAS).

  • The environment of IES is rugged and dancing and co-evolves with IES.

  • IES innovation is a co-evolutionary process of variation, selection, and retention (VSR).

  • Organizations can increase IES innovation by adopting a guided VSR.

Abstract

While firms view services as the main source of their revenue and competitive advantage, understanding of service and service innovation is limited. This lack of understanding is especially significant in IT-Enabled Services (IESs) and IES innovation. Much work is needed to understand the contemporary trend of integrating diverse material and social resources to address complex organizational and individual needs. This article proposes a novel framework for IES and IES innovation and develops propositions and implications for research and practice. This work draws upon the tenet of complexity theory and conceptualizes IES as complex adaptive systems (CAS), with such properties and behaviors as diverse adaptive elements, nonlinear interaction, self-organization, and adaptive learning, and IES innovation as a co-evolutionary process of variation, selection, and retention (VSR). The proposed framework is illustrated using business analytics (BA) as a new kind of decision support service (DSS) throughout this paper. Several propositions are developed. Finally, we present a discussion and implications.

Introduction

Firms in the business-to-business (B-to-B) market now increasingly view services, rather than traditional products, as the main source of their revenue and competitive advantage [18], [20], [54], [67], [77]. To this end, there are several streams of research and practice in what is now called “service innovation”, from economics [26], operations [49], marketing [54], strategy [18], and organization science [20]. Particularly, information systems (IS) literature discusses information technology (IT), along with services and service innovation, as a primary driver for economic growth and firms' competitive advantage [51], [76], [77]. Previous studies have suggested many roles for IT in service innovation [14], [22], [23], [34]. What is most evident is that IT (e.g., the Internet) has profoundly changed the ways customers interact with service providers [25]. Many traditional services (e.g., healthcare) are now delivered through IT (e.g., IT-enabled healthcare) [22], [74], often increasing operational efficiency. However, the role of IT does not stop here: certain new services, such as digitized services [76], remote diagnostic decision services [31] and cloud-based decision support services [19], are simply not possible without ubiquitous IT and big data management platforms. Thus, not only is IT (re)combined with traditional services and goods, but emerging technologies also enable the emergence of a new generation of services. Simply put, IT is integral to a growing number of services [33].

We call these services IT-enabled services (IESs). IESs differ from traditional IT products, such as word processors and email software, for which production (producer) and consumption (user) tend to be separable. These products often stand alone and support pre-defined requirements, such as storing texts and transmitting messages. IESs also differ from traditional business consulting services, through which a customer gets a master plan (e.g., strategic roadmaps) as the final deliverable from a service provider. Instead, IESs emerge through combining IT and other organizational resources (e.g., processes, skills, work practices) [20], [74], which are sourced from beyond organizational boundaries, including traditional providers, customers and even third-parties (e.g., developers). Related concepts are integrated solutions [18], servitization [66] and resource configurations [69]. An example is business analytics (BA) service, which combines various tangible and intangible resources, such as high-quality data, analytical talents, processes, analytical software, powerful hardware and data management platforms, and other organizational resources from providers, customers, and others [17]. There is considerable potential for researchers in IS to make contributions to the emerging debates and challenges in IES and service innovation [6], [7], [51].

In the literature, there are potentially useful perspectives and theories [67] for the research and practice of service and service innovation in general. Both research and practice are growing in these areas. Compared to product innovation, however, there is still a very limited understanding about service and service innovation in general: relevant theory and empirical work are quite limited [55], [75]. This lack of understanding is especially significant in IES and their innovation [7], [13]. Much work is needed to understand the contemporary trend of integrating diverse material and social resources to address complex organizational and individual needs.

This article aims to respond by developing a novel theoretical perspective on IES and IES innovation. The proposed perspective draws upon the tenet of complexity theory and conceptualizes IES as complex adaptive systems (CAS) and IES innovation as a co-evolutionary process. From this perspective, IESs are viewed as internally complex, dynamic, adaptive, and emergent. They are co-evolving with a wide network of technologies, organizational and individual needs, rules and regulations, institutional arrangements, etc., through variation, selection, and retention (VSR). Through this, this paper broadly covers two important topics in both IES research and practice: what IESs are; and, how such services emerge and evolve. How something (e.g., IT artifacts) is conceptualized influences our way of studying, analyzing, designing, delivering, and changing it [47]. Thus, the inquiry into IES conceptualizations is important, since it reveals what constitutes an IES and the interaction of its internal elements, and thus affects the way “various research priorities for the science of service” [49] are examined. However, focusing on “entities” may not be sufficient: understanding process is also necessary [24]. Thus, studying IES-related service innovation is necessary, since it is the process by which IES comes to life and evolves. We use BA service, as an example of IES, to illustrate the proposed perspective throughout this paper.

The contribution of this paper is multi-faceted. First, there is a recognition that complexity theory can be useful for service science [69] and IS research [61]. This research offers complexity theory as the basis for explaining IES and service innovation. Second, there is not yet consensus on service, in general, and IES, in particular. Many existing studies tend to offer a product-oriented view of service, which undoubtedly leads to a static view of IES, or an intangible view in which IT is either absent or treated as passive. The proposed CAS framework offers a dynamic view that IES are the emergent structures from the interaction of diverse adaptive elements (tangible and intangible). This helps properly explain the properties and behaviors of IES in practice, and further formulate ways to take advantage of such properties and behaviors. Third, innovation in the CAS view is evolutionary. Thus, IES innovation is formulated as a co-evolutionary process of VSR, through which IES and the environment are co-evolving. This is a novel framework that can help develop strategies for designing and managing IES by taking advantage of the evolutionary nature of IES innovation.

In what follows, we briefly review the literature on BA as an example of IES. The next section summarizes existing views of service and service innovation and suggests the need for a perspective to explain complex properties and dynamic behaviors inherent in BA service and other IESs, and the co-evolutionary process in IES innovation. Then, we introduce complexity theory and propose a novel framework for IES and IES innovation. Drawn from the proposed framework, theoretical propositions are developed and several implications for theory and practice are discussed. This discussion includes what strategies are effective for IES innovation.

Section snippets

Business analytics services: an example of IT-enabled services

BA service is neither a single IT product nor a set of non-IT resources. Rather, this emerging service is a configuration of organizational resources with diverse ITs, which aims to enable more efficient and effective decision-making by organizations and individuals. A typical BA service involves complex data and analytics technologies (e.g., in-memory analytics, parallel programming), enabling “the creation, modification, and manipulation of digital artifacts in the process of converting input

Literature review on (IT-enabled) service and service innovation

This review will suggest the need for a perspective to explain dynamic properties and behaviors inherent in IES and IES innovation.

Complex adaptive systems (CAS), environment, and co-evolution

In this section, we present a brief overview of CAS. A CAS is defined as “a system in which large networks of components with no central control and simple rules of operation give rise to complex collective behavior, sophisticated information processing, and adaptation via learning or evolution” [42]. There are many examples of CAS, from biological organisms to economies [9]. While they differ in details, in general there are three coherent dimensions to discuss CAS1

A complexity theory framework for IES and IES innovation

As presented, extant conceptualizations of service have evolved from physical goods or intangible supplements for products to integrated solutions or configurations of resources. Recent views (e.g., service system) are drawn from systems thinking. The previous section offers an overview of CAS. This section presents a complexity theory framework for IES and IES innovation (see Table 4 for summary).

Theoretical propositions

The previous section presented a novel perspective on IES and IES innovation, using BA as an illustration. From this complexity perspective, this section develops theoretical propositions related to IES as CAS, and IES innovation as a co-evolutionary process.

Theoretical and practical implications

The previous section presented a novel perspective on IES and IES innovation and developed theoretical propositions. This section offers several implications for the theory and practice of IES and IES innovation.

Conclusion

While the economies of many countries are shifting from manufacturing to service, understanding of service and service innovation is quite limited [75]. This lack of understanding is especially significant in IES and IES innovation, where IT plays important roles as core or peripheral elements. Drawn upon the tenet of complexity theory, this article has developed a novel framework for understanding IES as CAS and IES innovation as a co-evolutionary process of variation, selection, and

Bongsug (Kevin) Chae is Associate Professor in Information & Operations Management at Kansas State University. He has published papers in such areas as business analytics, supply chain management, service innovation, and knowledge discovery. He has made presentations in universities and global companies in several countries, primarily on the topic of business analytics and intelligence, supply chain management, and service innovation. He is a recipient of the Ralph Reitz Teaching Award and has

References (77)

  • H. Zhang et al.

    Service science in information systems research

    Decision Support Systems

    (2012)
  • H. Aldrich

    Organizations and Environments

    (2006)
  • S. Alter

    Viewing systems as services: a fresh approach in the IS field

    Communications of the Association for Information Systems

    (2010)
  • P. Anderson

    Complexity theory and organization science

    Organization Science

    (1999)
  • L. Argote et al.

    Transactive memory systems: a microfoundation of daynamic capabilities

    Journal of Management Studies

    (2012)
  • R. Axelrod et al.

    Harnessing Complexity

    (2000)
  • I. Bardhan et al.

    An interdisciplinary perspective on IT services management and service science

    Journal of Management Information Systems

    (2010)
  • M. Barrett et al.

    Call for papers MISQ special issue on service innovation in the digital age

    MIS Quarterly

    (2011)
  • R. Basole et al.

    Complexity of service value networks: conceptualization and empirical investigation

    IBM Systems Journal

    (2008)
  • E. Beinhocker

    The Origin of Wealth: Evolution, Complexity, and the Radical Remaking of Economics

    (2006)
  • A. Bitterer

    Hype Cycle for Business Intelligence

    (2011)
  • H. Chen et al.

    Business intelligence and analytics: from big data to big impact

    MIS Quarterly

    (2012)
  • H. Chen et al.

    Service-oriented challenges for design science: charting the “E”-volution

    Pacific Asia Journal of the Association for Information Systems

    (2010)
  • J. Chen et al.

    Service delivery innovation: antecedents and impact on firm performance

    Journal of Service Research

    (2009)
  • R. Cooper

    Perspective: the sate-gate idea-to-launch process: update, what's new, and NexGen systems

    Journal of Product Innovation Management

    (2008)
  • T.H. Davenport et al.

    Competing on Analytics: The New Science of Winning

    (2007)
  • A. Davies et al.

    Charting a path toward integrated solutions

    MIT Sloan Management Review

    (2006)
  • H. Demirkan et al.

    Leveraging the Capabilities of Service-Oriented Decision Support Systems: Putting Analytics and Big Data in Cloud

    Decision Support Systems

    (2012)
  • D. Dougherty et al.

    Organizing ecologies of complex innovation

    Organization Science

    (2011)
  • O. El Sawy et al.

    Seeking the configurations of digital ecodynamics: it takes three to tango

    Information Systems Research

    (2010)
  • A. Essen

    The emergence of technology-based service systems

    Journal of Service Management

    (2009)
  • J. Ettlie et al.

    Service versus manufacturing innovation

    Journal of Product Innovation Management

    (2011)
  • M. Feldman et al.

    Theorizing practice and practicing theory, organization science

    Articles in Advance

    (2011)
  • J. Fitzsimmons et al.

    Service Management: Operations, Strategy, Information Technology

    (2008)
  • F. Gallouj et al.

    Innovation in services: a review of the debate and a research agenda

    Journal of Evolutionary Economics

    (2009)
  • V. Grover et al.

    Cocreating IT value: new capabilities and metrics for multifirm environments

    MIS Quarterly

    (2012)
  • A. Henderson et al.

    Selection-based learning: the coevolution of internal and external selection in high-velocity environments

    Administrative Science Quarterly

    (2004)
  • J. Holland

    Hidden Order

    (1995)
  • Cited by (52)

    • Circular economy in the construction industry: A review of decision support tools based on Information & Communication Technologies

      2022, Journal of Cleaner Production
      Citation Excerpt :

      Decision support tools can be regarded as a special type of information systems. They are primarily developed for the collection and analysis of data, and extraction of knowledge that can be regarded either as a basis for a better understanding of the current situation, or as a source of predictive insights into future development trends, in order to support decision-making at a managerial level (Chae, 2014). However, the design processes of these tools are not always explicitly documented in the literature.

    • The role of business analytics capabilities in bolstering firms’ agility and performance

      2019, International Journal of Information Management
      Citation Excerpt :

      BA capabilities positively impact information quality. Past studies showed that the importance of firms’ capability to extract environmental information to reveal new business opportunities and evolve/innovate consistently (Bose, 2009; Chae, 2014; Wang & Dass, 2017). The extracted data help managers find the most efficient way to uncover new business opportunities (Ashrafi & Zare Ravasan, 2018; Howson, 2007; Sivarajah, Kamal, Irani, & Weerakkody, 2017).

    • A General framework for studying the evolution of the digital innovation ecosystem: The case of big data

      2019, International Journal of Information Management
      Citation Excerpt :

      This disruptive innovation is not created in a vacuum. Big data innovation has evolved from the past (e.g., business intelligence, data mining, data warehousing) and combines new resources, such as analytical platforms (e.g., R, Python), computing architecture (e.g., high performance computing), data processing frameworks (e.g., Hadoop), infrastructure (e.g., cloud computing, large data centers), analytical talents, beliefs, methodologies, professional meetings, and institutions (e.g., regulations, privacy) (2015a, Chae, 2014; Hashem et al., 2015; Sagiroglu & Sinanc, 2013). The proposed framework combines complex networks as a conceptual lens and computational research methodology for the empirical inquiry.

    View all citing articles on Scopus

    Bongsug (Kevin) Chae is Associate Professor in Information & Operations Management at Kansas State University. He has published papers in such areas as business analytics, supply chain management, service innovation, and knowledge discovery. He has made presentations in universities and global companies in several countries, primarily on the topic of business analytics and intelligence, supply chain management, and service innovation. He is a recipient of the Ralph Reitz Teaching Award and has been nominated for several teaching awards at Kansas State University.

    View full text