Elsevier

Technovation

Volume 118, December 2022, 102236
Technovation

Discovering IoT implications in business and management: A computational thematic analysis

https://doi.org/10.1016/j.technovation.2021.102236Get rights and content

Highlights

  • •This study sheds light on the topical structure of IoT research in Business and Management context.

  • Following explanatory sequential mixed-method, it represents and analyses 10 identical topics from 347 scholarly articles.

  • This paper investigates the temporal trend of topics to display the hot and cold research topics' distribution over time.

  • IoT-enabled Business Model is the highly attracted topic, so its latent topics are rendered to enrich our understanding.

  • By presenting the research gaps, this paper proposes the future avenues of IoT studies in Business and Management.

Abstract

IoT as a disruptive technology is contributing toward ground-breaking experiences in contemporary enterprises and in our daily life. Rapidly changing business environment and phenomenally evolving technology enhancement necessitate a robust understanding of IoT implications from business and management perspective. The current study benefits from an explanatory sequential mixed-method approach to represent and interpret the inductive topical framework of IoT literature in business and management with emphasis on business model. Bayesian statistical topic model called latent Dirichlet allocation is employed to conduct a comprehensive analysis of 347 related scholarly articles to reveal the topical composition of related research. Further, we followed a thematic analysis for interpreting the extracted topics and gaining in-depth qualitative insights. Theoretical implications with emphasizing on research agenda for future study avenues and managerial implications based on influential themes are provided.

Introduction

Internet of Things (IoT) has flourished over the last decade as a new wave of digital transformation, which enables real-time sensing, collecting and sharing data. The unique features of IoT like ubiquity have enabled the possibility of developing advanced applications across many domains. The momentum IoT has generated makes it an ideal frontier for driving technological innovation (Siow et al., 2018), garnering significant attention from both practitioners and scholars. IoT is perceived as a disruptive innovation given its potentiality to truly reshape our world (Manyika et al., 2013). Pervasive applications of IoT are dramatically transforming many aspects of societies and economies such as healthcare (Pang et al., 2015; Tuan et al., 2019), transportation (Davidsson et al., 2016), logistic (Hopkins and Hawking, 2018), manufacturing (Birkel et al., 2019; Hasselblatt et al., 2018), and tourism (Byun et al., 2017; Gretzel et al., 2015). It is estimated that the IoT market size will reach $1.2 Trillion worldwide by 2022 (IDC, 2018). However, IoT's extensive publicity and promising future do not guarantee its widespread success, since many concerns and potential issues of gaining actual value of IoT are not yet fully known (Nicolescu et al., 2018). IoT mass adoption and actualizing its values depend not only on technological advances but more on understanding its business and managerial needs and challenges. Porter and Heppelmann (2014) maintain that we need to identify the dynamics of IoT technologies from business and management perspective to survive and gain competitive advantage during the technological transformations.

The diffusion trend of IoT leads to a call for studies to advance our understanding of research on managerial and entrepreneurial opportunities of this disruptive innovation (Clarysse et al., 2019). Despite exponentially expanding opportunities arising from IoT and ever-growing attention it attracts among scholars, practitioners, and the general public, a critical literature review indicates the lack of systematic and rigorous study on the business and management perspective of this technology. Mostly, the extant literature has taken a narrow view to discuss specific aspects of IoT business and management such as generating value from IoT data (Hajiheydari et al., 2019), concentrating on IoT applications in servitization (Rymaszewska et al., 2017), or providing a descriptive business model for IoT (Dijkman et al., 2015). This gap highlights the need for an integrative study that considers the current body of knowledge to connect the disciplinary perspective and insight around IoT studies with business and management identity.

There are several grounds that signify examining IoT from the business and management lens is both timely and essential. First, the ever-increasing growth of investment, the predicted market size (IDC, 2018), and the continuous introduction of pervasive applications (Forbes, 2019) necessitate understanding of IoT business implications. Further, calls continue for the ‘Managerial and Entrepreneurial Opportunities and Challenges of IoT’, principally based on the role of this disruptive technology in generating new venture opportunities, shifting the nature of competition, and eroding the traditional business models (Clarysse et al., 2019). Finally, due to growing expansion of IoT applications and related publications, researchers suggest quantitatively examining the related literature (Lu et al., 2018), to explore the hidden thematic structure of IoT research (Yoon et al., 2018), and IoT issues associated with managerial and organizational areas and theories (Mishra et al., 2016).

Previous studies have mainly focused on ‘general IoT research domain’. By applying either quantitative or qualitative methods, researchers attempted to examine the generic IoT knowledge field and objectively or subjectively analyse the literature. Co-word analysis (Kim and Kang, 2018; Yan et al., 2015), co-citation analysis (Ng et al., 2018), bibliometrics (Mishra et al., 2016), and scientometrics approaches (Erfanmanesh and Abrizah, 2018) are some of quantitative methods have been used to explore IoT research domain. On the other stream, qualitative and mainly literature review approaches have been followed to examine the IoT study domain (e.g., Atzori et al., 2010; Li et al., 2015; Siow et al., 2018; Lu et al., 2018). It thus appears that scholarly attempt with direct focus on uncovering the intellectual structure of IoT literature from the business and management perspective is largely disregarded. This study contributes to advancing the current discourse on IoT in particular considering business and management issues more holistically, by integrating, representing and synthesizing current knowledge through an innovative methodological approach.

The main goal of this systematic and rigorous research is to map and link the knowledge landscape of IoT in business and management domains. To this aim, the present study seeks to: (i) extract the inductive topical framework to portray the IoT research field in business and management, and more specifically for the highly focal domain of ‘business model’; (ii) analyse and explain the main business and management latent themes and sub-themes in the research field of IoT; and (iii) highlight the trend of business and management studies in the IoT field to detect novelty and emergence. To address these objectives, we analysed the corpus of IoT research in the business and management disciplines applying an explanatory sequential mixed-method approach. This study thereby provides three key contributions. First, it drives and presents phenomenon-based constructs and grounded conceptual relationships in the IoT literature on business and management. Second, we explore and discuss the related latent subjects of these constructs and their relationships, with special attention to the business model theme. Finally, we provide theoretical contribution by proposing research agenda for future study avenues in this context, based on the identified thematic map.

Section snippets

Research method

As quantitative and qualitative methodological approaches both have certain weaknesses (Gioia et al., 2013), researchers have called for new methods to examine organizational phenomena (Taras et al., 2009). Some propose combining them to take the advantages of both methods, addressing their limitations, and overcoming the trade-off between performing large-scale quantitative analytics and gaining in-depth qualitative insights (Creswell and Clark, 2011, p. 17; Schmiedel et al., 2018). Thereby,

Results

Fig. 2 presents the global view of the topic model on IoT business and management research area. This view visualizes the output of topic modelling, wherein circles represent topics in a two-dimensional plane. Areas of the circles are proportional to the relative prevalence of the topics in the corpus. The centres of circles are estimated by calculating the distance between topics and further projected onto a two dimensions space by using multidimensional scaling to reflect the inter-topic

Theoretical implication and future research

Our findings contribute to the literature on business and management of IoT in three ways. First, the findings of this study uncover and present a thematic map of IoT research streams in business and management domains. This study is a response to a call by Lu et al. (2018) that highlight a need for a more quantitative approach to drive and present the inductive classification framework for eliciting the latent structure of IoT extant literature. To map out a broad and rich picture of the

References (210)

  • R.M. Dijkman et al.

    Business models for the Internet of things

    Int. J. Inf. Manag.

    (2015)
  • S. Escolar et al.

    A multiple-attribute decision making-based approach for smart city rankings design

    Technol. Forecast. Soc. Change

    (2019)
  • E. Fadda et al.

    Customized multi-period stochastic assignment problem for social engagement and opportunistic IoT

    Comput. Oper. Res.

    (2018)
  • T. Fan et al.

    Impact of RFID technology on supply chain decisions with inventory inaccuracies

    Int. J. Prod. Econ.

    (2015)
  • M. Farhan et al.

    IoT-based students interaction framework using attention-scoring assessment in elearning

    Future Generat. Comput. Syst.

    (2018)
  • H. Gebauer et al.

    Competitive advantage through service differentiation by manufacturing companies

    J. Bus. Res.

    (2011)
  • G.L. Geerts et al.

    A supply chain of things: the EAGLET ontology for highly visible supply chains

    Decis. Support Syst.

    (2014)
  • X. Gong et al.

    An efficient genetic algorithm for large-scale planning of dense and robust industrial wireless networks

    Expert Syst. Appl.

    (2018)
  • L. Guo et al.

    Investigating e-business models' value retention for start-ups: the moderating role of venture capital investment intensity

    Int. J. Prod. Econ.

    (2017)
  • H. Jiang et al.

    A topic modeling based bibliometric exploration of hydropower research

    Renew. Sustain. Energy Rev.

    (2016)
  • A.D. Joshi et al.

    Evaluation of design alternatives of end-of-life products using Internet of things

    Int. J. Prod. Econ.

    (2019)
  • B. Kamp et al.

    Servitization and advanced business services as levers for competitiveness

    Ind. Market. Manag.

    (2017)
  • D. Kiel et al.

    The influence of the industrial Internet of things on business models of established manufacturing companies - a business level perspective

    Technovation

    (2017)
  • D.H. Kim et al.

    Standards as a driving force that influences emerging technological trajectories in the converging world of the Internet and things: an investigation of the M2M/IoT patent network

    Res. Pol.

    (2017)
  • R. Agrifoglio et al.

    How emerging digital technologies affect operations management through co-creation. Empirical evidence from the maritime industry

    Prod. Plann. Contr.

    (2017)
  • B. Amshoff et al.

    Business model patterns for disruptive technologies

    Int. J. Innovat. Manag.

    (2015)
  • D. Antons et al.

    Big data, big insights? Advancing service innovation and design with machine learning

    J. Serv. Res.

    (2018)
  • C. Arnold et al.

    How the industrial Internet of things changes business models in different manufacturing industries

    Int. J. Innovat. Manag.

    (2016)
  • R. Arun et al.

    June). On finding the natural number of topics with latent dirichlet allocation: some observations

    Pacific-Asia Conference on Knowledge Discovery and Data Mining

    (2010)
  • T. Baines et al.

    A Delphi study to explore the adoption of servitization in UK companies

    Prod. Plann. Contr.

    (2015)
  • T.S. Baines et al.

    The servitization of manufacturing: a review of literature and reflection on future challenges

    J. Manuf. Technol. Manag.

    (2009)
  • H.S. Birkel et al.

    Development of a risk framework for industry 4.0 in the context of sustainability for established manufacturers

    Sustainability

    (2019)
  • D.M. Blei

    Probabilistic topic models: surveying a suite of algorithms that offer a solution to managing large document archives

    Commun. ACM

    (2012)
  • D.M. Blei et al.

    Latent dirichlet allocation

    J. Mach. Learn. Res.

    (2003)
  • T. Böhmann et al.

    Service systems engineering

    Business & Information Systems Engineering

    (2014)
  • V. Braun et al.

    Using thematic analysis in psychology

    Qual. Res. Psychol.

    (2006)
  • V. Braun et al.

    Thematic analysis

  • F. Burkitt

    A Strategist's Guide to the Internet of Things

    (2014)
  • J. Byun et al.

    4G LTE network access system and pricing model for IoT MVNOs: spreading smart tourism

    Multimed. Tool. Appl.

    (2017)
  • A.I. Canhoto et al.

    Exploring the factors that support adoption and sustained use of health and fitness wearables

    J. Market. Manag.

    (2017)
  • R. Casadesus‐Masanell et al.

    Business model innovation and competitive imitation: the case of sponsor‐based business models

    Strat. Manag. J.

    (2013)
  • A.H. Celdrán et al.

    SeCoMan: a semantic-aware policy framework for developing privacy-preserving and context-aware smart applications

    IEEE Systems Journal

    (2014)
  • J. Chang et al.

    Reading tea leaves: how humans interpret topic models

    Adv. Neural Inf. Process. Syst.

    (2009)
  • K. Charmaz

    Constructing Grounded Theory

    (2014)
  • J. Chen et al.

    Rotating directional sensors to mend barrier gaps in a line-based deployed directional sensor network

    IEEE Systems Journal

    (2014)
  • M. Chen et al.

    Big data: a survey

    Mobile Network. Appl.

    (2014)
  • N.H. Chen

    Extending a TAM-TTF model with perceptions toward telematics adoption

    Asia Pac. J. Market. Logist.

    (2019)
  • A.Y.L. Chong et al.

    Predicting RFID adoption in healthcare supply chain from the perspectives of users

    Int. J. Prod. Econ.

    (2015)
  • J. Chuang et al.

    May). Interpretation and trust: designing model-driven visualizations for text analysis

  • B. Clarysse et al.

    The Internet of Things: Managerial and Entrepreneurial Opportunities and Challenges

    (2019)
  • Cited by (26)

    View all citing articles on Scopus
    View full text