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Technology Innovation Management Review
Number of Followers: 6  

  This is an Open Access Journal Open Access journal
ISSN (Print) 1927-0321
Published by Carleton University Homepage  [3 journals]
  • Antecedents, Decisions, and Outcomes of a Sharing Economy: A Systematic
           Literature Review

    • Authors: TIM Review
      Abstract: Why Not Share Rather Than Own'Russell BelkIntroductionOver the last decade, both the notion of a “sharing economy” (SE) and collaborative consumption have changed the way consumers are exhibiting consumption behavior through digital spaces. “Sharing” can be seen an ancient practice, while a SE as a consumption practice with the help of technological innovation is recent Belk (2014). Sharing Economies (SEs) as a research phenomenon themselves become prominent after 2008 with a majority of publications (from developed and emerging markets) spanning across the industry after 2013.SEs are an economic phenomenon aiming to ensure access to underutilized assets and resources by different individuals through a digital platform. Through a digital platform, matchmaking is enabled between users and providers of the resources. Pallesen and Aakjaer (2020) investigated a SE as a path to welfare innovation where a digital platform is established to support citizens with lung cancer, demonstrating the use of a SE by the public sector to extend its goals. Ruben et al. (2020) examined trust, transparency, and security in SEs. Access to information is considered as one of the important digital cues to ensure trust. The study posits the role of government to facilitate information access as a way to enhance trust.SEs have many synonyms and the SE phenomenon overlaps with various concepts like “collaborative consumption”, “collaborative economy”, “access economy”, “platform-based economy”, and “community-based economy”. Hamari et al. (2016) linked SEs to collaborative consumption and defined them as a “peer to peer" based activity of obtaining, giving, or sharing the access of goods and services coordinated through community-based online services. Digital platforms are starting to provide block chain technology-based opportunities for SEs. The literature available on SE has much complexity, inconsistency, challenges, and conceptual overlapping Acquier et al (2017).A balanced explanation of the concept was given in the form of sharing exchange continuum by Belk (2007), as well as Gupta et al. (2019). Belk (2007) writes, that a “Sharing Exchange continuum was developed for the purpose of mapping any SE practices to determine how much non ownership forms of consumption consists of sharing related attributes” Belk(2007). For implementing a sharing exchange continuum, if any practice is categorized as a SE practice then a sharing score is calculated based on sharing vs. exchange related characteristics. SE practices, based on a calculated sharing score, are then placed on the continuum to understand whether a practice under consideration is closer to pure exchange, pure sharing, or balancing the two contrasting typologies (Habibi et al. (2017).An SE is applied when there are unused or underutilized resources with an individual intending to share the same with others for their utilization and usage. The idea is basically to ensure community building, ownership to access, and contribution towards sustainable goals. Major beneficiaries would be the service providers who contribute the resources and assets, users who avail the services, assets, and digital platforms that facilitate matchmaking between service providers and users.Though “sharing economy” is considered as a contested concept (Acquier et al. (2017). it is essential to review the literature available to dig deeper into the phenomenon (Gruszka, 2017). A previous research paper by Cheng (2016)reviewed 66 articles related to SE, out of which 10 specifically related to tourism and hospitality between 2010-2015. Altinay and Taheri (2019)reviewed the specific literature on SE related to tourism and hospitality to explain emerging theories and themes related to SE. Hossain (2020)conducted a comprehensive literature review on SEs, which presented a thematic analysis of selected papers between 2016-2018. In our study, we attempted to select the articles as recent as possible with a wider range of years, that is, from (2014-2020) as most publications associated with SE were published after 2013.Our study selected 93 articles for a literature review to address the knowledge gap by contributing antecedents, decisions, and outcomes (ADO) of SEs. “Antecedents” are defined as the key motives of SE participants, “decisions” are key decisions and characteristics of SEs, and “outcomes” are key outcomes and impacts of SEs. This review also studies various theoretical lenses used to understand the SE phenomenon. While previous review studies conducted on SE have concentrated more on SE in tourism and hospitality, our study does not concentrate on one specific sector. Rather, we proposed the given framework based on relevant literature picked across the sectors from developed and emerging economies addressing the contextual gap.In this article, we review empirical as well as theoretical studies published between the years 2008 to 2020 to understand both SEs and collaborative consumption, through the main characteristics of such phenomena in terms of antecedents, decisions, and motivations. This period was selected because the term “sharing economy” was first coined by Lawrence Lessig in 2008. The research questions addressed by this study investigate antecedents of SE as the key motives and enablers for SE, to examine the decisions that the key characteristics of a SE to participate, process, and explore the outcomes of SEs as a p...
      PubDate: Mon, 28 Mar 2022 06:24:01 +000
      DOI: 10.1016/j.ijhm.2016.03.005
  • The Role of Digital Platforms in Resident-Centric Housing Concepts

    • Authors: TIM Review
      Abstract: So we have to be idealists, in a way — because then we wind up as the true, the real realists.Viktor Frankl (1905-1997)IntroductionMegatrends such as aging, urbanization, sustainability, digitalization, and communality are reflected in the diverse needs and expectations of housing. In addition, servitization and changing consumer habits constitute significant drivers of change in housing-related industries (Siltaloppi, 2015). Our homes and living environments have also become a part of our self-realization. In their daily lives, people look for new ways to acquire and co-produce the services they need, for instance enabled by a sharing economy and related platforms (Acquier et al., 2019). Meanwhile, housing residents are understood as playing active roles in value co-creation, while companies adopt networked and data-driven value creation logic (Lusch & Nambisan, 2015; Siltaloppi, 2015; Vargo & Lusch, 2016). These trends enable opportunities to challenge established value creation logic and industrial boundaries between construction and residential service businesses, by means of more demand-driven and agile service models enabled by digital platforms.This also creates a huge challenge across industries, in both B2C and B2B markets. In construction and residential contexts, profound transformation in value creation and capture logic is required to align with servitization: First, a shift from transactional business models towards service- and customer-orientated business models (Siltaloppi, 2015; Xu et al., 2019; Mikkola et al., 2020); and second, a shift towards more networked and data-driven business models that build on the platform economy (Leminen et al., 2018; Maxwell, 2018; Woodhead et al., 2018; Xu et al., 2019; Lappalainen & Federley, 2020). The ongoing changes primarily relate to the expansion and diversification of the construction and real estate services industries, as new innovative service models and actors emerge alongside traditional actors and roles to challenge established operating and thinking patterns. The construction phase is crucial from the life cycle building perspective and related data-driven value creation opportunities. Yet, there remains a kind of ecosystem gap in terms of different actors, governance, and shared logic between construction and other life cycle phases of buildings, such as use, operation, maintenance and renovation (Xu et al., 2019; Mikkola et al., 2020). Further, research has still concentrated on firm-level service innovations, but not as much on the impact of changing business models on the operation and composition of business ecosystems (Petrulaitiene et al., 2017; Leminen et al., 2018; Lappalainen & Federley, 2020).While data-driven value creation opportunities for a platform economy in residential housing contexts are largely untapped and unstudied, the purpose of this article is to examine what kind of value creation opportunities digital platforms enable in housing concepts and related ecosystems. This study adopts a service-dominant logic approach to the housing context (Vargo & Lusch, 2016). It offers a holistic view on housing, comprised of promoting multi-sided value creation and optimal integration of resources between actors. The study focuses on comprehensive housing concepts that combine physical, social, and digital solutions provided by a local service ecosystem. Digital solutions and platforms are developed to make service exchange and shared resources easily available for residents, but also to support further development and new value co-creation opportunities, for example, through network effects.The paper adopts a networked and systemic perspective in particular to narrow the research gap highlighted in recent studies (Fehrer et al., 2018; Leminen et al., 2018). We define platform ecosystems theoretically according to “design” and “co-evolutionary” perspectives. We elaborate a conceptual platform design framework based on the literature (Parker et al., 2016; Täuscher & Laudien, 2018; Tura et al., 2018; Sorri et al., 2019; Hein et al., 2020; Isckia et al. 2020) and apply it for analyzing empirical findings from a multi-case study of holistic housing concepts. In the next section, we present the theoretical background, followed by the methodology and case descriptions of the empirical study. The article continues with a summary of the main findings and ends with a discussion and conclusion, including implications, limitations, and suggestions for further research.Theoretical Background  Housing as a service platform - framed by service innovation concept of S-D logicDriven by service-dominant (S-D) logic, “service innovation” can be defined as complex network- and information-centric value co-creation by resource re-bundling in novel ways among beneficiaries (Lusch & Nambisan, 2015). S-D logic and taking a broader view of service innovation have inspired scholars across disciplines to also examine more specific mechanisms of data-driven service innovation that have been enabled by advanced technologies (Lehrer et al., 2018; Kugler, 2020). However, in the housing context, the S-D logic approach to studying innovative service concepts still seems rather unknown, and with a particular lack of empirical research (Siltaloppi, 2015; Lappalainen & Federley, 2020).Lusch and Nambisan (2015) suggested a tripartite service innovation framework, comprised of service platforms, value co-creation processes, and servi...
      PubDate: Mon, 28 Mar 2022 06:16:58 +000
      DOI: 10.1111/jpim.12105
  • Technology Project Summaries as a Predictor of Crowdfunding Success

    • Authors: TIM Review
      Abstract: It’s fine to celebrate success, but it is more important to heed the lessons of failure.Bill GatesCo-founder of MicrosoftIntroductionCrowdfunding has become an important channel for innovators, entrepreneurs, and incumbents to raise funds for developing new technology products and business ideas (Yuan et al., 2016; Kraus et al., 2016; Dushnitsky et al., 2016; Brem et al., 2019; Popescul et al., 2020; Rrustemi & Tuchschmid, 2020; Sahaym et al., 2021). Crowdfunding has been defined as “the efforts by entrepreneurial individuals and groups – cultural, social, and for-profit – to fund their ventures by drawing on relatively small contributions from a relatively large number of individuals using the internet, without standard financial intermediaries” (Hörisch, 2015; Simons et al., 2019). Unlike traditional funding and investment options, crowdfunding is an alternative digital multisided marketplace that stays open to everyone (Kraus et al., 2016; Hoegen et al., 2018; Isabelle et al., 2019; Koch & Siering, 2019). It thereby aims to collect small amounts of money from many non-professional investors, rather than large amounts of money from a few professional investors (Simon et al., 2019). The benefits of crowdfunding include online platforms that allow for efficient matching of fund-seekers and funders, aggregating small donations into large pools of capital, lowering geographic barriers to fundraising, funding projects that may otherwise be outside of traditional funding methods, and democratizing research and exploration in underexplored fields (Pomeroy et al., 2019; Popescul et al., 2020; Felipe et al., 2022). Crowdfunding platforms provide fund-seekers and funders with means for investment transactions to take place that create value (that is, via legal groundwork, pre-selection screening, and processing financial transactions), as well as allowing for the testing of new products, estimating demands, and running new marketing campaigns (Cordova et al., 2015; Lukkarinen et al., 2016; Borst et al., 2018; Wehnert et al., 2019; Popescul et al., 2020).  According to Koch and Siering (2019), a successful funding of crowdfunding campaigns can be important for founders, investors, platform operators, and other interest groups. However, success in raising capital through crowdfunding involves non-professional investors and happens online may not be easy and the determinants of investment decisions on crowdfunding platforms may be different than in traditional investing environments (Lukkarinen et al., 2016; Hoegen et al., 2018; Song et al., 2019; Popescul et al., 2020; Cappa et al., 2021). Rosetto and Regner (2018) found that most successful crowdfunding projects are not succeeding for 75 percent of their funding period. Further, Liang et al. (2019) noted that the success rate of projects that reach their crowdfunding goal is low (for example, 33 percent on Kickstarter), implying a need for research on what affects funders’ intentions to sponsor or not sponsor a project. Borst et al. (2018) argued that, for example, the online nature of crowdfunding may amplify a “bystander effect”, which suggests that potential funders may withhold funding because they assume that others will provide funding. While research to understand and predict crowdfunding success has accelerated in recent years (for example, Majumdar & Bose, 2018; Song et al., 2019; Felipe et al., 2022), it has often focused on highly specific industrial domains, such as green energy (Hörisch, 2015; Kubo et al., 2021), restaurants (Lelo de Larrea et al., 2019), medical solutions (Ba et al., 2021), video games (Song et al., 2019), or space exploration (Pomeroy et al., 2019). Alternatively, research has also addressed multiple domains and numerous variables at once (for example, Parhankangas & Rernko, 2017; Zhou et al., 2018; Song et al., 2019; Ryoba et al., 2021).  More accurate prediction models may be provided by widening up a large number of variables into the research investigations, such as including project and funding level (Liang et al., 2019), the entrepreneur’s gender (Johnson et al., 2018; Geiger & Moore, 2022), education (Allison et al., 2017), number of social network ties (Lukkarinen et al., 2016; Borst et al., 2018; Hoegen et al., 2018), number of comments and blog entries, and presence of a video appeal (see Kraus et al., 2016; Wang et al. 2018; Geiger & Moore, 2022; Kubo et al., 2021; Ryoba et al., 2021). However, applying such complex models into practice can be difficult. Fundraising has also been suggested as dependant upon how funding requests are placed (Majumdar & Bose, 2018), implying that crowdfunding decisions could depend on the content and persuasiveness of short-text descriptions that summarize a fund-seeking project’s main idea (Parhankangas & Renko, 2017; Majumdar & Bose, 2018; Koch & Siering, 2019; Yeh et al., 2019). This possible avenue of exploration gives raise to our research question for this paper: can we identify what matters for funders deciding whether or not to sponsor fund-seekers by investigating fund-seeking project summaries and using that information to predict project crowdfunding success' Automated content analysis of texts can help to identify key topics in textual data (Yuan et al., 2016; Costello & Lee, 2022). One particular method of conten...
      PubDate: Sun, 27 Mar 2022 22:09:11 +000
      DOI: 10.3389/fpsyg.2020.588121
  • Can Artificial Intelligence be a Critical Success Factor of Construction
           Projects' Practitioner perspectives

    • Authors: TIM Review
      Abstract: Although the original vision for artificial intelligence was the simulation of (implicitly human) intelligence, research has gradually shifted to autonomous systems that compete with people.Susan L. Epstein (2015)IntroductionArtificial Intelligence (AI) can be defined as constructing computer programs that (i) are capable of exhibiting intelligence, (ii) exhibit intelligence by using processes used by humans for the same tasks, and (iii) are capable of complementing or supplementing human intelligence (Simon, 1995). As Epstein said (2015), “Although the original vision for artificial intelligence was the simulation of (implicitly human) intelligence, research has gradually shifted to autonomous systems that compete with people”. Artificial neural networks, machine learning, genetic algorithms, fuzzy logic, and statistical analysis form the basis of most applications under the label of “AI”.The role of AI and how it is transforming companies are not well studied (Kulkov, 2021). Despite its great potential for solving problems, there are still issues involved in its practical uses (Borges et al., 2021). Overpraised and highly criticized, AI died at least four times in five decades because of wild claims made by people and research about AI. Instead, we focus here on the best machine intelligence one can construct without regard to what people can do (Epstein, 2015), given that advances in AI research have mainly been in isolated silos (Loureiro et al., 2021).Over the past few decades, the use of AI in diverse applications has increased substantially across different sectors and industries (Borges et al., 2021). Global spending on AI was expected to reach around US$ 98 billion in 2023 (Collins et al., 2021). Nevertheless, AI adoption in the construction industry has been moving at a slow pace (Akinosho et al., 2020), with research on AI in this sector mainly confined to developing software models for a specific subset of construction works. For this they have been using knowledge-based expert systems that have failed to gain wide acceptance on account of their inherent deficiencies.Sinesilassie et al. (2019) stated that, “A construction project is considered as successful when it is completed in time, without cost overruns, and within the specified quality parameters”. So-called “success factors” are interconnected performance factors that contribute to project success, as determined by the project management system that provides the tools to coordinate the technologies and people needed to complete a project to maximise chances of project success (Olugboyega et al., 2020). They form the basis for organizations to achieve success on projects (Nguyen et. al., 2020).Though extensive research has explored the role of AI in software projects, the role of artificial intelligence as a critical success factor for construction projects has not been explored in project management literature. This omission spurs the current work that aims to identify whether AI is becoming a potential critical success factor for construction project success, that is, used in construction projects to increase project performance and efficiency. Thus, in this paper we address the following research question: Can AI help complete construction projects within budget, on-schedule, and according to specifications thereby increasing the chances of project success'The construction industry lags behind many other industries in implementing AI solutions and remains severely under-digitized. AI may help in developing collaborative business models that can alter the current business environment, thereby improving performance and efficiency in the construction industry across the value chain from production of building materials to design, planning, execution, and maintenance (Akinosho et al., 2020). The huge benefits that can be obtained from applying AI in construction projects, therefore, necessitates understanding its role as a success factor for construction project success.Very few studies have taken a practitioner’s viewpoint that could provide valuable insights to construction project professionals in their daily activities (Townsend and Gershon, 2020). This study explores the perceptions of senior project practitioners about AI’s role as a success factor in construction projects. To the best of our knowledge this is the first study in project management literature that identifies this gap and attempts to fill it. The rest of the paper is structured as follows: The next section provides a literature review. Following that, the research approach and results constitute the next two sections. The next two sections then contain discussion and conclusions, including limitations of the research and directions for future research.Literature ReviewArtificial IntelligenceThe roots of AI can be traced back to the seminal work of Vannevar Bush who proposed a system called memex, a machine proposed to be an enlarged intimate supplement to a person’s memory (Bush, 2021), and Alan Turing (1950) who gave the idea of thinking machines that can imitate human beings. The term “artificial intelligence” was first used by John McCarthy in his Dartmouth Summer Research Project proposal in 1955 (McCarthy et al., 2006; Epstein, 2015). Early systems like ELIZA and General Problem Solver were developed in the 1960s based on the assumption that human intelligence can be formalized (Haenlein & Kaplan, 2019). Since then, ...
      PubDate: Sun, 27 Mar 2022 22:04:32 +000
      DOI: 10.1016/j.jobe.2020.101827 2.5839
  • Coping with the Double-Edged Sword of Data Sharing in Ecosystems

    • Authors: TIM Review
      Abstract: I never guess. It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.Sir Arthur Conan Doyle,Author of Sherlock Holmes StoriesIntroductionDigital technology and artificial intelligence are fundamentally changing the rules of business competition in markets from an external perspective, as well as the processes of value creation from an internal perspective (Brynjolfsson & McAffee, 2014; Iansiti & Lakhani, 2020). Especially “big data” and “big data analytics” (BDA) create new possibilities for strengthening companies’ efficiency and productivity (Aaser et al., 2020; New Vantage Partners, 2020), or for fostering innovativeness and growth options (Aaser et al., 2020; Mariani & Fosso Wamba, 2020; New Vantage Partners, 2020) by enabling new products, processes, business models, or services (Lim et al., 2018; Auh et al., 2021). Big data is seen as a promising resource that has a positive effect on business or societal value (Aaser et al., 2020), competitive advantage, and company performance (Fosso Wamba et al., 2017; Auh et al., 2021). While the amount of data that is available for firms explodes (Davenport & Bean, 2018), many organizations are still struggling to compete regarding data (Akter et al., 2016; Vidgen, 2017; Urbinati et al., 2019). A recent New Vantage Partners study reported that there has been little to no success for companies over the past years to become data-driven (New Vantage Partners, 2020), and the majority of firms (61%) to date have been unable to turn insights from data into a competitive advantage (Jiang et al., 2021). The gap between leaders and laggards in adopting BDA is growing within and between industries (Diaz et al., 2018; Jiang et al., 2021).The literature identifies a variety of reasons that hinder firms from turning data into value. Firstly, networking and data sharing are prerequisites for value generating data applications in business ecosystems (Cui et al., 2020). However, sharing data is a double-edged sword because, on the one hand, the data’s value increases by sharing it (Lim et al., 2018; Thuermer, 2019) or by gathering and curating the data on sharing platforms (Parra-Moyano et al., 2021). On the other hand, organizations risk losing their source of value and competitive advantage to stakeholders because they run the risk of dependency or exploitation, especially in the longer run. Consequently, these firms are trapped in a data sharing dilemma (Kraemer et al., 2019). It can be concluded that to capitalize on data sharing, firms must first understand the mechanisms of data sharing that include, first, which data they can freely share with their (external) stakeholders, second, which data they need to protect, and, third, what tools and agreements help protect the data without compromising the value that is generated by sharing the data (IMDA & PDPC, 2019).Secondly, the specific characteristics of data as a resource prove to be a hurdle for turning data into value, because raw data alone are insufficient for the generation of value from it (Gupta & George, 2016; Bumblauskas et al., 2017). Data are an intangible good (IMDA & PDPC, 2019) that is non-exclusive in use (Parra-Moyano et al., 2020). Anyone, or any firm that has access to the data can use it, which makes raw data inadequate for generating a competitive advantage (Parra-Moyano et al., 2020). For capitalizing on data, firms must clean the data, integrate, aggregate, and analyze it in a data analytics process (Jagadish et al., 2014). By doing so (raw) data must first be turned into actionable knowledge (Argyris, 1995), a process that requires both interpretation and integration by humans (Bumblauskas et al., 2017).Based on an extant review of the literature on data analytics, this conceptual contribution aims at discussing how firms can constructively craft strategies for dealing with the double-edged sword of sharing data in a digital ecosystem. The paper introduces a comprehensive data sharing strategy framework that helps in deciding which company data can be easily shared with a firm’s stakeholders without losing possible competitive advantages that can be generated from the data. The framework combines two distinct categorizations of data and relates the data categories to a company’s competitive advantage by employing a resource-based view (RBV). Firstly, the framework is grounded in the various stages of the data analytics process (Jagadish et al., 2014). Secondly, it distinguishes between operative, strategic and monetizable data, a new categorization introduced by this paper. Based on the categories of data a company intends to share, the paper recommends five distinct strategies for sharing data that mitigates the risks of losing a company’s advantage.The second section of the paper presents a summary of the ongoing discussion on big data in the management literature. In the third section, the paper reviews how data and data categories are linked to resources, capabilities, and competitive advantage from a RBV perspective. Also, the contribution introduces a data categorization that is based on the data’s strategic value, operative, strategic and monetizable. In section four the paper introduces a data sharing strategy framework, that combines these data categories with the stages in the BDA process and it recommends five distinct strategies for sharing data in an ecosystem. Finally, a discussion on how firms ...
      PubDate: Sun, 27 Mar 2022 22:00:07 +000
      DOI: 10.1017/dap.2020.1 stallman, r. 2007.
  • Editorial: Insights (11/12, 2021)

    • Authors: TIM Review
      Abstract: Welcome to the 11/12 issue of the Technology Innovation Management Review. This issue consists of a mixture of themes structured under our usual “Insights” title.The issue starts with Petra Kugler and Thomas Plank’s article, “Coping with the Double-Edged Sword of Data Sharing in Ecosystems”. In an investigation of the changing rules of business due to the emergence of digital technology and artificial intelligence, they introduce a data sharing strategy framework based on a literature review of texts about data analytics.  The framework aims to help companies decide the kinds of data to share in a digital ecosystem, as well as what should be kept private to help companies maintain their comparative advantage. The paper explores the rules and regulations required for dealing with various types of operative, strategic and monetizable data. The intended audience includes both practitioners and scientists, who may benefit from the data framework to mitigate the risks of losing competitive advantage in digital ecosystems, or to improve usage of theoretical concepts related to data such as capabilities and resources.The second paper by Virender Kumar, Amrendra Pandey, and Rahul Singh involves practitioner perspectives in asking, “Can Artificial Intelligence be a Critical Success Factor of Construction Projects'” To answer the title’s research question, the authors conducted semi-structured interviews and analyzed the response content. The interviewees include experienced project managers from the global community with expertise in project management working on large construction projects. Results of the research include a distinction highlighted by senior project managers in perceiving artificial intelligence (AI) as different from information technology and advanced project management software. Some of the drawbacks of AI were identified as its lack of soft skills, having interpretive intelligence unlike human beings, and weak human relationship capabilities to address the ways people manage projects.In the third paper, Mika Westerlund, Ishdeep Singh, Mervi Rajahonka, and Seppo Leminen explore “Technology Project Summaries as a Predictor of Crowdfunding Success”. This paper looks at the recent emergence of crowdfunding as a way for technology entrepreneurs to raise funds for projects, products, and business ideas. Through an analysis of Kickstarter fundraising campaigns, the authors seek to predict what distinguishes projects that reach their fundraising goals from those that fail to do so. With the help of topic modelling on a data set of over 21,000 Kickstarter technology projects, they investigate if short-text project summaries may provide insights to help predict fundraising success or failure on crowdfunding platforms. Their results show that the displayed summaries of technology projects that successfully raise funds from backers use more trendy topics, offer wording that clearly reflects their novelty, and focus on solving a social problem.The next paper by Inka Lappalainen and Maija Federley is titled “The Role of Digital Platforms in Resident-Centric Housing Concepts”. The authors investigate the designs, as well as value creation and capture of platform ecosystems in housing markets using service-dominant logic. They focus on four holistic pilot housing ecosystems in Finland that are designed to combine the physical environment of residents with a digital platform. The novelty of this study builds on a holistic understanding of value co-creation in housing, enabled by digital platforms at the ecosystem level. The paper concludes that digital platforms can enable new value creation opportunities in resident-centric housing concepts through a novel “housing as a service” platform approach. The audience is intended as both practitioners and researchers who are exploring opportunities of platform economies.In the fifth paper, Shweta Shirolkar and Kanchan Patil present “Antecedents, Decisions, and Outcomes of a Sharing Economy”, following a systematic literature review. Their analysis covers research and papers published between 2008 and 2020, involving both developed and emerging countries. The literature review includes 93 articles gathered with an aim to understand emerging consumer behavior that involves collaborative consumption aided by technological innovation. The authors show that the impacts of sharing economies (SEs) on incumbents have increased competition between traditional market players due to the emergence of new platforms with sharing-oriented business model innovation. The research findings indicate that various value categories, including social value, hedonic value, economic value, environmental value, and entrepreneurial opportunity serve as major antecedents to participate in SEs.For future issues, we invite general submissions of articles on technology entrepreneurship, innovation management, and other topics relevant to launching and scaling technology companies, and for solving practical business problems in emerging domains such as artificial intelligence and blockchain applications in business. Potential contributors could also consult the TIM Review topic model ( to examine the dominant publication themes so far, which might help with ideas for valuable future contributions. Please contact us with potential article ideas and submissions, or proposals for special issues....
      PubDate: Sun, 27 Mar 2022 21:54:16 +000
  • A Review of Living Lab Research Development and Methods for User

    • Authors: TIM Review
      Abstract: Innovation is all about people. Innovation thrives when the population is diverse, accepting, and willing to cooperate.Vivek Wadhwa
      Author and EntrepreneurIntroductionThe notion of “living labs” has received growing attention in the realm of innovation management. Acting as one form of open innovation that brings external players into the innovation process (Chesbrough et al., 2006), a living lab provides a real-life milieu that stimulates innovative collaboration among people for solving challenges (Westerlund & Leminen, 2011; Almirall et al., 2012). The user-centric approach encourages active participation and integrates users’ knowledge into the value creation process, thereby magnifying innovative competence (Eriksson et al., 2006; Leminen et al., 2012).After over two decades of development, “living lab” is now a term associated with diverse meanings and research spread into multiple disciplines (Leminen & Westerlund, 2019). Earlier studies have touched upon numerous aspects such as definitions (Leminen et al., 2012), key principles and components (Bergvall-Kareborn & Stahlbrost, 2009; Westerlund et al., 2018a), users’ roles (Leminen et al., 2015a), and users’ motivation (Bergvall-Kareborn & Stahlbrost, 2009). One of the first living lab literature reviews from Følstad (2008)covered its theoretical foundations, processes, and methods in the Information Communication Technology (ICT) domain, highlighting contextual research and user co-creation as living labs’ unique attributes. Later scholars contributed in drawing a broader picture. For instance, a trend analysis of research topics in living labs (Westerlund et al., 2018b), with a longitudinal review of the living lab movement showed early scattered activities, then the establishment of cross-regional and professional living labs (Leminen & Westerlund, 2019). Some scholars used big data techniques, like bibliometric analysis, or similar ones for mapping a living lab’s landscape, thus adding a higher level of understanding such as its intellectual structure (McLoughlin et al., 2018; Greve et al., 2020).Despite its rapid growth, research on this fairly young phenomenon remains dispersed (Greve et al., 2020). Studies are sparse in areas, applications, publication venues, etc., making it hard to grasp the latest situation. When it comes to user involvement, one unique characteristic of living labs (Bergvall-Kareborn & Stahlbrost, 2009)follows from having inadequate information about how living labs actually involve users (Puerari et al., 2018). Methods and details reflecting their user-centric character remain unclear (Schuurman et al., 2015). Scholars have not yet reached a consensus about models or guidance involving living lab governance and value creation for stakeholders (Westerlund et al., 2018a), which hinders the integration of studies at large. Measuring the effectiveness of user-centric approaches is another underexplored area (Ballon et al., 2018). Meanwhile, wide-ranging practices and methodologies get labelled as "living labs” (Leminen, 2015), making living lab methods and approaches sometimes into just vague words. Here arises the need for more practice-oriented living lab research, both for scholars and practitioners (Westerlund et al., 2018b). On that account, we decided to shed more light on the living lab phenomenon, and aim in this paper to answer the following questions: i. How has living lab research advanced over time, and what are the current trends' ii. What are the methods and tools used by living labs for user involvement'              We employ a two-step approach in this literature review. The first section presents a bibliometric analysis of 535 living lab studies from 1991 to 2021 on the topic of developing a consolidated understanding of its research development in terms of publication venues, contributing authors and their collaboration patterns, structures of research domains, and trends. By dividing the twenty years into two periods, we contrast and observe the change and shift of development patterns over time. In the second section, we contribute a further review of 42 empirical papers by identifying eight thematic domains of methods for user involvement in living labs from various aspects, including the format, technique, design approach, and overarching rules across different stages of the innovation process. We also summarize the tools for user involvement in these studies, in both physical and digital forms. Based on these findings and analyses, we discuss the implications and conclude with suggestions for future exploration.Living Lab Research DevelopmentThe global “living lab movement”, especially boosted by European living labs since the establishment of the European Network of Living labs (ENoLL) in 2006, has been drawing attention from researchers and policymakers over the last few years (Hossain et al., 2019; Leminen & Westerlund, 2019). Living lab meanings are manifold: a user-centric methodology (Eriksson et al., 2005), an approach for empowering users (Bergvall-Kareborn & Stahlbrost, 2009), an intermediary for collaboration (Almirall & Wareham, 2011), both the methodology and its structural instrument/agent for user collaboration activities (Almirall et al., 2012), an innovation system/approach/organization that monitors a living social experiment, or just the European living lab movem...
      PubDate: Sun, 30 Jan 2022 21:35:14 +000
  • Urban Living Labs and Transformative Changes: A qualitative study of the
           triadic relationship between financing, stakeholder roles, and the
           outcomes of Urban Living Labs in terms of impact creation in the city of
           Groningen, the Netherlands

    • Authors: TIM Review
      Abstract: He who does not trust enough, will not be trusted.Lao Tzu
      Ancient Chinese philosopher and writer1 IntroductionUrban living labs (ULLs) have arisen in cities as a response to a pressing challenge (Marvin et al., 2018): How can cities provide economic prosperity and social cohesion while achieving environmental sustainability' In this perspective, the core idea of ULLs is that urban sites can provide a learning arena within which the co-creation of innovation can be pursued between research organisations, public institutions, private sectors, and community actors (Liedtke et al., 2012). Not only in practise, but also in academic spheres, the concept of ULLs has increasingly gained interest in recent years (Schuurman, 2015; Hossain et al., 2019).Yet, despite the growth of ULLs and their experimentation, their nature and purpose as an empirical phenomenon is still not fully understood (Bulkeley et al., 2016). This is partly because the acceleration and normalisation of ULLs in practise has proceeded much more rapidly than the development of evidence and theoretical understanding about them (Bulkeley et al., 2016; Marvin et al., 2018). As such, international comparison and systematic learning is lacking on how ULL impacts can be scaled up to achieve transformative changes (Marvin et al., 2018), and how they can effectively facilitate urban sustainability transitions (Evans & Karvonen, 2013; Nevens et al., 2013). Such transitions are about changes in markets, policy, culture, technologies, and infrastructure, as well as in human behaviours and practises (Bulkeley et al., 2010; Frantzeskaki & Loorbach, 2010; Schaffers & Turkama, 2012; Voytenko et al., 2016).A key point therein is to examine the role of (urban) experiments to govern these transitions, and in doing urban innovation and governance (Marvin et al., 2018) to gradually transform stable regimes (Kemp et al, 1998; Schot & Geels, 2008). Existing regimes or systems seem to be difficult to pry off because they are stabilised by processes that create path dependencies (Grin et al., 2010; Loorbach & Rotmans, 2010; Neef et al., 2017). ULLs are one way to effect change (Schaffers & Turkama, 2012; Marvin et al., 2018), because they are similar in approach to “transition management” (Loorbach & Rotmans, 2010), and centre on the use of experiments, including less directed processes in which innovation and ideas are demonstrated, tested, and experienced for gain (Kemp et al., 1998; Bulkeley & Castán Broto, 2012). The degree to which these experiments lead to regime transitions seem to depend on growing social networks, innovations, and learnings that they establish (Brown & Vergragt, 2008). Existing research, however, mainly focusses on the aims and workings of ULLs instead of critically reviewing their implications (Bulkeley et al., 2016), their essence (Hossain et al., 2019), or to what extent they shape new governance modes (Marvin et al., 2018). Some challenges in ULLs, therefore, link with temporality and unpredictable outcomes (Hossain et al., 2019), such as financial sustainability (Gualandi & Romme, 2019), scalability, diffusion, and impact (Puerari et al., 2018; von Wirth et al., 2018), and the redistribution of agency and risks (Loorbach & Rotmans, 2010; Smith & Raven, 2012; Burch et al., 2018).
      This study addresses this research gap by focussing on how the relationship between funding, stakeholder roles, and process outcomes in ULLs can contribute to transformative changes. The main research question is: How does the trinity of funding options, stakeholder roles, and outcomes in ULLs influence their impact creation for transformative changes in cities' Tensions between these aspects were observed by Hodson and colleagues (2018) in the UK, which are still present in today’s ULL practises (Scholl & de Kraker, 2021).The paper is structured as follows. First, it elaborates on current literature about ULLs and the trinity under study to explore and identify current approaches and theories. Second, it explains and justifies the methodology chosen in the literature review and comparative case study in the context of the city of Groningen. Then, it provides the results of the empirical study focussed on funding options, stakeholder roles, outcomes created, and impact. Lastly, the paper presents the importance of trust building in ULLs to overcome the particular challenge under study, highlighting its theoretical and practical implications, as well as limitations and recommendations for further research.2.0 Literature Review2.1 Origin and positioning of urban living labsAlthough the origin of the living lab movement can be traced back to the 1960s, and later, the founding of the European Network of Living Labs in 2006 (Hossain et al., 2019), the emergence of ULLs more generally started following the 2008 Global Economic Crisis. Since then, cities have struggled to find solutions to challenges faced via three sets of issues: 1) there is no singular pathway towards urban sustainability (De Jong et al., 2015), 2) interest increased in the potential of experimentation in place-based contexts to overcome rigidity in existing socio-technical systems based on private contexts (Chesbrough, 2006; Almirall & Wareham, 2011), and 3) various stakeholders, like research and technology institutions, started to see urban environments as places to support local communities, as well as grassroots initiatives that align with national innovat...
      PubDate: Sun, 30 Jan 2022 21:12:32 +000
  • Rural Living Labs: Inclusive Digital Transformation in the Countryside

    • Authors: TIM Review
      Abstract: Don't walk in front of me, I may not follow. Don't walk behind me, I may not lead. Walk beside me and be my friend.Albert CamusIntroductionDigital transformation (DT) nowadays is changing the dynamics of how societies are shaped (Agarwal, 2020). DT can be understood as the “changes that [the] digital technology causes or influences in all aspects of human life” (Stolterman & Fors, 2004). These changes are visible in different levels and scales, from individual to societal levels, and from more modernized urban areas, like smart cities, to less digitalized rural areas, in which DT occurs in an uncontrolled real-life context, and where people are involved in their everyday use context (Bockshecker et al., 2018; Spagnoli et al., 2019). Since most studies of the societal effects of digitalization and DT have been carried out in urban areas, there is a dearth of research on the effects of digitalization in rural areas (Salemink et al., 2017; Rotz et al., 2019; Runardotter et al., 2020). Following a participatory design approach, we believe that people have the moral and ethical right to be a part of DT processes (Bansler, 1989; Bjerknes & Bratteteig, 1995), also in rural areas, since digitalization of society can bring enormous (positive and negative) impact in peoples’ lives.  In this paper, we focus on DT and innovation pilots carried out in rural areas, aiming to manage the challenges that emerge in these contexts. The study is supported by a living lab (LL) approach (Bagalkot, 2009; Schaffers et al., 2009; Schuurman, 2015)that has been introduced and proposed as an inclusive and sustainable approach involving various stakeholders, focusing on how individuals in their role as citizens, inhabitants, end-users, etc., are engaged throughout the DT process in their real-life settings (Ståhlbröst, 2008; Bergvall-Kåreborn et al., 2009). Accordingly, LLs can be seen as an approach for facilitating innovation processes, as they allow one to simultaneously focus on individuals, technologies, tasks, and structures, and on the interactions between various stakeholders (Schaffers et al., 2009). To date, most research attention has been paid to urban areas as the context for LL activities, the so-called Urban LL (or ULL) (Steen and Bueren, 2017; Chronéer et al., 2019), for example, the initial list of key components of traditional LLs were further revised and modified for the context of Urban LLs by Chronéer and colleagues (2019).Nevertheless, few studies have examined the possibilities and potentials of LL activities in relation to rural areas. Most have investigated, for example, one specific dimension such as business models for Rural LLs (RLLs) (Schaffers et al., 2009), co-creation activities and actions in rural context (Bagalkot, 2009), as well as nature-based solutions and sustainability in rural contexts (Zavratnik et al., 2019; Lupp et al., 2021). None that we are aware of have investigated the overall construction of RLLs and their key components. In addition, most studies of LL activities in rural areas have focussed on the context of innovation (Bagalkot, 2009; Salemink et al., 2017; Rotz et al., 2019)in relation to traditional rural activities such as farming and agriculture. Following that, little attention has been paid about how to design RLL activities, as well as to what constitutes a RLL. This is important for boosting peoples’ understanding of LL innovation activities in rural areas, and for building a solid research foundation upon which innovation processes can be built.One important aspect in relation to the character and philosophy of RLLs compared with ULLs is related to the way they can be interpreted. ULLs are often considered as a context that supports and boosts the development of smart city innovations (Chronéer et al., 2019). In the same vein, RLLs can be seen as an approach that facilitates digital innovation in rural areas. In addition, ICT and digital innovations in ULLs are relatively mature technology (Salemink et al., 2017). Meanwhile, in RLLs, digital innovations and ICT infrastructure are less mature, at the so-called fuzzy front-end of innovation (Koen et al., 2001; Takey & Carvalho, 2016).The aim of this paper is to explore how the LL approach should be designed to support DT pilots distributed in rural areas, while including a diversity of stakeholders. Our point of departure is the five “traditional” key components of LLs, namely, ICT and infrastructure, management, partners and users, research and approach (Bergvall-Kåreborn et al., 2009; Ståhlbröst, 2012). By adopting a “design science” research methodology (Peffers et al., 2007; Gregor & Hevner, 2013), we identify and assess what distinguishes ULL and RLL approaches, and present a framework for RLL DT pilots that contributes to the overall body of research. We also propose a definition for RLL, as well as highlight the key differences and similarities between RLLs and traditional ULLs.Theoretical Foundation: LLs, Urban LLs and Rural LLsThe need for new approaches to engage various stakeholders and users (rural residents) in the DT process is growing (Evans & Karvonen, 2011). Considering the various consequences of digitalization on peoples’ everyday lives (Yoo, 2010; Bockshecker et al., 2018; Baskerville et al., 2019), several reasons exist, such as empowerment and democracy (Boston College et al., 2014)for the acceptance and adoption of digital technologies (Moore, 2019; Pady...
      PubDate: Sun, 30 Jan 2022 20:57:13 +000
  • Living Labs for Public Sector Innovation: insights from a European case

    • Authors: TIM Review
      Abstract: Alone, we go faster. Together, we go further.Motto of the Living Lab of Foch Hospital,Suresnes (Paris), FranceIntroduction The acknowledged move from traditional public administration (TPA), over to new public management (NPM), then to the current shift towards new public governance (NPG) has spurred an increased awareness on the role of external stakeholders in developing public services, and hence the way public sector innovation takes place (Hartley, 2005; Torfing, 2019). Public sector innovation is now more dependent on joint processes based on cross-sectorial collaboration, which implies that public innovation has become complex and dynamic, since citizens multifaceted needs require several actors to coordinate their efforts. Innovation therefore now takes place in a complex multi-actor context of politicians, policymakers, public managers, employees, users, citizens, civil actors, and private firms. A platform and methodology for such innovation processes are living labs (Leminen et al., 2012; Ruijer & Meijer, 2020). Living labs are defined as collaborative environments for experimentation in and of real-life contexts (Gascó, 2017). Living labs are still, however, somewhat underexplored in the context of public sector innovation, herein how they are organized and with what they contribute (Schuurman & Tõnurist, 2017; Hansen & Fuglsang, 2020). Therefore, to better understand and learn from existing living labs, the main aim of this article is to investigate and analyze how living labs spur and enact processes of public sector innovation in a European context, and to discuss the potentials and pitfalls of living labs as a way of doing public sector innovation. This leads to the following two research questions: a) How are living labs applied to engage actors in public sector innovation processes', and b) What promises do such innovation processes hold'The research is based on a mixed methods design, encompassing 21 case studies of living labs across nine EU countries (Fuglsang & Hansen, 2021; 2022) and a thorough survey of co-creation methods in the public sector, distributed to public managers in six EU countries (Arundel & Es-Sadki, 2021). The paper extends previous research on the societal framing of living labs (Ruijer & Meijer 2020; Fuglsang & Hansen, 2022), and involving methods used in living labs, by presenting experiences from cases of how living labs can organize public sector innovation processes in terms of various scenarios.The article is structured as follows: first, a short overview of the theory base is presented, followed by an introduction to the methodology applied. Subsequently, key analytical results are accounted for and discussed. Finally, concluding remarks are given, and future research avenues proposed.Theory BasePublic sector innovationInnovation as concept may take slightly different meanings across various sectors and research traditions. Yet, most of the literature maintains that innovation encompasses the two intertwined processes of creating something new, and implementing this new creation in practice (Torfing 2019; Fuglsang & Hansen, 2022). The processes that lead to innovation are summarized in terms of, for example, structures and stages of innovation, specific drivers that lead to innovation, such as entrepreneurs or R&D, specific procedures such as design processes, and certain innovation roles. While much emphasis is on the structures and stages of innovation processes, some authors have also conceptualized innovation as a practice-based inherently incremental activity (Fuglsang, 2010), that is, as integrated with work and organizational routines. The practice-based approach is especially evident in innovation processes taking place within everyday work in public service delivery leading to the creation of new knowledge and new behaviors (Fuglsang, 2021). The acknowledgement of contextual factors has led to the argument that it is important to develop relevant and restricted concepts for public sector innovation (Gault, 2018). Windrum (2008) proposed a useful distinction between six types of innovation found in the public sector: service innovation, service delivery innovation, administrative and organizational innovation, conceptual innovation, policy innovation, and systemic innovation. Hartley (2005) added governance innovation as a special feature of public sector innovation. Governance innovation refers to new forms of citizen engagement in innovation, and rhetoric innovation, which means new language and concepts in a service domain. Hartley also suggested that rather than speaking of types of innovation, such as radical and incremental, governance or rhetorical, it may be more correct to treat innovation, particularly complex innovations, as multidimensional processes since the different types are connected in practice (Hartley, 2005).Besides the focus on how and with what innovation contributes, innovation processes in a public sector context, especially in settings with a high degree of citizen-employee encounters, is based on the logic of open, co-creational and collaborative innovation (Hartley et al., 2013; Voorberg et al., 2015). Open innovation describes how the knowledge of citizens and other actors external to government organizations is included (Fuglsang, 2008). Resulting from this openness, the knowledge that is created can be heterogeneous in its nature and might also result in beneficial outcomes for the organization due ...
      PubDate: Sun, 30 Jan 2022 20:43:40 +000
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