Unlashing the next Wave of Business Models in the Internet of Things Era: New Directions for a Research Agenda based on a Systematic Literature Review

Pervasive digitization of products and services open additional avenues for the next wave of business model opportunities. Most of firms are aware of the monetization potentials that the Internet of Things has to offer, however, they still struggle to create a compelling value propositions. Despite the attention of both research and practice onto business models and the IoT, only few concepts and research endeavors regarding their intersections exist. This paper tends to unleash the specificity of the business models within the IoT technologies, and motivate new, ecosystem, perspective for upcoming research. Following a rigorous methodology for a comprehensive and systematic literature review, we develop five literature clusters related to the IoT-driven business model research, evaluate and analyze the papers within clusters, and finally identify the gaps and propose directions for future research

The integrity of digital technologies in the evolving characteristics of real-time enterprise architecture

Advancements in interactive and responsive enterprises involve real-time access to the information and capabilities of emerging technologies. Digital technologies (DTs) are emerging technologies that provide end-to-end business processes (BPs), engage a diversified set of real-time enterprise (RTE) participants, and institutes interactive DT services. This thesis offers a selection of the author’s work over the last decade that addresses the real-time access to changing characteristics of information and integration of DTs. They are critical for RTEs to run a competitive business and respond to a dynamic marketplace. The primary contributions of this work are listed below. • Performed an intense investigation to illustrate the challenges of the RTE during the advancement of DTs and corresponding business operations. • Constituted a practical approach to continuously evolve the RTEs and measure the impact of DTs by developing, instrumenting, and inferring the standardized RTE architecture and DTs. • Established the RTE operational governance framework and instituted it to provide structure, oversight responsibilities, features, and interdependencies of business operations. • Formulated the incremental risk (IR) modeling framework to identify and correlate the evolving risks of the RTEs during the deployment of DT services. • DT service classifications scheme is derived based on BPs, BP activities, DT’s paradigms, RTE processes, and RTE policies. • Identified and assessed the evaluation paradigms of the RTEs to measure the progress of the RTE architecture based on the DT service classifications. The starting point was the author’s experience with evolving aspects of DTs that are disrupting industries and consequently impacting the sustainability of the RTE. The initial publications emphasized innovative characteristics of DTs and lack of standardization, indicating the impact and adaptation of DTs are questionable for the RTEs. The publications are focused on developing different elements of RTE architecture. Each published work concerns the creation of an RTE architecture framework fit to the purpose of business operations in association with the DT services and associated capabilities. The RTE operational governance framework and incremental risk methodology presented in subsequent publications ensure the continuous evolution of RTE in advancements of DTs. Eventually, each publication presents the evaluation paradigms based on the identified scheme of DT service classification to measure the success of RTE architecture or corresponding elements of the RTE architecture

Reference Models for Digital Manufacturing Platforms

[EN] This paper presents an integrated reference model for digital manufacturing platforms, based on cutting edge reference models for the Industrial Internet of Things (IIoT) systems. Digital manufacturing platforms use IIoT systems in combination with other added-value services to support manufacturing processes at different levels (e.g., design, engineering, operations planning, and execution). Digital manufacturing platforms form complex multi-sided ecosystems, involving different stakeholders ranging from supply chain collaborators to Information Technology (IT) providers. This research analyses prominent reference models for IIoT systems to align the definitions they contain and determine to what extent they are complementary and applicable to digital manufacturing platforms. Based on this analysis, the Industrial Internet Integrated Reference Model (I3RM) for digital manufacturing platforms is presented, together with general recommendations that can be applied to the architectural definition of any digital manufacturing platform.This work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 825631 and from the Operational Program of the European Regional Development Fund (ERDF) of the Valencian Community 2014-2020 IDIFEDER/2018/025.Fraile Gil, F.; Sanchis, R.; Poler, R.; Ortiz Bas, Á. (2019). Reference Models for Digital Manufacturing Platforms. Applied Sciences. 9(20):1-25. https://doi.org/10.3390/app9204433S125920Pedone, G., & Mezgár, I. (2018). Model similarity evidence and interoperability affinity in cloud-ready Industry 4.0 technologies. Computers in Industry, 100, 278-286. doi:10.1016/j.compind.2018.05.003Mehrpouya, M., Dehghanghadikolaei, A., Fotovvati, B., Vosooghnia, A., Emamian, S. S., & Gisario, A. (2019). The Potential of Additive Manufacturing in the Smart Factory Industrial 4.0: A Review. Applied Sciences, 9(18), 3865. doi:10.3390/app9183865Tran, Park, Nguyen, & Hoang. (2019). Development of a Smart Cyber-Physical Manufacturing System in the Industry 4.0 Context. Applied Sciences, 9(16), 3325. doi:10.3390/app9163325Fernandez-Carames, T. M., & Fraga-Lamas, P. (2019). A Review on the Application of Blockchain to the Next Generation of Cybersecure Industry 4.0 Smart Factories. IEEE Access, 7, 45201-45218. doi:10.1109/access.2019.2908780Moghaddam, M., Cadavid, M. N., Kenley, C. R., & Deshmukh, A. V. (2018). Reference architectures for smart manufacturing: A critical review. Journal of Manufacturing Systems, 49, 215-225. doi:10.1016/j.jmsy.2018.10.006Sutherland, W., & Jarrahi, M. H. (2018). The sharing economy and digital platforms: A review and research agenda. International Journal of Information Management, 43, 328-341. doi:10.1016/j.ijinfomgt.2018.07.004Corradi, A., Foschini, L., Giannelli, C., Lazzarini, R., Stefanelli, C., Tortonesi, M., & Virgilli, G. (2019). Smart Appliances and RAMI 4.0: Management and Servitization of Ice Cream Machines. IEEE Transactions on Industrial Informatics, 15(2), 1007-1016. doi:10.1109/tii.2018.2867643Gerrikagoitia, J. K., Unamuno, G., Urkia, E., & Serna, A. (2019). Digital Manufacturing Platforms in the Industry 4.0 from Private and Public Perspectives. Applied Sciences, 9(14), 2934. doi:10.3390/app9142934Digital Manufacturing Platforms, Factories 4.0 and beyondhttps://www.effra.eu/digital-manufacturing-platformsZero Defect Manufacturing Platform Project 2019https://www.zdmp.eu/Zezulka, F., Marcon, P., Vesely, I., & Sajdl, O. (2016). Industry 4.0 – An Introduction in the phenomenon. IFAC-PapersOnLine, 49(25), 8-12. doi:10.1016/j.ifacol.2016.12.002Announcing the IoT Industrie 4.0 Reference Architecturehttps://www.ibm.com/cloud/blog/announcements/iot-industrie-40-reference-architectureVelásquez, N., Estevez, E., & Pesado, P. (2018). Cloud Computing, Big Data and the Industry 4.0 Reference Architectures. Journal of Computer Science and Technology, 18(03), e29. doi:10.24215/16666038.18.e29Pisching, M. A., Pessoa, M. A. O., Junqueira, F., dos Santos Filho, D. J., & Miyagi, P. E. (2018). An architecture based on RAMI 4.0 to discover equipment to process operations required by products. Computers & Industrial Engineering, 125, 574-591. doi:10.1016/j.cie.2017.12.029Calvin, T. (1983). Quality Control Techniques for «Zero Defects». IEEE Transactions on Components, Hybrids, and Manufacturing Technology, 6(3), 323-328. doi:10.1109/tchmt.1983.113617

Improving service scalability in IoT platform business

Abstract. This thesis aims to improve the scalability of several case companies’ business which offer their services through their own IoT platforms. The case companies are still in the early stages of their lifecycle, and their aim is to grow their businesses significantly in the future. Thus, enabling high scalability in service production is important for them. A literature review was conducted to find the most critical factors that affect scalability of services that are provided through an IoT platform. Interviews with open-ended questions were used to determine the current state of the case companies regarding the factors that were presented by the literature review. Based on the literature review and the current state analysis, two productization models were created including commercial and technical portfolios. Resource drivers were also included in the models. The created productization models for IoT service offerings are suggested to ease sales item management and to clarify the service offerings for both the provider and the buyer. Further, linking the resource drivers to the processes needed to offer the services illustrates the needed resources in different service production processes. The presented productized service models are one step that the case companies can take to improve their service scalability, but the models are not a solution to all scalability problems. However, similar models could be used in other companies that provide their service offerings through an IoT platform to improve their service scalability as well.Palvelutuotannon skaalautuvuuden parantaminen alustan kautta toimivissa yrityksissä. Tiivistelmä. Tämän opinnäytetyön tavoitteena on parantaa alustatalouden kautta palveluitaan tarjoavien case yritysten skaalautuvuutta. Case-yritykset ovat vielä elinkaarensa alkuvaiheessa ja niiden tavoitteena on kasvattaa liiketoimintaa merkittävästi tulevaisuudessa. Tämän johdosta korkean skaalautuvuuden mahdollistaminen yrityksien palvelutuotannossa on tärkeää. Kirjallisuuskatsauksessa pyritään löytämään merkittävimmät tekijät, jotka vaikuttavat skaalautuvuuteen alustatalouden kautta tehtävässä palveluntarjonnassa. Case yritysten nykytila analysoidaan avoimin kysymyksin suoritettavilla haastatteluilla, joilla pyritään selvittämään tekijät, joissa case yrityksillä olisi parantamisen varaa. Kirjallisuuskatsauksen ja yritysten nykytila-analyysin pohjalta luodaan kaksi tuotteistusmallia, joissa kaupallinen ja tekninen tuoteportfolio on eroteltu toisistaan, lisäksi resurssiajurit on kuvattu mukaan malleihin. Tuotteistusmalli helpottaa eri tuotenimikkeiden hallintaa ja lisää palvelun selkeyttä niin myyjän kuin ostajankin puolella, lisäksi resurssiajureiden ottaminen mukaan malliin havainnollistaa tarjoajayritykselle sen tarvitsemia resursseja eri palveluprosessin vaiheissa. Työn loppupäätelmänä luodut tuotteistusmallit toimivat yksinä toimenpiteinä, joidenka voidaan nähdä parantavan case-yrityksien skaalautuvuutta, mutta ne eivät ole ratkaisu kaikkiin skaalautuvuuden ongelmiin. Samankaltaisia malleja voitaisiin kuitenkin hyödyntää muissakin yrityksissä, jotka tarjoavat palveluitaan alustatalouden kautta toimialasta riippumatta

Influencing Factors of Smart Community Service Quality: Evidence from China

Smart community is an important constituent part of a smart city and an extension and deepening of the concept of the latter. When it comes to smart community, the digitalization upgrading of traditional community service is conducted via information technology, in an effort to improve the service experience of community residents and elevate their happiness index. From social functions, smart community also has the advantages in facilitating the smart transformation of cities, promoting the harmonious society construction, and improving governmental efficiency and image, among others. However, various problems persist in the construction and development process of a smart community, such as mismatching service contents and low service quality. To explore the influencing factors of smart community service quality, a total of 16 influencing factors were extracted from 5 dimensions: service object, service subject, government role, management system, and service content. The relationships among the influencing factors were analyzed via the decision-making trial and evaluation laboratory (DEMATEL)-interpretative structural modeling (ISM) composite model, and a multi-order explanation model was constructed for these influencing factors. Result shows that the legal guarantee is the root cause influencing the smart community service quality. Development standard, basic service, and expected service are deep influencing factors that play mediating roles. Middle-layer factors such as service and operating systems have a direct bearing on quality perception. The surface-layer factors directly decide residential assessment on the smart community service quality. This study has also manifested the feasibility of the integrated DEMATEL-ISM method in analyzing the action mechanism of influencing factors for smart community service quality, providing a new analytical idea and modeling method for the smart community service quality

Business models in healthcare

Healthcare industry is changing worldwide, due to an increase in demand, both qualitative and quantitative. Business models in healthcare are struggling to give an answer to such changes. Different societies manage their healthcare systems according to their politics and possibilities, this way different business models fit each society needs. Technology is promoting an evolution in business models in healthcare, increasing efficiency and effectiveness. Among the most common technologies and business models’ enablers applied in healthcare field are Digitalization, Big Data, Internet-of-things and Project financing. This work intends to understand how these new technologies are impacting healthcare sector in Portugal. How they are being applied, which are the main barriers their implementation is facing and what is expected to improve by incorporating them in healthcare industry. Data was collected through semi-structured interviews performed to an expert panel composed by C-level managers from public and private healthcare organizations. The results achieved portrayed a strong correlation with what is found in literature. Showing that most organizations are applying these resources, understanding that they may bring better outcomes for all stakeholders in healthcare field.A indústria da saúde está a mudar em todo o mundo, devido ao aumento da procura, tanto qualitativa quanto quantitativa. Os modelos de negócios na área da saúde estão a lutar para dar uma resposta a essa mudança. Diferentes sociedades gerem os seus sistemas de saúde de acordo com as suas políticas e possibilidades, desta forma diferentes modelos de negócios adaptam-se às necessidades de cada sociedade. A tecnologia tem vindo a promover uma evolução nos modelos de negócios na área da saúde, aumentando a sua eficiência e eficácia. Entre as tecnologias e “promotores” dos modelos de negócios mais comummente aplicados na área da saúde estão os programas de Digitalização, “Big Data”, Internet das Coisas e Financiamento de projetos Este trabalho pretende perceber como estas novas tecnologias estão a impactar o setor da saúde em Portugal. Como estão a ser aplicadas, quais são as principais barreiras que a sua implementação enfrenta e o que se espera melhorar ao incorporá-las neste setor. Os dados foram adquiridos por meio de entrevistas semiestruturadas realizadas a um painel de especialistas composto por gerentes de nível C de organizações de saúde públicas e privadas. Os resultados obtidos retrataram uma forte correlação com o que se encontra descrito na literatura. Mostrando que a maioria das organizações aplica estes recursos, entendendo que os mesmos podem trazer melhores resultados para todos os “stakeholders” da área da saúde