20-PM1-01: ST7.5 - Emerging Landscapes. New Skills, New Technologies and New Organizational Challenges in the 4.0 Age
The concept of digitizing everything is already a reality. Automation, artificial intelligence, IoT, machine learning and other advanced technologies are capturing and analyzing a wealth of data that gives us sizable amount and types of information to work from. One of the major challenges we face is to change the way we think, train and work with data in order to create value through advanced technologies. The 4.0 revolution is occurring where countless elements comprising industrial systems and services are being interfaced with internet communication technologies to form the smart future factories and manufacturing organizations. 4.0 age and its key technologies (cloud-based design, Mobile Devices, Big Data, smart manufacturing systems, the Internet of Things (IoT), the Industrial Internet of Things (IIoT), 3D printing) are currently being driven by disruptive innovation that promises to bring countless new value creation opportunities across all major market sectors. Its vision of ecosystems of smart factories with intelligent and autonomous shop-floor entities is inherently decentralized. This, in turn, entails new complexities within platforms, metaplatforms and socio-technological ecosystems, constantly creating new challenges and opportunities (i.e. responding to customer demands for tailored products and/or creating new products for new customers) for technology enablers, users and users/enablers. The 4.0 age seems to dictate the end of consolidated models (mental, educational, managerial, organizational, cultural, social etc.) and, at the same time, it asks for new “lenses” and interpretative paradigms enabling old and new actors to succeed in such magmatic landscape. Despite the significant hype around the topic, there is extant research regarding the exact consequences for people, companies and institutions involved. For example, millions of workplaces are being vaporized in a rhythm never seen before, while others are emerging towards becoming of billion-dollar companies (i.e. unicorn companies), which are managed by a reduced number of highly skilled professionals. The 4.0 landscapes are made of diverse technologies spread across many disciplines with many different types of subject matter experts. However, there are few standards and processes designed to assist each entity to speak a common language and think systemically. Academics and practitioners are trying to deeply comprehend the consequences of the 4.0 age revolution for employees, businesses, technology users/enablers and the society at large. This is particularly challenging in the newly emerging socio-technological context where organizational boundaries and the distinction between services and manufacturing are getting fuzzier than ever. Under this perspective, atoms and bits interpenetrate more and more like a fluid and virtuosic dance. These key issues will be debated in the papers as forerunner ideas for future research on this emerging landscape.
The track aims to critically analyze the state-of-the-art about the industrial 4.0 context, its opportunities, dark side and challenges in terms of:
• new competitive rules;
• new skills, new jobs, new educational programs;
• new labor organization and new organizational models;
• new technologies;
• new paradigms for the value co-creation;
• new models of interactions among human beings, machines and virtual world.
Design of consistent scenarios for IT technologies in system creation based on multiple domain matrices
1Fraunhofer Institute for Industrial Engineering IAO; 2Institute for Human Factors and Technology Management IAT, University of Stuttgart; 3Institute for Control Engineering of Machine Tools and Manufacturing Units ISW, University of Stuttgart
Producing industries develop more complex mechatronic products including a variety of engineering disciplines . Companies transform towards the advanced systems engineering (ASE) , , where new process, visualization and information technologies extend the human cognition. However, especially SMEs struggle with the continuous digital thread, which is a precondition for ASE.
Recent research on the metamorphosis of the intelligent and interlinked factory especially in small and medium enterprises (SMEs) proposes an approach of multiple-domain-matrices . We extend this approach by methodologies and algorithms used for the development of integrated strategies based on the analysis of consistent future scenarios , . Some of these extensions include system theoretical views on cross-impacts , weighting algorithms like the Page Rank , multiple domain matrices  or the logic of cause-and-effect matrices.
Literature so far does not cover the IT and processual landscape design with the special challenges of interdisciplinary, advanced systems engineering, which includes especially processes and tools in the early phases such as product development.
Consequently, the overarching goal should be to provide a methodology that systematically reduces the investment risk with IT architectures for SMEs. Thus, the research question is: How can we generate alternative, valid scenarios for the process and IT environment in a company in the realm of advanced systems engineering?
The work developed iteratively in phases of conceptual method transfer from scenario management and practical application in industry consulting for product lifecycle management.
ASE depends on the interoperability between workstations, human cognition and IT. Firstly, based on the company's requirements, we describe and identify the systemic context and the objects of analysis, such as functional IT-entities and human work. Secondly, the bidirectional relations of the functional entities are analyzed and matched against indicators from the requirements. This creates a cross-impact matrix  to analyze the impacts and generate consistent scenarios for the design of the IT and process environment.
As a result of this work, we show a proven method flow that generates alternative IT designs. The method flow adapts common techniques for the analysis of the specific characteristics of advanced systems engineering. More precise, the method evaluates the consistency of IT-system interfaces, organizational functions and the company's individual preferences of process maturity. The developed methodology suggests guidelines to set the scope within a company, from which the critical influential factors on the consistent IT-landscape are derived. From a practical point of view, a proper identification of the scope is crucial for a basis of significant and correct data. Next, these factors are analyzed towards their consistency, for which we use the Monte Carlo method. Now, these factors can be clustered into consistent scenarios, that represent possible designs of the advanced systems landscape.
Contribution to Scholarship
Currently, little theory is known about the consistent design of the engineering environment for developing complex mechatronic systems. Here, not only an overview on the new technological challenges is given, but also on key success factors to conquer them. This gives guidance to other research activities.
Contribution to Practice
This work highly supports the structural and methodological design of functional entities within the product creation in SMEs in terms of advanced systems engineering. The presented methodology fosters the consistency of IT-systems and processes and therefore also persistent and sustainable structures in producing companies. Additionally, the presented methodology is practically proven. It creates a manageable abstraction of technologies for systems engineering, reduces the complexity and is therefore applicable by SMEs.
Advanced systems engineering opens a novel technological area that extends the fourth industrial revolution into the whole product creation process. The presented work introduces this new realm by giving a first approach to designing the landscape of novel process technologies and therefor bridges research and technology into direct industrial application.
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Museums in the 4.0 age: discussing the current approaches to the digital transformation
1Sapienza University of Rome, Italy; 2University of Macerata, Italy; 3University of Salerno, Italy
According to the ICOM, museums acquire, conserve, research, communicate and exhibit “the tangible and intangible heritage of humanity and its environment for the purposes of education, study and enjoyment” (ICOM, 2007). Over the last decade, the contribution of digital technologies to the management of these activities has increased significantly.
As suggested by scientific literature on this matter, digital technologies have created new spaces for interaction and sharing among various stakeholders (Hooper-Greenhill, 1999; Lazzeretti et al., 2015a; Pallud, 2017). In particular, they are contributing to reshaping museums’ role as producers and distributors of cultural value (Lazzeretti et al., 2015b) and their pervasiveness is transforming several aspects of the cultural offer by attracting and satisfying new audiences (Levent et al., 2014). Technologies, therefore, might have the potential to aid museums in redefining their unique place in public life, by changing the relationship between visitors and a museum object (Levent et al., 2014). As a consequence of this progressive transformation, museums have started to rethink their business, becoming community-oriented and led by more inclusive strategies (Cerquetti, 2016; Solima, 2016). In so doing, museums have changed the way to achieve their institutional goals, also triggering new forms of art fruition (Porter, Heppelmann, 2014).
The 4.0 revolution is suggesting new value creation opportunities not only for the manufacturing industry, but also for the museum one (Del Maso, 2018). However, museum studies have not yet analysed how digital technologies impact on the different dimensions of the museum value chain (research, conservation and communication).
Four relevant research questions can be set: 1) Which needs drive museums’ digital shift? 2) How does the 4.0 revolution impact on museum management? 3) Which are the main design dimensions of the emerging Museum 4.0? 4) What are the main challenges museums have to face?
Aiming to prevent the Museum 4.0 from becoming an empty concept or just a new label for old strategies, the research critically discusses current approaches to the digital transformation of museums through a review of scientific research and relevant case studies on this matter. Both academic literature and reports about activities carried out by museums of different types are analysed, in order to provide a framework of the emerging trends and challenges for the development of the sector.
The investments in digital technologies occur gradually and differently involve art fruition and visitors’ experience (Bonacini, 2014; Solima, 2016). The widespread use of social media is, for example, a strategic trend which creates, develops and delivers innovative business models where physical and digital boundaries are even more fuzzy and blurred (Kelly, 2010; Bertacchini, Morando, 2013; Pulh, Mencarelli, 2015; #Socialmuseums, 2016).
In this context, the research aims to identify the various 4.0 opportunities and tools already applied or to be applied to museums and if and how they affect the different dimensions of the museum value chain (research, conservation and communication) and their interconnection.
The analysis identifies three museum clusters: 1) a first group of museums that takes the 4.0 challenge for marketing research (e.g. through the use of big data); 2) a second group that focuses on technologies for restoration and conservation; 3) a third group that uses digital technologies to improve the quality of museum services through the interaction with users (e.g. IoT, 3D, etc.). The third cluster is certainly the broader one and includes different approaches to digital storytelling are emerging (e.g. on-line vs on-site).
Contribution to Scholarship
As already argued, over the last decade, different cases and experiences have been studied in-depth from different disciplinary perspectives (e.g. information sciences, marketing, museum studies, etc.), but an overall framework which highlights possible implications for museum management is still missing. The research aims to fill this gap by identifying the impact of ICTs on the different dimensions of museum value chain.
Contribution to Practice
The research aims to help museums to identify how to grasp the various opportunities which emerge from the 4.0 revolution and to overcome difficulties in developing innovation. Current managerial gaps and possibilities of improvement are also highlighted (e.g. acquisition of new professional skills, adoption of new organisational models, etc.) (Confetto, Siano, 2017).
The research fits the aims of the conference in general and the theme of this particular edition because it investigates the innovation challenge in museums, bridging scientific research, museum management and its impact on society. Moreover, it is relevant for the track because it explores the impact of the 4.0 revolution on museums.
#SOCIALMUSEUMS. Social media e cultura, tra post e tweet (2016). X rapporto CIVITA, Milano: Silvana editoriale.
Bertacchini, E., Morando, F. (2013). The Future of Museums in the Digital Age: New Models of Access and Use of Digital Collections. International Journal of Arts Management, 15(2), 60-72.
Bonacini, E. (2014). La realtà aumentata e le app culturali in Italia: storie da un matrimonio in mobilità, Il capitale culturale. Studies on the Value of Cultural Heritage, 9, 89-121, https://riviste.unimc.it/index.php/cap-cult/article/viewFile/740/573.
Cerquetti, M. (2016). More is better! Current issues and challenges for museum audience development: A literature review, ENCATC Journal of Cultural Management and Policy, 6(1), 30-43.
Confetto, M.G., Siano, A. (2017). Museo e tecnologie digitali: profili professionali emergenti. Il capitale culturale. Studies on the Value of Cultural Heritage, 15, 103-135, https://riviste.unimc.it/index.php/cap-cult/article/view/1581/1162.
Dal Maso, C., edited by (2018). Racconti da museo. Storytelling d’autore per il museo 4.0. Bari: Edipuglia.
Hooper-Greenhill, E. (1999). The Educational Role of the Museum. London and New York: Routledge.
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Lazzeretti, L., Sartori, A., Innocenti, N. (2015a). Museums and social media: the case of the Museum of Natural History of Florence. International Review on Public and Nonprofit Marketing, 12, 267-283.
Lazzeretti, L., Sartori, A., Innocenti, N. (2015b). The role of social media in museum marketing communication strategies: The case of the Museum of Natural History of the University of Florence. International Review on Public and Non-profit Marketing, 12(3), 267-283.
Levent, N., Knight, H., Chan, S., Hammer, R.L. (2014). Technology, senses, and the future of museums. In N. Levent & A. Pascual-Leone (Eds.), The multisensory museum, cross- disciplinary perspectives on touch, sound, smell, memory, and space (pp. 341-348). Lanham, MD: Rowman & Littlefield.
Pallud, J. (2017). Impact of interactive technologies on stimulating learning experiences in a museum. Information & Management, 54, 465-478.
Porter, M., Heppelmann, J.E. (2014). I prodotti intelligenti interconnessi che stanno trasformando la competizione. Harvard Business Review.
Pulh, M., Mencarelli, R. (2015). Web 2.0: Is the museum visitor relationship being redefined? International Journal of Arts Management, 18(1), 43-51.
Kelly, L. (2010), How Web 2.0 is Changing the Nature of Museum Work. Curator, 53(4), 405-410
Solima, L. (2016). Smart Museums. Sul prossimo avvento della Internet of Things e del dialogo tra gli oggetti nei luoghi della cultura. Sinergie Italian Journal of Management, 34(99), 263-283.
Solima, L., Della Peruta, M.R., Maggioni, V. (2016). Managing adaptive orientation systems for museum visitors from an IoT perspective. Business Process Management Journal, 22(2), 285-304.
Zimmer, R. (2008). Touch technologies and museum access. In H. Chatterjee (Ed.), Touch in museums: Policy and practice in object handling (pp. 150-162). Oxford, England: Berg.
Do you care how digital platforms use your data? The role of transparency in Data-Driven Business Models
1Politecnico di Milano, Italy; 2Penn State University, USA
“If you're not paying for it, you become the product”
The daily routine of millions of people relies on digital applications, that support a wide range of activities. These services have the opportunity to transform interactions into valuable data that can be leveraged in several ways: what about users' perspective?
Hartmann et al. (2016) provide a conceptualization of data-driven business models, focusing on companies using data as a key resource in their model. Scholars point out how the chance to gather a considerable amount of user-generated data may lead to different innovation opportunities, exploiting their value (Del Vecchio et al., 2018; Trabucchi et al., 2018). According to Xie and colleagues (2016), users can generate data having various roles and these data can be used internally by the firm to improve its business model. Otherwise, companies can expand their business model scouting for other groups of customers who are interested in the value embedded in the gathered data (Trabucchi and Buganza, 2019). This may give birth to peculiar business models, where the central firm gather data from the main users of its services and offer them to another group of customers, creating a non-transaction two-sided platform (Trabucchi et al., 2017).
Users are often unaware of the usage that companies do on their data (Trabucchi et al., 2017; 2018). Nevertheless, privacy issues and concerns are increasing (Acquisti et al., 2013). This research aims to understand if being transparent on the usage of data would impact the willingness to use the service.
This research aims to Investigate whether business model transparency about user-generated significant data usage affects users' willingness to use digital services. The results may have an important impact on the design of a data-driven business model, increasing trust towards the service provider.
According to Carpenter and colleagues (2004), we worked on a framed field experience, which is an artefactual field experiment but with field context in either the commodity, task or information set that the subjects can use. To deliver the experiment, a within-subjects design was chosen, in which the participants are exposed to multiple treatments over time. In this way, the respondent bias is reduced having the chance to see if and how the response change in the two different settings. The dependent variable – the willingness to use the service – is measured through a Likert-scale on a construct previously used in the literature (Liu et al., 2015), as well as the various control variables (e.g., age, nationality, innovation propension, privacy attitude). We delivered the framed field experiment through a digital survey, gathering 500 complete responses.
Data have been analyzed through an ANOVA, implementing all the necessary control variable and reliability tests. Preliminary analysis of the gathered data shows a counterintuitive result. The transparency of the business model does not influence the willingness of users to use the digital service. This means that the willingness to download and use the services presented in the mock-ups is not statistically different in the two scenarios. This results open up interesting space for discussions on business model design.
Contribution to Scholarship
The main contribution of this research is related to the design of digital business models based on data. First, it expands the studies of data-driven business model by adding a user perspective (e.g., Del Vecchio et al., 2018; Trabucchi et al., 2018). Second, it builds on the growing body of literature on the privacy concerns, showing how - independently from the concerns - transparency is appreciated and valued (Acquisti et al., 2013; Hann et al., 2007).
Contribution to Practice
This year conference aims to dig in the innovation challenge, bringing different perspectives. This paper contributes to the discussion analyzing how data-driven business models are perceived by the society, aiming in particular to have a role in Track 3.2 that aims to discuss the opportunities provided by digital platforms.
• Acquisti A., Leslie K. J., & Loewenstein G. (2013). What is privacy worth? Journal of Legal Studies 42 (2), p. 249 – 274.
• Buganza, T., Dell'Era, C., Pellizzoni, E., Trabucchi, D., & Verganti, R. (2015). Unveiling the potentialities provided by new technologies: A process to pursue technology epiphanies in the smartphone app industry. Creativity and Innovation Management, 24(3), 391-414.
• Carpenter J., Harrison G. W., & List J. A. (2004). Field Experiments in Economics: An Introduction. Research in Experimental Economics.
• Del Vecchio, P., A. Di Minin, A.M. Petruzzelli, U. Panniello, and S. Pirri. (2018). Big data for open innovation in SMEs and large corporations: Trends, opportunities, and challenges. Creativity and Innovation Management 27(1): 6-22.
• Hann I., Hui K., Lee S. & Png I. (2007). Overcoming online information privacy concerns: An information-processing theory approach. Journal of Management Information Systems 24 (2), p. 13 – 42.
• Hartmann P. M., Zaki M., Feldmann N. & Neely A. (2016). Capturing value from big data – a taxonomy of data-driven business models used by start-up firms. International Journal of Operations & Production Management 36 (10), p. 1382 – 1406.
• Liu C., Marchewka J. T., Lu J. & Yu C. (2005). Beyond concern—a privacy-trust-behavioral intention model of electronic commerce. Information & Management, 42 289-304.
• Podsakoﬀ P. M. & Podsakoﬀ M. P. (2018). Experimental designs in management and leadership research: Strengths, limitations, and recommendations for improving publishability. The Leadership Quarterly.
• Trabucchi D., Buganza T. & Pellizzoni E. (2017). Give away your digital services. Research-Technology Management, 60(2), p. 43 – 51.
• Trabucchi D., Buganza T., Dell’Era C. & Pellizzoni E. (2017). Exploring the inbound and outbound strategies enabled by user generated big data: Evidence from leading smartphone applications. Creativity and Innovation Management 27 (1), p. 1 – 14.
• Trabucchi, D., and Buganza, T. (2019). Data-Driven Innovation: switching the perspective on Big Data. European Journal of Innovation Management: https://doi.org/10.1108/EJIM-01-2018-0017
• Trabucchi, D., T. Buganza, C. Dell'Era, and E. Pellizzoni. 2018. Exploring the inbound and outbound strategies enabled by user generated big data: Evidence from leading smartphone applications. Creativity and Innovation Management 27(1): 42-55.
• Xie K., Wu Y., Xiao J., Hu Q. (2016). Value co-creation between firms and customers: The role of big data-based cooperative assets. Information & Management, 53, 1034 – 1048.
The role of technology enablers in fostering resilience
Sapienza Università di Roma, Italy
This work analyzes the relationship between resilience and technology, with reference both to the role played by the three different actors (technology creators, enablers, and users) that shape specific technological paradigms and to the interactions between them.
In organizational contexts, resilience is defined as the ability to sense, recognise, adapt, and absorb variations, changes, disturbances, disruptions, and surprises, being able to adapt and manage the environments variability, and capable to continuous reconstruction (Horne & Orr, 1998; Hamel & Valikangas, 2003; Hollnagel et al. 2007). The emergence of knowledge capitalism is related to the action of two drivers (Drucker, 2003): on the one hand, the long-term increase of resources allocated to the production and the transmission, in time and space, of knowledge; on the other hand, the advent of a new techno-economic paradigm (Dosi et al., 1988) produced by the ICT revolution and by the growing number of its developments and applications.
The above-mentioned emphasizes the deep link between knowledge and technology, highlighting how the latter can represent a synthesis of human knowledge.
Different theoretical contributions (Layton, 1974; Arthur, 2009) assimilate technology to knowledge; however, what is important is the ability to create associative circuits where knowledge is expressed as a technology capable of linking the components present in a specific context. This work aims to contribute to this literature gap.
a) Is there a strong cognitive distance between the actors who create technology (technology creators) and those who use it (users)?
b) Can this distance be reduced?
After a literature review on technology, the main theoretical contributions about its assimilation to knowledge, and the dimensions that impact on its pattern, the work focuses on analyzing the relationship between resilience and technological know-how, with the aim of contributing to the above-mentioned literature gap.
We will propose a theoretical model in which three actors (technology creators, enablers, and users) are represented by 3 curves that distribute according to a power law.
In focusing on the middle curve, represented by the enablers, we can identify the originality of this work, as these actors, that we can assimilate to technology broker, act in reducing the cognitive slack between technology creators and users and in limiting the resulting slowdown in the diffusion of technology itself. In this sense, we can say that they are promoters and amplifiers of resilience.
Contribution to Scholarship
Enablers, acting as a spectrum between the moment of creating and that of adopting technology, on the one hand through learning processes and on the other through knowledge transfer processes (Cohen & Levinthal, 1990), allow the emergence of definite techniques, from a specific method, which in turn are declined in appropriate instruments (Barile et al., 2014).
Contribution to Practice
In terms of practical implications, the 3 curve model can be useful to understand the mechanisms that affect the process of technological knowledge transfer, providing an instrument to act, through planned management actions, on the existing cognitive gap, in order to make the new technology consonant with the reference context.
In contemporary socio-economic contexts, in which organizations face growing innovation challenges, the ability of resilience is even more required, and it becomes more and more linked to the availability of intangible slacks in what is usually defined as "knowledge capitalism"(Foray, 2006; Volpato & Stocchetti, 2007; Greco & Silvestrini, 2009).
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