Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

 
Session Overview
Session
20-PM2-03: ST7.5 - Emerging Landscapes. New Skills, New Technologies and New Organizational Challenges in the 4.0 Age
Time:
Thursday, 20/Jun/2019:
2:45pm - 4:15pm

Session Chair: Marcelo Enrique CONTI, Sapienza University of Rome, Management Dep.
Session Chair: Giuliano Maielli, Queen Mary, University of London
Session Chair: Laura RIOLLI, California State University Sacramento
Session Chair: Cristina SIMONE, Sapienza University of Rome, Management Dep.
Location: Amphi Becquerel

Session Abstract

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.


Show help for 'Increase or decrease the abstract text size'
Presentations

How do organizations create and develop capabilities in Analytics?

Cristiane Matsumoto, Mario Salerno

University of São Paulo, Brazil

Context

Analytics practice involves tools, methods, technology, skillful analysts and management to orchestrate those resources in order to extract value from data. However, systematic use of analytics and the creation of a capability depends on organizational actions and mechanisms that support required transformations to create more data-driven environment mindset.

Literature

Analytics capability concept relates to an organizational proficiency in the use of data for a strategic and operational vision (Mikalef, Pappas, Krogstie, & Giannakos, 2018). Wamba, et al. (2017) suggest a capability model formed by capabilities dimensions (management, people, infrastructure) that can improve firm performance. Besides identifying needed resources, it lacks on detailing how analytics results are operationalized. Sharma, Mithas and Kankanhalli (2014) state that there is not an obvious relationship between best insights and expected outcomes, and researchers must focus on behavioral, organizational and strategic aspects. Gupta and George (2016) highlights human and intangible resources in the capability model, emphasizing firm´s managerial roles – similar assumption to studies that identifies managers as key parts on incorporating analytical results (Janssen, Voort, & Wahyudi , 2017; Vidgen, Shaw, & Grant, 2017). Gupta and George also considers data-driven culture and intensity of organizational learning, supporting and enabling the analytics capability.

Literature Gap

Analytics capability models identify resources required, lacking descriptions on how organizations can organize them for a systematically use. There is also a misunderstanding on what consists that capability that must be viewed as a proficiency on both generating analytical results and incorporating them in firm´s operations.

Research Questions

The first part of research is to conceptualize analytics capability, describing two important phases: analytical results generation and their operationalization. That is essential to understand how organizations actions influences analytics practice. Thus, the focus can be fine-tuned to investigate mechanisms that foster and support the development of analytics capability.

Methodology

This research presents a qualitative approach. The first part consists on a literature review, selecting papers that describes organizational aspects on deploying analytics initiatives. Using micro foundations logic (Felin, Foss, Heimeriks, & Madsen, 2012) and organizational project theory (Galbraith, 1983), five dimensions (strategy, structure, process, people, rewards) will guide the review in order to identify patterns on actions and mechanisms on analytics practice. The review results will structure the research model. Thus, the second part of researching will be testing the model in real cases, conducting semi-structured interviews with organizations that are in further phases of (analytics) experimentation.

Empirical Material

References selected for literature review includes 10 papers and reports. Those reports, despite the lack of academic rigor, have considerable descriptions and results in the application of analytics in organizations. All references are: Hernandez, Berkey and Bhattacharya (2013), LaValle, Lesser, Shockley, Hopkins and Kruschwitz (2011), Kane, Palmer, Phillips, Kiron and Buckley (2017), Davenport and Dyche (2013), Félix, Tavares and Cavalcante (2018), Janssen, Voort and Wahyudi (2017), Vidgen, Shaw and Grant (2017), Popovic, Hackney, Tassabehji and Castelli (2018) and Galbraith (2014). Empirical material will be collected, ongoing planning is to finalize this part of process until the date of submission of the full article (May 31st).

Results

Researching first objective was to propose an analytics capability concept, which can be summarized as an ability to systematically generate and operationalize analytical results, aiming to improving organizational performance. Reviewing application cases described on literature, it was identified that interaction coordination (among multifunctional professionals) is critical, especially for generating analytical results; in operationalization phase, managers’ active engagement is emphasized. Some elements facilitate overcoming those factors and systematically support analytics practice such as formalizing standard procedures and tasks, assuring security and conformity (data and processes), strategically selecting analytical demands and transferring autonomy to leaderships and employees. Those elements can be promoted by organizational mechanisms (constructs) identified as analytical structure with centralized coordination, governance system, ‘management action’ (training, incentives, metrics) and analytical strategy. Each construct aggregates different tasks, and it is possible to nominate its main purpose, respectively as resources coordination, processes coordination, operations coordination and guiding the practice. Those constructs and their primordial function, together (not alone), enables analytics capability development. The analytics strategy guides and strongly influences other constructs and its 'absence' compromises analytics capability creation – absence can be understood as lack of clear objective, non-alignment with organization´s macro strategy or lack of communication or understanding of strategy.

Contribution to Scholarship

This research aims to contribute to the increasing analytics literature, which has been working with the concept of analytics capability (including other terms as business analytics or big data analytics capability). Besides more researches dealing with this concept, there is a lack of clarity about the extension of the capability that must be understood not only as an ability to generate substantial analytical results as a proficiency to implement them in organization operations. Recurrent issue in this field is lacking of studies regarding how analytics capability can be structured in the organization (Mikalef, et al., 2018; Popovic, Hackney, Tassabehji, & Castelli, 2018; Posavec & Krajnovic, 2016). The study also aims to aggregate to management theories, such as resource based view and dynamic capability, that suffer frequent criticism about the lack of explicit distinction in the construction and acquisition of capability and the implementation processes required (Kraaijenbrink, Spender, & Groen, 2010).

Contribution to Practice

Analytics is one of the many objects searched when promoting digital transformation in organizations. Many aspects that support analytics capability building can be the same required to engage other subjects in digital field, especially some strategical elements that induce organizational actions towards analytics practice. Consequently, identifying organizational mechanisms that support analytics capability development can contribute to firms that aims to use analytics systematically in their operations and want to become digital. This path requires actions and certain degrees of transformations, resulting on evolving a (more) data-driven environment.

Fitness

Research main theme seems to be aligned to the theme track, in the sense of the efforts required by organizations in order to promote one of the solution in the Industry 4.0. Analytics capability development need organization mechanisms in order to support and structuralize a systematic use of it.

Bibliography

Davenport, T. (Dezembro de 2013). Analytics 3.0. Harvard Business Review, 91(12), 1-12.

Davenport, T., & Dyche, J. (2013). Big Data in Big Companies. International Institute for Analytics.

Felin, T., Foss, N., Heimeriks, K., & Madsen, T. (2012). Microfoundations of Routines and Capabilities: Individuals, Processes, and Structure. Journal of Management Studies, 1351-1374.

Félix, B. M., Tavares, E., & Cavalcante, N. W. (2018). Fatores críticos de sucesso para adoção de Big Data no varejo virtual: estudo de caso do Magazine Luiza. Revista Brasileira de Gestão de Negócios, 20(1), 112-126.

Galbraith, J. (1983). Strategy and Organization Planning. Human Resource Management, 63-77.

Galbraith, J. (2014). Organizational Design Challenges Resulting from Big Data. Journal of Organization Design, 3(1), 2-13.

Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics capability. Information & Management, 53, 1049–1064.

Hernandez, J., Berkey, B., & Bhattacharya, R. (2013). Building an Analytics-Driven Organization. Accenture.

Janssen, M., van der Voort, H., & Wahyudi , A. (2017). Factors influencing big data decision-making quality. Journal of Business Research Factors, 70, 338-345.

Kane, G., Palmer, D., Phillips, A., Kiron, D., & Buckley, N. (July de 2017). Achieving Digital Maturity. MIT Solan Management Review and Deloitte University Press, 1-29.

Kraaijenbrink, J., Spender, J.-C., & Groen, A. (2010). The Resource-Based View: A Review and Assessment of Its Critiques. Journal of Management, 349-372.

LaValle, S., Lesser, E., Shockley, R., Hopkins, M., & Kruschwitz, N. (2011). Big Data, Analytics and the Path From Insights to Value. MIT Sloan Management Review, 52(2), 21-32.

Mikalef, P., Pappas, I., Krogstie, J., & Giannakos, M. (2018). Big data analytics capabilities: a systematic literature review and research agenda. Information Systems and e-Business Management, 547–578.

Popovic, A., Hackney, R., Tassabehji, R., & Castelli, M. (2018). The impact of big data analytics on firms’ high value business performance. Information Systems Frontiers, 209-222.

Posavec, A., & Krajnovic, S. (2016). Challenges in Adopting Big Data Strategies and Plans in Organizations. 39th International Convention on Information and Communication Technology, Electronics and Microelectronics, (pp. 1229-1234). MIPRO 2016 - Proceedings.

Sharma, R., Mithas, S., & Kankanhalli, A. (2014). Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organisations. European Journal of Information Systems, 23(4), 433–441.

Vidgen, R., Shaw, S., & Grant, D. (2017). Management challenges in creating value from business analytics. European Journal of Operational Research, 261, 626–639.

Wamba, S., Gunasekaran, A., Shahriar, A., Ren, S.-f., Dubey, R., & Childe, S. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365.



HYBRID PLATFORMS, LEADERSHIP AND INNOVATION IN HUMANOID ROBOTICS

Giuliano Maielli

Queen Mary, University of London, United Kingdom

Context

Based on two in-depth case studies, this paper analyses the R&D dynamics of hybrid platforms in the context of on technological platforms literature and analyses.

Literature

Over the last few years, the notion of technological platforms (Gawer, 2014) has gained substantial currency in innovation, strategy and management studies (Thomas, Autio and Gaan, 2014) as it offers a coherent framework enabling the multi-level analysis of R&D (i.e. within, firms, supply chains and ecosystems) across the entire spectrum of products and services, both in traditional and digital sectors of the economy. The main underpinning of this framework is that technological platforms work as meta-organisations that coordinate and federate knowledge across a range of productive units. In doing so, technological platforms continuously change their configuration. Gawer (2014) distinguishes between three types of platform configurations, namely, internal, supply chain and market platforms each with different level of R&D openness. According to the established literature, platforms are able to shift form one configuration to another strategically. However this implies a shift in architectural frameworks (Henfridsson, Mathiassen and Svahn, 2014).

Literature Gap

More empirical work is needed to understand the actual dynamics of meta-organisations in relation to the hardware/software entanglement in complex platforms and in relation of architectural frameworks. To this end we investigate hybrid platforms where open and close innovation coccus simultaneously in different parts of the product architecture.

Research Questions

This paper investigates whether, in humanoid robotics platforms, there are different levels of openness in hardware and software innovation, and between high and low level controls (hence configuring humanoid robotics as a hybrid platform) and how this would affects platform leadership.

Methodology

The paper adopts an in-depth cases study methodology (Tripsas & Gavetti, 2000). In particular it analyses two case studies in humanoid robotics, namely Aldebaran and Shadow Robotics.

Empirical Material

The paper is based on a mixture of ethnographic observations and 6 long interviews to R&D managers. More empirical work will be conducted.

Results

The analysis of Aldebaran and Shadow shows that humanoid robotics can be considered a hybrid platform at the intersection between mechanical engineering and information technology with complex hybrid platform configurations. In respect to lower level control (a robot hardware and software for command implementation) R&D is semi-open and carried out within a supply-chain configuration. Design is governed by a “hierarchy of parts logic” in respect of both hardware and software. However, high level controls (artificial intelligence) is carried out by a market platform where a vast network of complementors including firms of various sizes, educational institutions and even users/programmers develop new software and share it in open source. AI is developed through a “patterns of networks” logic.

Contribution to Scholarship

The main contribution of the paper is to theorise how within these hybrid platforms, platform leadership is widely shaped and almost spontaneously emerge by the interaction between hierarchy of parts and patterns of networks. We also discuss how studies of complex technological platforms as in the case of humanoid robotics can further our understanding of innovation theory, policy and practice.

Contribution to Practice

The paper is of interest to R&D managers and strategist operation in complex technological platforms, where different architectural logics are in operation.

Fitness

The paper addresses issues of platform leadership highlighting the dynamics of open innovation in complex hybrid technological platforms.

Bibliography

Björkdahl, J. (2009). “Technology cross fertilization and the business model: The case of integrating ICTs in mechanical engineering products”. Research Policy, 38: 1468-1477.

Ching-Long, S., Wen-Yo, L., Chia-Pin, W. (2012). “Planning and Control for Stable Walking 3 D Bipedal Robot”. International Journal of Advanced Robotic Systems, Vol. 9, 47.

Chesbrough, H. W. (2006). Open Innovation. The new Imperative for Creating and Profiting from Technology. Harvard Business School Press.

Chesbrough, H. W. (2007). “Business model innovation: It’s not just about technology anymore”. Strategy and Leadership, 35: 12-17.

Gawer A, Cusumano M (2013). “Industry Platforms and Ecosystem Innovation”. Journal of Product Innovation Management 31 (3) pp. 417-433 Wiley

De Reuver, M., Sørensen, C., Basole, R., C. (2017). “The digital platform: a research agenda”. Journal of Information Technology doi:10.1057/s41265-016-0033-3

Eisenhardt, K. (1989). “Building Theories from Case Study Research”. Academy of Management

Review 14 (4), 532–550.

Forge, S., Blackman C. (2010) “A Helping Hand for Europe: The Competitive Outlook for the EU Robotics Industry”, JRC.

Gausemeier J. (2005). “From mechatronics to self-optimizing concepts and structures in mechanical engineering: new approaches to design methodology”. International Journal of Computer Integrated Manufacturing, 18(7): 550-560.

Gawer, A., (2014). “Bridging differing perspectives on technological platforms: Toward an integrative framework”. Research Policy, http://dx.doi.org/10.1016/j.respol.2014.03.006 xxx

Gereffi, G.; Humphrey, G.; Sturgeon, T. (2005). "The Governance of Global Value Chains”. Review of International Political Economy, 12:1 February 2005: 78–104.

Henderson, R., Clark, K. (1990). “Architectural innovation: The reconfiguration of existing product technologies and the failure of established firms”. Administrative Science Quarterly, 35, 9-30.

Henderson, R. (2006). “The Innovator’s Dilemma as a Problem of Organisational Competence”. The Journal of Product Innovation Management, 23: 5-11.

Jansen, S., J. Cusumano, M. (2013). “Defining Software Ecosystems: A Survey of Software Platforms and Business Network Governance”, in Jansen Slinger, Sjeaak Brinkkemper and Michale Cusumano. Software Ecosystems: Analysing Business Networks in the Software Industry. Edward Eldgar, Cheltenham, UK, Northampton, Massachusetts, US (2013), pp. 13-27.

Katila, R. (2002). “New Product Search Over Time: Past Ideas in Their Prime?”. Academy Of Management Journal, 45(5), 995-1010.

Lashi, C. (2002). “Recognizing hand posture by vision: applications in humanoid personal robotics”. Robotics and Automation. Proceedings. ICRA 02. International Conference on Robotics and Automation.

Mcintyre D., P., Srinivasan, A. (2017). “Networks, Platforms and Strategy”. Strategic Management Journal 38: 141–160.

Moore, J.F. (2006) “Business Ecosystems and the View from the Firm”. The Antitrust Bulletin, Vol. 51, N 1.

Porter, M. E., & Heppelmann, J. E. (2014). How smart, connected products are transforming competition. Harvard Business Review, 92(11), 64-88.

Porter, M. E., & Heppelmann, J. E. (2015). How Smart, Connected Products Are Transforming Companies. Harvard Business Review, 93(10), 96-114.

Rajapakshe, T., Dawande, M., Sriskandarajah, C. (2011). “Quantifying the Impact of Layout on Productivity: An Analysis from Robotic-Cell Manufacturing”. Operations Research Vol. 59, No. 2, pp. 440–454.

Shu-Hsien, L., Chi-Chuan, W., Da-Chian, H., Guang An, T. (2009). “Knowledge Acquisition, Absorptive Capacity, and Innovation Capability: An Empirical Study of Taiwan's Knowledge-Intensive Industries”. World Academy of Science, Engineering and Technology 53, pp. 160-167.

Shafiei-Monfared, S., Salehi-Gilani K., Jenab, K. (2009). “Productivity Analysis in a Robotic Cell”. International Journal of Production Research, Vol. 47, No. 23, 1, 6651–6662.

Sirkin, A., H., Zinser, M., Rayan Rose, J. (2015). The Robotics Revolution. The next Great Leap in Manufacturing. The Boston Consulting Group.

Solis, J., Atsuo Takanishi, (2010). “Recent Trends in Humanoid Robotics Research: Scientific Background, Applications, and Implications”. Accountability in Research, 17:278–298.

Svenstrup, M., Hansen, S., Andersen, H. & Bak, T. (2011). “Adaptive Human-Aware Robot Navigation in Close Proximity to Humans”. International Journal of Advanced Robotic Systems, 8(2), 7-21.

Sydow, J., Windeler, A., Schubert, C., Mӧllering, G. (2012). “Organizing R&D consortia for path creation and extension: The case of semiconductor manufacturing technologies”. Organization Studies, 33(7): 907-936.

Tashakkori, A, Teddlie, C. (2003). Handbook of Mixed Methods in Social &

Behavioral Research. Thousand Oaks: Sage.

Thomas, L. D., Autio, E., & Gann, D. M. (2014). Architectural leverage: putting platforms in context. The Academy of Management Perspectives, 28(2), 198-219.

Tiwana, A. (2002). “Modes of e-business innovation and structural disruptions in firm knowledge”. Knowledge & Process Management, 9(1), 34-42.

Tripsas, M., Gavetti, G. (2000). “Capabilities, cognition, and inertia: evidence from digital imaging”. Strategic Management Journal, 21, 1147-1161.

Yaochu, J. & Yan, M. (2011). “Morphogenetic Robotics: An Emerging New Field in Developmental Robotics”. IEEE Transactions on Systems, Man & Cybernetics: Part C - Applications & Reviews, 41(2), 145-160.

Yin, R. (1994). “Case Study Research Design and Methods”. Applied Social Science Methods

Series, vol. 5. Sage Publications, New York.

Zimmermann, O., Miksovic, C. & Küster, J. M. (2012). “Reference architecture, metamodel, and modelling principles for architectural knowledge management in information technology services”. Journal of Systems & Software, 85(9), 2014-2033

Zott, C., Amit, R., Massa, L. (2011). “The Business Model: Recent Developments and Future Research”. Journal of Management, Vol. 37, No. 4, pp. 1019-1042.



4.0 Plastic Knowledge: Hybrid Skills and Competencies

Laura Riolli1, Ryan Fuller1, Antonio La Sala2

1California State University, Sacramento; 2University of Salerno, Italy

Context

Industry 4.0 is bringing massive value-creation opportunities. This contextually implies the rising of new complexities. People’s skills/competencies, specifically, play a pivotal role: work organizations will transform due to the disruptive nature of emerging 4.0 technologies. This will require employees/leaders to be outfitted with a wider range of competencies.

Literature

As suggested by scientific literature on this matter, Industry 4.0 and the introduction of new artifacts and technologies is creating a myriad of new professional challenges: thousands of jobs vaporized at a rhythm never seen before, while others are emerging for a reduced number of highly skilled professionals (Macaulay et al., 2010; Jazdi, 2014; Lasi et al., 2014; Le et al, 2014; Herman et al., 2016; Lu, 2017; McAfee & Brynjolfsson 2017; Ustundag & Cevikcan, 2018). This will require employees and leaders to be outfitted with a wide range of competencies (Bianchi, 2018). High-skilled work profiles will gain increasing significance, while labour workforce will be mostly replaced by automated processes (Devezas & Sarygulov, 2017). Therefore, competencies and skills development for employees, managers, leaders are one of the key challenges to adapt to the 4.0 context (Erol et al., 2016; Richert et al., 2016; Prifti et al., 2017).

Literature Gap

Managerial literature affirms the necessity to conjugate deep vertical and broad horizontal capabilities enabling effective move across different disciplines that face changing contexts. However, Industry 4.0 requires a focus on a wider dimension that is the net skills of new special kind of individuals called hybrids.

Research Questions

- Which competencies/skills needed to cope with 4.0 challenges and what the future holds for hybrid employees/leaders?

- Are leaders/employees ready to grasp the opportunities fostered by 4.0 revolution? Are they already hybrid individuals?

- What is the role of institutions/higher education in creating new hybrid leaders/employees?

Methodology

After a pervasive literature review on Industry 4.0 and its linkage with T-shaped and hybrid individuals. We will survey employees and leaders in multiple industries and across functions currently dealing with the ramifications of Industry 4.0 technologies. The aim of this research to get more insights, information and measures on organizational readiness and leadership preparedness to face the emerging and rubber 4.0 scenarios by also identifying competencies for becoming hybrid employees and leaders.

Empirical Material

This study will survey employees and leaders in multiple industries and across functions currently dealing with the ramifications of Industry 4.0 technologies. Our measures include perceptions about industry dynamism predisposition toward changes, organizational readiness and leadership preparedness to face the emerging 4.0 scenario, and competencies for becoming hybrid employees and leaders.

Results

In all significant cases of successful technology implementation for competitive advantage or for achieving major organizational change, there is a person at the heart of the change who displays specific characteristics such as: an understanding of the business and of what is required within it; a deep technical knowledge. In addition, hybrids displayed two more types of organisational skills: the knowledge of their business net; a wide set of social skills as listening, understanding, negotiating and persuading. By merging creativity, entrepreneurial thinking, problem solving, conflict management, decision making, efficiency orientation, strong technical skills, i.e. their plastic knowledge, hybrid individuals can push not only the change of isolated functional areas in their organization, but they can “transmit” their intelligence to the whole organizational level, enhancing resilience. This plastic knowledge gives leaders/employees the possibility to build, deconstruct, rebuild the changing 4.0 scenarios, shaping them according to what is required by the challenge they face, both inside and outside organizations. The general ambiguity surrounding 4.0 Revolution, also calls for a clearer perspective about the leaders’ role. These include: societal/ethical implications, importance of collaboration, trade-offs of investing in technology for the short rather than the long term, addressing the talent gap.

Contribution to Scholarship

In the recent years, the human side of technology has been studied in-depth from different disciplinary perspectives. Regarding human capital management, however, an overall framework which highlights possible implications and evolutionary paths is still missing. The research, specifically, aims to get more insights on organizational readiness and leadership preparedness to face the emerging Industry 4.0 scenario, focusing on hybrid competencies and needed skills to become hybrid employees and leaders.

Contribution to Practice

Technology created jobs for people who worked with their heads but destroyed jobs for those who worked with their hands. The resulting deindustrialization devastated communities and created enormous social and economic difficulties for blue (and white) collar workers. This “skills twist” could be effectively managed via hybrids.

Fitness

The research fits the conference aims because it investigates the innovation challenge in building competencies and skills for 4.0 leaders and employees, bridging scientific research with its impact on society. Moreover, it is relevant for the track because it explores the impact of the 4.0 revolution on human capital.

Bibliography

Anderson, C. (2006), The Long Tail: Why the Future of Business Is Selling Less of More (English Edition).

Anderson, C. (2012). Makers. The new industrial revolution, Business, New York.

Baldwin, R. (2019). The Globotics Upheaval: Globalization, Robotics, and the Future of Work. Oxford University Press.

Barile, S., Saviano, M.L., Simone, C. (2014). “Service economy, knowledge, and the need for T-shaped innovators”, WWW Journal.

Benkler, Y. (2006). The Wealth of Networks: How Social Production Transforms Markets and Freedom. Yale University Press.

Boyatzis, R. E., Goleman, D., & Rhee, K. (2000). Clustering competence in emotional intelligence: Insights from the Emotional Competence Inventory (ECI). Handbook of emotional intelligence, 99(6), 343-362.

Brettel, M., Friederichsen, N., Keller, M., & Rosenberg, M. (2014). “How virtualization, decentralization and network building change the manufacturing landscape: An Industry 4.0 Perspective”. International Journal of Mechanical, Industrial Science and Engineering, 8(1), 37-44.

Burgess, N., Strauss, K., Currie, G., & Wood, G. (2015). Organizational ambidexterity and the hybrid middle manager: The case of patient safety in UK hospitals. Human Resource Management, 54(S1), s87-s109.

Devezas, T., & Sarygulov, A. (2017). Industry 4.0. Springer.

Earl, M. J. (1989). Management strategies for information technology. Prentice-Hall, Inc.

Erol, S., Jäger, A., Hold, P., Ott, K., & Sihn, W. (2016). Tangible Industry 4.0: a scenario-based approach to learning for the future of production. Procedia CiRp, 54, 13-18.

Gilchrist, A. (2016). Industry 4.0: the industrial internet of things. Apress.

Goleman D. (1995), Emotional Intelligece, Bur Rizzoli, Milano.

Goleman, D. (2001). An EI-based theory of performance. The emotionally intelligent workplace: How to select for, measure, and improve emotional intelligence in individuals, groups, and organizations, 1, 27-44.

Hansen T., von Oetinger, B. (2001). Introducing “T-shaped” Managers. Knowledge Management’s Next Generation. Harvard Business Review, 106–116

Hermann, M., Pentek, T., & Otto, B. (2016). “Design principles for industrie 4.0 scenarios”. In System Sciences (HICSS), 2016 49th Hawaii International Conference on (pp. 3928-3937). IEEE.

Homes, G. (2001). The hybrid manager. Industrial and Commercial Training. 33 (1), 16-26.

IfM, IBM (2008), Succeeding through Service Innovation: a Discussion paper, Ed. Cambridge, United Kingdom.

Jazdi, N. (2014). “Cyber physical systems in the context of Industry 4.0”. In Automation, Quality and Testing, Robotics, 2014 IEEE International Conference on (pp. 1-4). IEEE.

Kagermann H., Wahlster W. and Helbig J. (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0. Heilmeyer und Sernau, Germany.

Keen, P.G.W. (1986) Rebuilding Ihe Human Resources of IS. In: hfonnafion Management: The Strategic Dimensbn, Earl, M.J. (ed.). Oxford University Press, Oxford.

Lasi, H., Fettke, P., Kemper, H. G., Feld, T., & Hoffmann, M. (2014). Industry 4.0. Business & Information Systems Engineering, 6(4), 239-242.

Lee, J., Kao, H. A., & Yang, S. (2014). “Service innovation and smart analytics for industry 4.0 and big data environment”. Procedia Cirp, 16, 3-8.

Lu, Y. (2017). “Industry 4.0: a survey on technologies, applications and open research issues”. Journal of Industrial Information Integration, 6, 1-10.

Macaulay, L., Moxham, C., Jones, B., Miles, I. (2010). Innovation and skills: Future service science education. In: Maglio, P.P., Kieliszewski, C.A., Spohrer, J.C. (eds.) Handbook of Service Science, pp. 717–736. Springer-Verlag, New York.

McAfee, A., & Brynjolfsson, E. (2017). Machine, platform, crowd: Harnessing our digital future. WW Norton & Company.

Morin, E. (2007), “Le vie della complessità”, in Bocchi G., Ceruti M. (2007), La sfida delle complessità, Bruno Mondadori Editore, Milano, pp. 25-36.

O’Connor, G . Smallman, C. (1995). The hybrid manager: a review. Management Decision. 33 (7), 19-28.

Palmer, C. (1990). ‘Hybrids’-A Critical Force in the Application of Information Technology in the Nineties. Journal of Information Technology, 5(4), 232-235.

Piaget, J. (1967). On the development of memory and identity.

Prifti, L., Knigge, M., Kienegger, H., & Krcmar, H. (2017). A Competency Model for" Industrie 4.0" Employees.

Richert, A., Shehadeh, M., Plumanns, L., Groß, K., Schuster, K., & Jeschke, S. (2016, April). Educating engineers for industry 4.0: Virtual worlds and human-robot-teams: Empirical studies towards a new educational age. In 2016 IEEE Global Engineering Education Conference (EDUCON) (pp. 142-149). IEEE.

Secchi, R., & Rossi, T. (2019). Fabbriche 4.0. Percorsi di trasformazione digitale della manifattura italiana. goWare & Guerini Next.

Spohrer J, Maglio P.P., Bailey J., Gruhl D. (2007), “Steps towards a Science of Service Systems“, in Computer, 40(1), pp. 61-77.

Spohrer J.C., Maglio P.P. (2010), “Toward a Science of Service Systems: Value and Symbols“, in P.P. Maglio, C.A. Kieliszewski, J.C. Spohrer (eds) Handbook of Service Science. Service Science: Research and Innovations in the Service Economy (pp. 157-194), Springer, New York.

Sutcliffe, K. M., & Vogus, T. J. (2003). Organizing for resilience. Positive organizational scholarship: Foundations of a new discipline, 94, 110.

Ustundag, A., & Cevikcan, E. (2018). Industry 4.0: Managing The Digital Transformation. Springer.

Veza, I., Mladineo, M., & Gjeldum, N. (2015). Managing innovative production network of smart factories. IFAC-PapersOnLine, 48(3), 555-560.

Vogus, T. J., & Sutcliffe, K. M. (2007, October). Organizational resilience: towards a theory and research agenda. In 2007 IEEE International Conference on Systems, Man and Cybernetics (pp. 3418-3422). IEEE.



Shaping the 4.0 landscape: from the rip-bridge-fork process to the T-shaped capabilities.

Sergio BARILE, Cristina SIMONE

Sapienza University of Rome, Management Dep., Italy

Context

4.0 landscape is currently being driven by challenging innovation featured by ripping-bridging-forking process that promises to bring new value creation opportunities. Focusing on this new landscape the paper introduces the T-shaped capabilities architecture that turns out to be more and more crucial to face the challenges of 4.0 landscapes.

Literature

The T-shaped capabilities architecture is rooted in the dynamic capabilities perspective (Teece et al. 1997) and in the information variety definition provided by the Viable System Approach (VsA) (Barile, 2009; Barile et al. 2014). The need for T-shaped Human Resource has been widely recognized in the last decade (Hansen, and von Oetinger, 2001; Spohrer, Maglio, Bailey, and Gruhl, 2007). The increasing need for T-shaped Human Resource arises from the necessity to conjugate deep vertical and broad horizontal capabilities enabling to effectively move across different disciplines and systems. In the common representation of T-shaped profile ‘analytic thinking’ and ‘problem solving’ represent typical vertical competences, while ‘critical thinking, communications, perspective, global thinking, project management, network’, etc. qualify horizontal capabilities (Spohrer, Maglio, Bailey, and Gruhl, 2007; IfM and IBM, 2008; Spohrer and Maglio, 2010).

Literature Gap

Integrating the extant literature, the paper links the need for a T-shaped architecture with the main features of 4.0 landscape; secondly, according to a systemic approach, the paper develops an original fractal T-shaped architecture consistently linking interdependent levels: individual, organizational, meta-organizational.

Research Questions

The paper tries to answer to the following challenging research questions: Which are the main cognitive, organizational and meta-organizational process shaping the 4.0 landscape? Which is the architecture of capabilities promoting and underpinning these process?

Methodology

Moving from the extant managerial literature, this academic research paper develops a description of the main 4.0 process (rip-bridge-fork) and provides a conceptual framework describing the architecture of capabilities underpinning those process.

Empirical Material

---

Results

Firstly, the paper identifies the main cognitive and organizational process shaping the 4.0 landscape: ripping-bridging-forking. Although the 4.0 key technologies (cloud-based design, Mobile Devices, Big Data, smart manufacturing systems, IoT, IIoT, 3D printing) are crucial to support those main process, focusing on them is not enough to understand the main critical issue of 4.0 landscape. To this aim, secondly the paper proposes an original fractal architecture of capabilities -from individual to meta-organizational level- able to successfully ensuring and underpinning those process: the T-shaped capabilities.

Contribution to Scholarship

4.0 landscape are made of diverse technologies spread across many disciplines with many different types of subject matter experts. Although the technological dimension is crucial for the understanding of this challenging landscape, it is not enough. In the emerging socio-technological landscape, market and organizational boundaries and the distinction between services and manufacturing are getting fuzzier than ever. In such a landscape, more and more the opportunity to create value emerge non only inside the extant boundaries (broaden meant) rather by rip-bridge-fork process and by a set of dynamic capabilities that still need to be deeply investigated and understood by scholars. The paper aims to contribute to this stimulating research filed.

Contribution to Practice

As well as academics, practitioners are trying to deeply comprehend the consequences of the 4.0 age for employees, businesses, technology users/enablers and the society at large. As well as academics, practitioners are asking to adopt new “lenses” enabling old and new actors to walk successfully in such magmatic landscape. The paper contributes to provide this new lens by identifying both some of the key-process (rip-bridge-fork) and a specific architecture of key capabilities -the T-shaped one - fitting the challenge of the 4.0 landscape.

Fitness

The paper fits the aims of the Conference because it investigates typical 4.0 process such as rip-bridge-fork and it tries to develop a framework of related T-shaped capabilities helpful to promote and facilitate those process in the emerging 4.0 landscape.

Bibliography

Anderson, C. (2006), The Long Tail: Why the Future of Business Is Selling Less of More (English Edition).

Anderson, C. (2012). Makers. The new industrial revolution, Business, New York.

Barile S. (2009), The dynamic of information varieties in the process of decision making. In The 3rd International Conference on Knowledge Generation, Communication and Management: KGCM 2009 in the context of The 13th World Multi-Conference on Systemics, Cybernetics and Informatics: WMSCI 2009 10-13 July. Orlando, Florida, USA.

Barile, S., Saviano, M.L., Simone, C. (2014). “Service economy, knowledge, and the need for T-shaped innovators”, WWW Journal.

Benkler, Y. (2006). The Wealth of Networks: How Social Production Transforms Markets and Freedom. Yale University Press.

Bianchi P., 4.0. La nuova rivoluzione industriale, il Mulino, Bologna, 2018.

Brettel, M., Friederichsen, N., Keller, M., & Rosenberg, M. (2014). “How virtualization, decentralization and network building change the manufacturing landscape: An Industry 4.0 Perspective”. International Journal of Mechanical, Industrial Science and Engineering, 8(1), 37-44.

Brynjolfsson E., McAfee A., La nuova rivoluzione delle macchine, Feltrinelli, Milano, 2014.

Burmeister, C., Lüttgens, D., & Piller, F. T. (2015). Business Model Innovation for Industrie 4.0: Why the Industrial Internet Mandates a New Perspective on Innovation. vol. 0, 1-31.

Chesbrough, H. (2010). Business model innovation: opportunities and barriers. Long range planning, 43, 2, 354–363; , 354–363

Ciravegna, L., Maielli, G. (2011). “Outsourcing of New Product Development and the Opening of innovation in mature Industries: A Longitudinal Study of Fiat During Crisis and Recovery”, International Journal of Innovation Management, Vol. 15 Issue 1, p.69-93.

de Bono, E. (1969). The Use of Lateral Thinking. R.C.S., Milano.

de Bono, E. (1970). Lateral Thinking. A textbook of Creativity. Mica Management Resources, UK.

Devezas, T., & Sarygulov, A. (2017). Industry 4.0. Springer.

Gawer, A. (2014). Bridging differing perspectives on technological platforms: Toward an integrative framework. Research policy, 43(7), 1239-1249.

Gawer, A., & Cusumano, M. A. (2014). Industry platforms and ecosystem innovation. Journal of Product Innovation Management, 31(3), 417-433.

Gilchrist, A. (2016). Industry 4.0: the industrial internet of things. Apress.

Hansen T., von Oetinger, B. (2001). Introducing “T-shaped” Managers. Knowledge Management’s Next Generation. Harvard Business Review, 106–116

Henderson, R.M., Clark, K.B. (1990). Architectural Innovation: The Reconfiguration of Existing Product Technologies and the Failure of Established Firms. Administrative Science Quarterly, 35, 1, 9–30.

Hermann, M., Pentek, T., & Otto, B. (2016). “Design principles for industrie 4.0 scenarios”. In System Sciences (HICSS), 2016 49th Hawaii International Conference on (pp. 3928-3937). IEEE.

IfM, IBM: Succeeding through Service Innovation: a Discussion paper. Cambridge, United Kingdom (2008).

Jazdi, N. (2014). “Cyber physical systems in the context of Industry 4.0”. In Automation, Quality and Testing, Robotics, 2014 IEEE International Conference on (pp. 1-4). IEEE.

Lasi, H., Fettke, P., Kemper, H. G., Feld, T., & Hoffmann, M. (2014). Industry 4.0. Business & Information Systems Engineering, 6(4), 239-242.

Lee, J., Kao, H. A., & Yang, S. (2014). “Service innovation and smart analytics for industry 4.0 and big data environment”. Procedia Cirp, 16, 3-8.

Lom, M., Pribyl, O., & Svitek, M. (2016, May). “Industry 4.0 as a part of smart cities”. In Smart Cities Symposium Prague (SCSP), 2016 (pp. 1-6). IEEE.

Lu, Y. (2017). “Industry 4.0: a survey on technologies, applications and open research issues”. Journal of Industrial Information Integration, 6, 1-10.

Macaulay, L., Moxham, C., Jones, B., Miles, I. (2010). Innovation and skills: Future service science education. In: Maglio, P.P., Kieliszewski, C.A., Spohrer, J.C. (eds.) Handbook of Service Science, pp. 717–736. Springer-Verlag, New York.

Maielli, G. (2015). “Explaining Lock-in through the Concept of hegemony: Evidence from the Italian car industry”. Organisation Studies, Vol. 36(4) 491-511.

Monostori, L. (2014). “Cyber-physical production systems: roots, expectations and R&D challenges”. Procedia Cirp, 17, 9-13.

Paelke, V. (2014). “Augmented reality in the smart factory: Supporting workers in an industry 4.0. environment”. In Emerging Technology and Factory Automation (ETFA), 2014 IEEE (pp. 1-4). IEEE.

Prause, G. (2015). “Sustainable business models and structures for industry 4.0”. Journal of Security & Sustainability Issues, 5(2).

Roblek, V., Meško, M., & Krapež, A. (2016). “A complex view of industry 4.0”. SAGE Open, 6(2), 2158244016653987.

Shamim, S., Cang, S., Yu, H., & Li, Y. (2016). “Management approaches for Industry 4.0: A human resource management perspective”. In Evolutionary Computation (CEC), 2016 IEEE Congress on (pp. 5309-5316). IEEE.

Spohrer, J, Maglio, P.P., Bailey, J., Gruhl, D.: Steps towards a Science of Service Systems. Computer, 40, 1, pp. 61–77 (2007).

Spohrer, J.C., Maglio, P.P.: Toward a Science of Service Systems: Value and Symbols. In Maglio, P.P., Kieliszewski, C.A., Spohrer, J.C. (eds) Handbook of Service Science. Service Science: Research and Innovations in the Service Economy, pp. 157–194. Springer, New York (2010).

Teece, D.G., Pisano, P., Shuen, A.: Dynamic Capabilities and Strategic Management. Strategic Management Journal, 18, 7, pp. 509–533 (1997).

Ustundag, A., & Cevikcan, E. (2018). Industry 4.0: Managing The Digital Transformation. Springer.

Wang, S., Wan, J., Zhang, D., Li, D., & Zhang, C. (2016). “Towards smart factory for industry 4.0: a self-organized multi-agent system with big data-based feedback and coordination”. Computer Networks, 101, 158-168.

Zhou, K., Liu, T., & Zhou, L. (2015). “Industry 4.0: Towards future industrial opportunities and challenges”. In Fuzzy Systems and Knowledge Discovery (FSKD), 2015 12th International Conference on (pp. 2147-2152). IEEE.



 
Contact and Legal Notice · Contact Address:
Privacy Statement · Conference: R&D Management Conference 2019
Conference Software - ConfTool Pro 2.6.129+TC
© 2001 - 2019 by Dr. H. Weinreich, Hamburg, Germany