Technology and Organization

???????????Technological development allows firms to unlock new solutions that can significantly increase the performance of their operations: for example, the increase in efficiency for energy-intensive productions (Escobar and Vredenburg, 2011), or the greater control on complex manufacturing processes allowed by Industry 4.0 and digitalization, which can extend traceability through the supply chain and internal processes, thus allowing a higher quality, a higher respondence to customer requirements, lower downtime of equipment due to better information on its functioning and better efficiency in the usage of energy and consumables which can create a cost advantage on competitors (Felsberger et al., 2022).

??????????? For the adoption of these technologies to succeed, it must be accompanied by commitment to concurrent organizational innovations. According to literature, organizational rationalization is a precondition for successful adoption of new technologies: this rationalization must be accompanied and followed by a mapping of the activities, in order to foresee the impact of technological innovation on the business model (Zangiacomi et al., 2020). Such mapping requires to look at the organization with open mind, ready to discover new dependencies among activities and, since changes are likely going to assume a recursive nature, it is advisable that everything is thoroughly documented (Kretschmer and Khashabi, 2020). The synergy between good organizational practices, such as lean manufacturing, and technological innovation, such as digitalization, is vital to reap the fruits of both: each of the two allows to achieve results that would not be possible when they are practiced independently; therefore valid organizational practices do not become obsolete by technological advancement, but can achieve the maximum yield in terms of operational results and allow technological change to do the same (Buer et al., 2021). In the case of digitalization, the centrality of data collection and analysis can require to the organization to make dramatic changes to its business model: a manufacturer of equipment can become also a software developer, hiring the necessary talents, creating new business units and acquiring the capability to provide services for internal use and even sell them externally, supporting the entrance in this new value chain with new partnerships with suppliers, providers of expertise and complementary services and customers, to which it is possible to offer more customized products based on the data collected (Correani et al., 2020). Such radical changes require letting go of the established understanding and framing of the business model, to make room for a new vision of the organization (Siltaloppi et al., 2021).

???????????Given the fact that adopting a new technology has the potential to impact so deeply in the life of a firm, it has to be performed with a strategic view: the priority, in the case of digital transformation (which includes the creation of infrastructure for storage of data and their processing, AI and machine learning), is to define its goals for the firm, what is the expectation and the scope of the change (Correani et al., 2020). If management lacks the necessary information (which is very likely for innovative developments), a search for appropriate guidance becomes necessary: literature suggests that they co-opt by experts from universities, or specialized partners within the business ecosystem, for example start-ups (Zangiacomi et al., 2020); additional options are the participation to case studies and the absorption of lessons learn from other industries which might have already adopted the target technology (ibid.).

???????????Despite such preparations, it is inevitable that in the short term the organization, including management, will not be fully aware of the ramifications of the technologies it adopts (Kranzberg, 1986). In the case of most recent innovations, however, digital technologies create the conditions for the collection and analysis of unprecedented amounts of data, whose analysis can now be supported by AI (Correani et al., 2020). This means that a lot more elements are going to be available for consideration, not just as raw data but as actionable information that can be considered by management in taking decisions. Management’s guide is still needed in defining which data to collect, as well as to decide if it is the case to set up partnerships to obtain data from the rest of the value chain of which the company is part (including suppliers and customers), keeping in mind that good data can have a place at the core of the competitive advantage of the firm (Correani et al., 2020). Organizational agility is a key to success, because it allows firms to change their processes allowing data flow and accumulation, adopting a holistic approach that involves all business streams, finding technological partners and resources and embarking employees on the change process (Brock and von Wangenheim, 2019).

???????????Information can simplify management’s life in other ways, for example in preparing organizational transformations. Once processes have been mapped and integrated with digital technologies, management will have a precise overview of the workflow, with the possibility to further reorganize it, while being aware of the number of tasks, of their interdependency and the division of the value chain within the firm, as well as among the available resources, including human resources (Kretschmer and Khashabi, 2020). An informed reorganization can be the key to respond better and faster to new business needs and to increase the value created in the company by internalizing or externalizing segments of the supply chain in liaison with customers and suppliers (ibid.). The improved systems of data collection and analysis of information can be used to revolutionize employee performance evaluation (Robert et al., 2022).

???????????In general, the most important characteristics of the firms that succeed in digital transformation are the capability to integrate the flow of information across the company, avoiding that some data remain isolated, and the commitment of management to the change (Brock and von Wangenheim, 2019). For example, data collection is a major obstacle in preparing sustainability reports (Searcy and Buslovich, 2014) because it introduces a need to retrieve information that are spread in multiple organizational units and whose storage was not designed with this new need in mind.

???????????Management should always consider that some negative sides of technologies can also be hypothesized by employees, which must be prepared and trained to their adoption. The new technological knowledge should be spread to middle management and employees, through appropriate coordination and information tools such as committees dedicated to circulation of information about the new technologies, their possible applications, as well as success stories that can motivate the organization in picking up the new tools, along with best practices to increase their effectiveness from the start (Zangiacomi et al., 2020). The engagement of employees is crucial for them to exhibit knowledge sharing and solidarity in the wake of the change: for this reason, it is important that their instances are listened to, accepted and addressed by management (Robert et al., 2022). As a support during the change, KPIs can be set up and communicated across the organization to support the adoption of new technologies and be the basis for incentive systems (ibid.).

???????????There might fears of the substitution of the workers’ by new tools (Pedota and Piscitello, 2022) but the main focus for the employees should actually be on the fact that the intake of new technologies puts them in the condition to elaborate an entirely new way of working, operating a synthesis with their previous processes (ibid.). For example, with greater possibilities for remote coordination also on an international scale and greater empowerment for employees, thanks to the possibility to access database and tools that can assist them in their daily tasks (Kretschmer and Khashabi, 2020). Both managers and employees need to be willing to invest time to allow the change to happen, so that the complementarity between human skills and new techniques and tools can create a better performance, instead of being limited to substitution of certain tasks by technology (Pedota and Piscitello, 2022): the process should be encouraged via proper hiring, training and reorganization of human resources.

???????????As a matter of fact, technological changes require in truth an array of new skills to cover innovative aspects, such as data collection, architecture and security (Brock and von Wangenheim, 2019): such evolution may require years to be completed, also because technological revolutions tend to create a demand for talent faster than the talent pool can provide in the short term (Whysall et al., 2019) and, on top of that, established companies have a branding problem, as they tend to attract the same type of employees they already have, which would be suitable to continue the same way of working but not to adapt to mutated circumstances: a rebranding is necessary to expand the recruitment target to meet the new needs (ibid.).

???????????In conclusion, the answer to the challenge of technological and organizational change requires management to set up a mapping and rationalization of processes. This is destined to be a recursive endeavor, benefiting from the progressive digitalization of the firm. Perhaps the most important initiatives are however the choice of the goals of the transformation, and the cultivation of open mindedness: both in employees during transformation (to welcome the new tools as a possibility to enrich their professional life and eventually even their work-life balance) and in management, which should be willing to dare to look for external expertise on technology from universities and start-ups as well as to let go of the past business model. Such cultivation is going to be a long process, to be made with the long-term target situation for the firm, including a plan for skill building that extends from training employees to hiring strategies.

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References

Brock, J. K.-U., & Wangenheim, F. von (2019). Demystifying AI: What Digital Transformation Leaders Can Teach You about Realistic Artificial Intelligence. California Management Review, 61(4), 110–134. https://doi.org/10.1177/1536504219865226

Buer, S.-V., Semini, M., Strandhagen, J. O., & Sgarbossa, F. (2021). The complementary effect of lean manufacturing and digitalisation on operational performance. International Journal of Production Research, 59(7), 1976–1992. https://doi.org/10.1080/00207543.2020.1790684

Correani, A., Massis, A. de, Frattini, F., Petruzzelli, A. M., & Natalicchio, A. (2020). Implementing a Digital Strategy: Learning from the Experience of Three Digital Transformation Projects. California Management Review, 62(4), 37–56. https://doi.org/10.1177/0008125620934864

Escobar,?L.?F., & Vredenburg,?H. (2011). Multinational Oil Companies and the Adoption of Sustainable Development: A Resource-Based and Institutional Theory Interpretation of Adoption Heterogeneity. Journal of Business Ethics, 98(1), 39–65. https://doi.org/10.1007/s10551-010-0534-x

Felsberger, A., Qaiser, F. H., Choudhary, A., & Reiner, G. (2022). The impact of Industry 4.0 on the reconciliation of dynamic capabilities: evidence from the European manufacturing industries. Production Planning & Control, 33(2-3), 277–300. https://doi.org/10.1080/09537287.2020.1810765

Kranzberg,?M. (1986). Technology and History: "Kranzberg's Laws". Technology and Culture, 27(3), 544. https://doi.org/10.2307/3105385

Kretschmer, T., & Khashabi, P. (2020). Digital Transformation and Organization Design: An Integrated Approach. California Management Review, 62(4), 86–104. https://doi.org/10.1177/0008125620940296

Pedota, M., & Piscitello, L. (2022). A new perspective on technology‐driven creativity enhancement in the Fourth Industrial Revolution. Creativity and Innovation Management, 31(1), 109–122. https://doi.org/10.1111/caim.12468

Robert, M., Giuliani, P., & Gurau, C. (2022). Implementing industry 4.0 real-time performance management systems: the case of Schneider Electric. Production Planning & Control, 33(2-3), 244–260. https://doi.org/10.1080/09537287.2020.1810761

Searcy,?C., & Buslovich,?R. (2014). Corporate Perspectives on the Development and Use of Sustainability Reports. Journal of Business Ethics, 121(2), 149–169. https://doi.org/10.1007/s10551-013-1701-7

Siltaloppi,?J., Rajala,?R., & Hietala,?H. (2021). Integrating CSR with Business Strategy: A Tension Management Perspective. Journal of Business Ethics, 174(3), 507–527. https://doi.org/10.1007/s10551-020-04569-3

Whysall, Z., Owtram, M., & Brittain, S. (2019). The new talent management challenges of Industry 4.0. Journal of Management Development, 38(2), 118–129. https://doi.org/10.1108/JMD-06-2018-0181

Zangiacomi,?A., Pessot,?E., Fornasiero,?R., Bertetti,?M., & Sacco,?M. (2020). Moving towards digitalization: a multiple case study in manufacturing. Production Planning & Control, 31(2-3), 143–157. https://doi.org/10.1080/09537287.2019.1631468