Learning and knowledge transfer by humans and digital platforms: which tools best support the decision-making process?

2.1 Innovation in Industry 4.0

Technological innovation is the greatest source of competitive advantage and is essential for the success of an organization and for its survival (Del Giudice et al., 2010; Egbu, 2004): it is a guide for competitive behavior. In organizations, the implementation of evolutionary processes is mainly based on technological progress and managerial innovation, knowledge and best practice (Chesbrough, 2006).

According to Kim et al. (2012), various authors have provided classifications of innovation in digital content services, such as Freeman and Hagedoorn (1995), Jenkins (2001) and Nordmann (2004), who link the divergence and convergence of existing services into new creative services under the concept of incremental innovation. This allows the application of a single existing service to other content or platforms to create new services and also facilitates recombinative innovation, i.e. the merging of existing services to produce new ones.

In Industry 4.0, companies acquire a stable and lasting competitive advantage through implementation of enabling technologies because this process produces an enhancement of strategic knowledge related to intellectual capital (Trequattrini et al., 2021). In particular, efficiency and productivity rise through the use of analysis and algorithms, and at the same time, the marginal costs of production and sharing of several products and services are almost entirely eliminated (Rifkin, 2014).

For success, the abilities of machines and humans need to be combined through proper management because, in some activities, machines perform better than humans; in others, humans are better than machines (Brynjolfsson and McAfee, 2014).

The creation of Big Data can be regarded as a process that makes it possible to obtain knowledge from large quantities of data (Harlow, 2018). Furthermore, digital platforms are powered by Big Data: today, the trend is the commercial exploitation of such data acquired from customers (Srnicek, 2017). According to Constantinides et al. (2018), digital platforms are a set of digital resources (also services and content) that enable value-creating interactions between external producers and consumers. They simplify the link between supply and demand (Baldwin and Clark, 2000; Gawer, 2014). Platform business models make a firm more efficient. Human intelligence, which is used to link demand and offerings, can be replaced by artificial intelligence and/or collective intelligence. Hence, feedback can be collected in real time and limited internal productive capacity can be replaced with the illimited external capacity of a community of platforms (Bagnoli et al., 2018). Platforms facilitate mix-and-match innovation (Garud and Kumaraswamy, 1995). In the next paragraphs, we seek to analyze how innovation, digital platforms in particular, can function as useful tools in learning and the transfer of knowledge.

2.2 Formal and informal learning in the knowledge transfer process

According to a knowledge-based view, knowledge is the most strategic and significant resource of a firm and the source of competitive advantage (Alavi and Leidner, 2001). Knowledge as “information with meaning that exists within the individual […] occurs either as a result of experience, or is generated through thinking or reasoning; otherwise, it remains mere data or information” (Beesley and Chalip, 2011).

Organizations can be seen as social communities specialized in the efficient creation and transfer of knowledge (Kogut and Zander, 1992). “Organizations learn only through individuals who learn. Individual learning does not guarantee organizational learning. But without it no organizational learning occurs” (Senge, 1990, p. 139). Therefore, the knowledge transfer capability of organizations is a critical determinant of sustainable competitive advantage (Grant, 1996). Advanced organizations manage knowledge, which means that they strategically build, transform, organize, deploy, use and transfer knowledge (Wiig, 1993).

Argote and Ingram (2000) stated that “knowledge transfer in organizations is the process through which one unit (e.g. group, department, or division) is affected by the experience of another”. Knowledge transfer has also been deeply analyzed in the context of groups or teams. Interestingly, the extent of the knowledge and skills of members belonging to a group change after training that supports knowledge transfer activities (Argote and Fahrenkopf, 2016; Lapré and Van Wassenhove, 2001; Salas et al., 2012). Knowledge is transferred between actors (individuals and organizations) in several ways (Nakauchi et al., 2017): through social practices (Von Krogh, 2012) and via both tacit and explicit means (Nonaka and Takeuchi, 1995).

The workplace is a site of knowledge learning, which in turn facilitates the development of the organization through enhanced production, effectiveness and innovation and which creates individuals with enhanced knowledge skills and learning capacities (Boud and Garrick, 1999). The literature asserts that the level of workplace learning is directly linked to the efficacy of both formal and informal learning (Poell, 2013).

Formal workplace learning is characterized by a prescribed learning framework or a designated trainer, whereas informal learning occurs in situations that are not usually intended for learning or where learning is not the primary goal of the activity (Eraut, 2000). Informal learning requires a blending of individual differences, e.g. intellectual curiosity, self-directedness and self-efficacy (Beckett and Hager, 2002). Informal learning also includes contacting individuals possessing higher levels of insight or competence on a topic (Werner and Dickson, 2018).

Formal and informal learning are complementary and contribute to the efficient workplace learning of individuals (Poell, 2013). Previous research studies have focused on formal learning opportunities among employees, such as face-to-face learning events with a designated teacher or trainer (Eraut, 2000). Although informal learning is considered a more effective learning style (Boud and Garrick, 1999; Garrick, 1998), only a few scholars address this mode of learning in the workplace (Manuti et al., 2015; Marsick and Volpe, 1999). Moreover, informal workplace learning is positively correlated with flexibility, employability, adaptability to context, rapid transfer to practice and resolution of work-related problems through regular review of work practices and performance (Dale and Bell, 1999).

A growing number of social networks and other Web 2.0 and Web 3.0 services can be used for flexible and informal learning, making it possible to share infinite multimedia learning resources in future Industry 4.0 learning (Tvenge and Martinsen, 2018). In particular, in informal workplace learning, digital platforms can support knowledge transfer; indeed, their use is increasing. They support the placement of key knowledge and value creation activities close to demand and they create new ways of building knowledge and relationships (Nambisan et al., 2019).

Digital platforms produce a competitive advantage through interconnections among partners, sharing of knowledge and risk and exercise of collective power. Such platforms also allow increased numbers of different players to cooperate easily; indeed, they allow for an openness and flexibility that transcends the barriers and boundaries of distance, geography and industry (Lusch and Nambisan, 2015).

The search range is increased, the participation cost for each partner is decreased and tacit knowledge is more effectively codified by network models of intermediation and by platform-based knowledge transfer intermediaries enabled by new digital innovations, thus allowing for knowledge transactions on the go (Cahoy, 2020; Dushnitsky and Klueter, 2017; Lee, 2020).

2.3 Learning and knowledge transfer in the sports sector

Knowledge has an important role in sport. Learning and knowledge transfer in cognitive-intensive environments, such as sports clubs (Trequattrini et al., 2018), has been the subject of several research studies (Jones et al., 2003; Potrac and Jones, 2009; Potrac et al., 2007). Sports clubs can be considered communities of practice – vehicles for knowledge creation and sharing in intensive and dispersed settings (Werner and Dickson, 2018). There is a broad consensus that most of the knowledge transfer within sports clubs companies takes place outside formal sporting contexts (Nelson and Cushion, 2006; Stoszkowski and Collins, 2014).

In the sports context, formal learning is identified as the knowledge that managers acquire from sporting contexts, such as training, matches and official events. Informal learning is the use that executives make of the information available on digital platforms and in the fiduciary relationships that management has with the players’ brokers; by far, it is the most frequently used and the most important in the general learning process of executives. The above point is supported by previous studies that demonstrate the significance of informal learning (Boud and Garrick, 1999; Cushion and Nelson, 2013; Garrick, 1998; Mallett et al., 2009).

Artificial intelligence and statistical modeling in sports have become increasingly prominent, with analytic strategies and techniques being quickly developed, depending on the type of sport, the data and the goals of the analysis (Brefeld and Zimmermann, 2017; Chmait and Westerbeek, 2021). O’Brien and O’Keeffe (2022) assert that sporting environments continually change, which means that training protocols and development systems will not remain valid indefinitely. Moreover, the capability of contemporary computer systems to analyze and mimic human-cognitive functions creates unprecedented possibilities for developing digital platforms.

Indeed, an increasing number of clubs use data science tools to make increasingly informed decisions during matches and in the transfer market. According to Trequattrini et al. (2021), digital platforms can support knowledge transfer in sport in many ways; indeed, some platforms can replace DVDs and VHSs, making it possible to examine a player without needing to send a scout to follow him. Other platforms emphasize the relationship between football and Big Data. Still, others allow athletes to develop a real network of intermediaries and companies in the professional or amateur world: coaches, health professionals, journalists and marketing staff.

Concerning the role of knowledge brokers, the literature asserts that knowledge brokers explore, learn and connect a large number of existing problems, creating innovative solutions through the combination of existing ideas (Phipps and Morton, 2013). In the contribution of Phipps and Morton (2013), the main characteristics of “knowledge brokers” have been outlined:

• ability to understand the needs of knowledge recipients;

• ability to communicate effectively;

• ability to identify and acquire knowledge;

• ability to create and manage knowledge networks; and

• results orientation.

Knowledge brokers work collaboratively with key stakeholders to facilitate the transfer and exchange of information in a given context (Bornbaum et al., 2015). Sports management learning is enhanced by knowledge brokers, a role typically assumed by those who are called “sports agents”, who act as representatives of the athletes and who usually advise the executives of sports clubs about the recruitment of certain players rather than others (Willem et al., 2019). However, few researchers have investigated the role of these knowledge brokers (Girginov et al., 2015) and there is a lack of understanding about the extent to which these brokers are able to enhance learning in sports communities and ensure a process for the creation and sharing of knowledge.

Effective management of high-performance athletes and teams to build competitive advantage is receiving increased practitioner interest (Sotiriadou and Shilbury, 2013). However, research is relatively sparse and further studies would help in better explaining the complexity of managing athletes and the decision-making process required to identify and recruit athletes for high-performance teams.

Instead, when it comes to learning and knowledge sharing, sports management scholars focus primarily on the role of the coach and on the knowledge transfer skills between players (Cassidy et al., 2006; Coté and Gilbert, 2009; Kellett, 1999; Sotiriadou and Shilbury, 2013; Werner and Dickson, 2018). The form of knowledge transfer typically analyzed relates to peer learning (Sotiriadou and Shilbury, 2013; Werner and Dickson, 2018). Werner and Dickson (2018) argue that this form of learning constitutes an important way of transferring knowledge within the sporting context, promoting the development of motor, cognitive and social skills and encouraging sharing of knowledge and experience among the individuals involved. Furthermore, the authors argue that clubs should develop an organizational culture that supports knowledge sharing, where their players are encouraged to learn from each other and actively seek to share their knowledge. The learning culture positively impacts productivity and economic growth, leading to the greater motivation of people to learn and develop their skills, increasing their productivity and contributing to the economic development of sports clubs. Previous research has also focused on formal learning opportunities among employees, such as face-to-face learning events with a designated teacher or trainer (Eraut, 2000). Although informal learning is considered a more effective learning style (Boud and Garrick, 1999; Garrick, 1998), only a few scholars investigate informal learning in the workplace (Manuti et al., 2015).

To fill the gap in the literature and to highlight the importance of informal learning, our research aims to investigate whether players’ brokers or digital platforms are more useful in the managerial decision-making process of identifying and recruiting athletes for high-performance teams.

Moreover, in the sporting context, studies of the learning and knowledge transfer between sports managers and their brokers have mainly focused on the football industry, with other sports receiving limited attention. With this in mind, our research seeks to fill this gap by analyzing the basketball sector.

Taking the aims of this paper together with the analysis of the previous literature, we derived the following research question:

3.1 Research context

Since 2006, the European Union has recognized the significant contribution of sport to the continental economy, not only as an engine of growth and employment but also for its positive effects on citizens’ health, territorial development, tourism and social integration. According to a report edited by Ifis Bank (2022), the sport system is a sector of value for the Italian economy and society, thanks to the contribution of the four subsectors that make it up:

1. manufacturers of sports clothing, equipment and vehicles: 10,000 companies generate over €17bn in revenues;

2. professional and amateur sports clubs and facilities management: 74,000 companies have an annual turnover of €46bn;

3. sports media: expenditure linked to events and sports betting generated almost €23bn in one reference year; and

4. social value: €10bn is generated each year as an indirect economic effect, contributing 0.56% of the Italian GDP.

The last available edition of the Italian Basketball Federation’s Social Responsibility Report 2018–2020 discloses that sports associations and clubs produce annual revenues of over €40bn in an average year, 10% of which is from professional clubs alone. In this context, the importance of sports disciplines is affirmed, and in Italy, we find basketball as the third most practiced sport with 346,144 participants registered in various capacities with the Italian Basketball Federation, including athletes, children, coaches, physical trainers, managers, referees, field officials and youth instructors.

Furthermore, from an economic point of view, the impact of basketball is decisive for the Italian economy, with a global turnover of the entire top division of A1 equaling €77m, a figure which positions it as the professional sport with the second-highest number of investments by companies operating in Italy.

The Italian Basketball Federation recognizes the A1, A2 and B series championships as categories of national interest. Series B represents the championship with the largest number of teams involved, divided geographically by contiguous regions. We decided to investigate Series B because it provides a homogeneous sample of as many sports clubs as possible. Having examined a national championship, the information collected still reflects indications from top professionals in the sector.

The Italian Series B basketball championship is the third level of the reference sport in the national context. Sixty-four teams compete in the tournament, divided into four groups of 16 teams. The season runs from September to June, with most games played on Saturdays and Sundays. Series B represents the competition with the largest number of clubs participating in a single championship in the panorama of Italian basketball, as well as being the competition with the largest number of athletes and managers.

3.2 Sample and data collection

The sample consisted of the 64 sporting directors of the clubs participating in the 2021–2022 championship, most of them widely known throughout Italy. The construction of the reference universe and the identification of the relevant electronic addresses (e-mail) commenced with the list surveyed provided by the Lega Nazionale Pallacanestro (www.legapallacanestro.com) (first accessed in September 2022), an association that manages the A2 and B series national championships and was updated with the help of the website of the organization that coordinates the activities of the affiliated companies.

3.3 Data collection

The data were collected through questionnaires presented in the form of structured written interviews. This form was chosen because it was considered most suitable for conducting the research and because ease of answering allowed for greater involvement of the participants. In any case, this method helps in comparing cases, structures or events (Bryman, 2008) and facilitates comprehensive interviews within a limited time frame (Patton, 2002).

To seek a meaningful vision of the context in which executives, athletes and professionals of sports clubs live, play, interact and interpret their construction of the technical-sports project, the questionnaire was structured into four sections (Table 1). The total number of questions administered was 21.

The questionnaires were produced in Italian. The survey was carried out in 2022 using the Google Forms Webmail application, and the results were subsequently reprocessed using a spreadsheet. The questions were structured to ensure that the participants’ important thoughts and perspectives were appropriately captured (Patton, 2002). All respondents were informed that their names and clubs would be removed from the transcript. This was done to encourage respondents to provide information about sensitive issues, such as knowledge sharing between agents and companies and the possible role of digital platforms in this brokerage relationship, which might otherwise have been withheld (Marshall and Rossman, 2011). From the 64 questionnaires sent, a total of 34 responses were received. The research participation rate was, therefore, determined as 53.13%.

3.4 Data analysis

Thematic analysis was used for the data analysis. This is a method that searches for themes or patterns in qualitative data. Thematic analysis is very flexible, as it can summarize the key characteristics of a large body of data and can also generate unexpected insights into the topic. Moreover, thematic analysis is also very useful for highlighting similarities and differences in the data set (Braun and Clarke, 2006). Due to the lack of preexisting research into knowledge sharing between leaders, agents and platforms available to clubs, an inductive approach was adopted. We followed the step-by-step thematic analysis guide from Braun and Clarke (2006). The guide has six phases:

1. familiarization with the data by reading and rereading the transcripts;

2. initial coding;

3. research of the themes;

4. review of the themes to create a thematic map of the analysis;

5. defining and naming the topics under analysis; and

6. production of the report. In an iterative process, the codes and general themes were revisited, double-checked and discussed by the research group to ensure the reliability and consistency of the classification procedure (Bryman and Bell, 2007).

4.1 Essential characteristics of the construction of a technical sports project

In the first part of the questionnaire, we wanted to investigate the procedural process that sports directors follow to structure a new sports project. By technical sports project, we mean the programing work aimed at achieving a certain sporting result. Specifically, as regard its implementation, it is important to take into account some important variables, such as:

• identification and concrete delimitation of the specific problem or problems of the technical sports structure;

• exhaustive research and collection and analysis of what is deemed necessary information;

• goal construction, i.e. understanding the goal to be achieved with the project in question;

• research that can be undertaken and choice of possible alternatives;

• approach to the actions, tasks and resources, both technically and economically, which can be used for the implementation;

• start-up, i.e. execution of the selected proposal or alternative;

• periodic evaluation of the work done and the progress achieved; and

• voluntary disclosure in making the results obtained from the project known to the participants.

Forty-seven per cent of the interviewees chose the identification of shortcomings in sports structure as the first training step, 29% prioritized identifying the characteristics of the athletes to be engaged, whereas about 24% considered deciding the list of athletes to be engaged was the primary task (Figure 1).

According to most of the interviewees, the role of sports agents was considered relevant and essential, as only 17.6% of respondents gave an answer of less than 3 on a scale of 1–5. This finding is confirmed when one considers that managers themselves have digital platforms as a means of acquiring knowledge, which implies technology is not capable of decreasing the influence of sports agents in the selection of athletes (Figure 2).

Managers also consider the trust-based relationship established with a specific agent essential for scouting for and choosing athletes. In fact, only 17.6% gave an answer with a value of less than 3 on a scale of 1–5. Furthermore, if they did not have a good relationship with a particular agent, managers identified the coach as the person capable of increasing the quality of the brokerage relationship. In fact, 73.6% of the interviewees gave an answer with a value greater than 3 on a scale of 1–5. By contrast, sports directors tend to use the current platform containing statistical and technical information on athletes participating in the Italian Series B championship to get to know the athletes, rather than for the purpose of scouting and selection (Figure 3).

4.2 Use of digital tools applied to sport

With regard to the use of digital platforms, the first objective was to investigate the level of satisfaction with the LNP platform, which contains statistical and technical information on the athletes participating in the Italian Series B championship and which is freely accessible to clubs. In response, 61.8% of sports directors seem satisfied with the information available on the platform (Figure 4).

However, despite the fact that digital tools are found to be a great means of support for the management of sports clubs, 50% of respondents believe that such platforms are not able to fully replace the brokerage relationship between agent and club in the acquisition of the athletes.

Furthermore, 94.1% of respondents would find useful access shared with all clubs to a platform containing more information than that which is already available. The platform should be usable not only by the technical staff but also by the club’s management.

4.3 Useful information contained in the digital platforms

Although there is already much technical information about athletes available on the market, the sports directors of the clubs believe that this information is in some ways insufficient. In fact, 44.1% of the interviewees believe that it would be useful if the platforms contained additional information about character traits, 41.2% wanted news on players’ private lives and 14.7% sought additional information of a technical nature (Figure 5).

Beyond mere technical information, further data that sports directors would consider useful for their choice of athletes included the performance and physical characteristics of athletes currently not available in any database. Nevertheless, for 58.8% of the interviewees, if this information was available to the technical staff and the management of the clubs, it would in no way be preferable to the manager’s relationship of trust with the athletes’ agent.

Furthermore, 100% of the interviewees would consider it appropriate for the league to offer clubs a platform that allowed them to view, in real-time, the possible acquisition of athletes by competing clubs to understand which athletes are actually available on the market.

4.4 Benefits and drawbacks of the use of digital platforms

Despite the information already available on the athletes and any data that could be integrated, 55.9% of the interviewees would in any case accord greater reliability to the knowledge provided by the athletes’ agents. Consequently, 67.6% of respondents believe that in no case can the figure of the agent be fully replaced by a digital platform, albeit complete in all its forms in the data provided to sport managers. However, 88.3% of respondents tend not to consider certain players if the latter are assisted by an agent with whom the management of the company does not have a good brokerage trust relationship.

The interviewees believe that additional information contained in the platforms could enhance the brokerage relationship (Figure 6).

Finally, for 76.5% of sports directors, the use of platforms by management and technical staff does not in any way affect the relationship of the company with the athletes and the companies’ stakeholders.