Organizational Wisdom: Impact on Museum’s Innovation

Wisdom is a concept rooted in philosophical sciences. The idea of practical wisdom (phronesis), whose author was Aristotle, is promoted in management sciences (Reeve, 2002). It is understood as a lasting disposition to act, consisting of the proper selection of means for a properly identified goal of human action (Leś, 2016, p.127). Nonaka and Toyama (2007) define the concept of phronesis as a political judgment and identify it with "the ability to initiate future actions, based on a common consensus on specific goals and means, achieved through the shared judgment and belief of individuals in any context." The practical aspect of wisdom in this approach indicates its purposefulness and propriety of action, which, in philosophical sciences, is often presented as ethics (Chemhuru, 2017) or aspects of spirituality (Rocha, et al., 2020). However, research shows that such an interpretation of the purposefulness of action makes it difficult for managers to understand what OW is (Zaidman, et al., 2011). Other researchers identify the direction of purposefulness with values, which is closer to the perspective of management sciences (Kitcher, 2016).

OW is presented in the literature as a multidimensional concept that encompasses different aspects. In particular, it is defined as the ability to integrate knowledge and values to do things right (Rowley, 2006); a dynamic complex system that includes learning, reflection, and the ability to deal with biases to make effective and ethical decisions (Hays, 2007); the competence of a company to use the knowledge resources acquired through human judgments, ethics, intuition, emotional virtues, and actions aimed at effective decision making (Kessler, et al., 2007); or the ability to make judgments (Bierly III, et al., 2000). When synthesizing the definitions of OW, one can observe the features identified by researchers, particularly the fact that it is inextricably linked to learning and knowledge management in the company (Bierly III, et al., 2000), and that it surpasses knowledge in its reflective nature (Kessler, 2006). Another key element that runs through the definitions of OW is the practical application of knowledge. The decision-making process and the orientation toward success expressed through the concept of effectiveness determine the direction in which the phenomenon of OW should be understood in practice.

The new research trend promoted in this study defines OW in two dimensions: OL and AC. This concept of OW, proposed by Pierścieniak and Stelmaszczyk (2020), constitutes a new approach to defining OW and aims to generalize scientific knowledge on OL and AC, that is, mechanisms of acquisition, assimilating, processing, and using knowledge as a resource in museums.

In the literature, many researchers point to positive relationships between OL and IN. For example, Jiménez-Jiménez and Sanz-Valle (2011), in their analysis of business organizations in Spain, demonstrated that OL affects business performance and IN. Zhou, et al. (2022), examining the IN of public library services, indicated that the most significant impact of OL on IN is the acquisition and application of knowledge. The specificity of OL in relation to knowledge acquisition, its sharing, and adaption to new information and experiences is a mechanism that drives IN. Through OL, new knowledge supports and inspires creativity, strengthening the ability to understand and apply innovative ideas (Bueno, et al., 2010). As research shows, creativity is supported by wisdom (Kalyar, et al., 2018).

OL is analyzed from two perspectives, namely exploratory and exploitatory learning, initially created by Match (1991). As Li and Yeh (2017) noted, exploratory learning is the acquisition and use of new knowledge that goes far beyond the company's previous experience. Current research in this area focuses on the search for innovative ideas and alternatives to current activities, considering techniques, new technologies, and potential customer needs that evolved due to the COVID-19 pandemic. However, explicatory OL is understood as the use of knowledge from well-defined solutions that are generally associated with the company's applied solutions, existing experience, and knowledge (Li, et al., 2017). This dimension reflects the processes of using knowledge to create new products and marketing ideas that museums were forced to implement during the pandemic, when customers, visitors, could not leave their homes. However, it was necessary to implement strategic goals, the achievement of which determined the subsidies accessible to their operations.

AC is an abstract category that was created in the early 1990s. Most authors define it as activities that start from recognizing and acquiring valuable external knowledge through its assimilation and processing by the organization to using the new knowledge to achieve business goals (Stelmaszczyk, 2024). It is used to describe phenomena related to the application of knowledge that lead the company to achieve its goals. This ability allows companies to adapt to environmental changes, explore new opportunities, integrate knowledge with IN processes, and increase the organization’s ability to adapt to changes (Prihatna, 2023).

In the research on museums, existing work processes were used to adapt to the changes brought by COVID-19. This approach enabled the recognition of new opportunities that enhanced museum offerings and supported the development of new technologies for emerging services (Stelmaszczyk, et al., 2024).

Regarding the research by Cohen and Levinthal (1990), it was found that the processes defined in the AC framework enhance knowledge utilization, which is crucial for their IN potential. The influence of AC on the company's IN is also confirmed by Vázquez, et al. (2019) while examining industrial sector firms on the northern border of Mexico. Whether Mao, et al. (2021) examined the context of sustainable IN to demonstrate its significance in driving sustainable entrepreneurial activities. OL transforms AC into IN in Spanish small and medium enterprises (Sancho-Zamora, 2022).

Studies have also highlighted the mediating role of AC in various IN-related relationships. For example, AC mediates the relationship between knowledge sharing and IN performance (Zhao, et al., 2020), between digital transformation and corporate IN (Wang, 2022), between information technology (IT) capabilities and open IN performance (Wu, et al., 2019), as well as between external knowledge acquisition and product IN (Akram, et al., 2020). When analyzing the context of the cited studies, it is worth noting the diversity of the environments in which the researches were conducted and the broad contextual spectrum of INs, namely, from product IN to enterprise IN.

Each organization, such as a museum, implements its strategic and operational activities. Strategic management involves setting priorities for strategic activities and goals, obtaining information and opinions on existing and future strategies, adapting organizational processes to the intended strategy, and allocating budgets and investment expenditures (Porter, 1991). These elements significantly influence IN practices, including IN in process design (Rismayani, et al., 2023). Strategic capabilities provide a framework for their development, including situational analysis, strategy development, implementation, and control (Ruchiwit, et al., 2019).

Operational capabilities are responsible for the proper daily operations, which bring organization (museums) closer to achieving their strategic goals. As Koufteros (2014) noted, they can create some elements of competitive advantage, the essential components of which are more productive, innovative and integrated processes and higher employee productivity. Operational capabilities are vital to increasing a company's competitive advantage and performance, especially in automotive and electronics industries (Tamam and Abdurahman, 2018). Successful companies are characterized by their ability to deliver operational excellence while taking a systematic approach to IN management, emphasizing the interconnectedness of operations and IN management (Saunila and Jong, 2022).

OW is desired as it helps in identifying knowledge work processes such as OL and AC in every organization. In every organization, both strategic and operational strategic capabilities can also be identified. They are intermediaries that transform knowledge resources through strategic and operational activities into INs. In assuming such transfers, the research problem described in questions RQ1 and RQ2 thus concerns the influence of OW on INs through strategic and operational OCs.

The conceptual research model (Figure 1) is analyzed to create and implement museum INs during the COVID-19 pandemic. In times of significant changes in a given market condition, cultural institutions had to demonstrate OW. Using available strategic and operational capabilities, museums focused on INs that helped them survive the difficult period. During this period, museums had to demonstrate statutory activity, a prerequisite for receiving government subsidies. The critical direction, the consequence of changes for customer, was IN. The course of the cited structure of events is presented in Hypothesis H.

Figure 1.

Conceptual framework

(Source: Author’s own elaboration)

H: OW positively impacts IN and OC by mediating this relationship.

In the empirical part, a quantitative approach was used, which resulted from the nature of the research problem. The survey research method was used, and it involved computer-assisted telephone interviews (CATIs). The research tool was a questionnaire with a 7-point Likert scale. Each response was assigned a numerical value from 1 (strongly disagree) to 7 (strongly agree).

The scope of the investigation was reduced to the concept of OW. An attempt was made to explain the impact of OW on IN in museums. The subjective scope was museums. The study adopted the definition of a museum, based on Article 1 of the Act on Museums of November 21, 1996. Accordingly, a museum is "an organizational unit not aimed at making profits, but whose purpose is to collect and permanently protect human natural and cultural heritage of material and nonmaterial nature; to inform about the values and content of the collected collections; to disseminate information regarding the basic values of Polish and world history, science and culture; to shape cognitive and aesthetic sensitivity and to enable the use of the acquired collections" (Dz. U. 1997 Nr. 5 poz. 24, art. 1). The spatial scope covered the territory of the Polish state, which means that the study covered museums operating in the Republic of Poland. Empirical research was conducted from June 14 to July 6, 2021.

The general population included 236 museums registered with the National Institute of Museums and Museum Protection (Państwowy Rejestr Muzeów, 2021). The sampling calculator was used and adopted to calculate the minimum sample size: the number of museums in the general population (N) was 236, the estimated fraction (P) was assumed at 50%, the maximum estimation error was set at 3%, the significance level (α) was 0.05, and the z-value reached the level of 1.96.

The minimum number (required) of entities is 193 museums. The recommendations of other researchers are taken into account. According to Comrey (1988), for example, 200 observations are considered an adequate number. In the case of a study conducted in one country, among institutions with a small number of subgroups (one to nine), the recommended sample size is 200–500 observations (Zakrzewska-Bielawska, 2021). Taking into account these recommendations, the optimal size of the representative research sample was assumed to be 200 museums. They were randomly obtained from the general population. The sample implementation rate (response rate) was 84.7%.

While determining the structure of the research sample, it was found that the oldest museum was founded in 1805, and the youngest in 2021. The study included museums with branch offices (33.5%) and those without them (66.5%). The largest group consisted of local government museums (77%), followed by state museums (12%), private museums (6%), and those that did not fit into the above categories (5%). Most of the institutions surveyed, up to 58.5%, were interdisciplinary museums in terms of collection coherence, while museums with homogeneous collections accounted for 31.5%. The characteristics of the sample also included adherence to the main disciplines of the study. The most significant number of museums were history museums (39%), archaeological museums (20%), ethnographic museums (10.5%), and the category “other” (10%) had similar numbers.

The empirical study considered three types of variables. The independent variable focused on OW, and for measurement, the study used the AC scale created by Flatten, et al. (2011) and the OL scale created by Atua-hene-Gima and Murray (2007). A total of 18 measurement indicators (see Table 1) helped assess the integration and ingenuity as a part of OW. The study used OCs as the mediator. The measurement relied on the scale proposed by Koufteros, et al. (2014). Respondents evaluated the strategic capabilities and OCs of the studied museums. In the strategic context, the focus included aligning museum goals with environmental changes, implementing INs, and acquiring resources for innovative projects. On the operational side, the study emphasized engaging employees in IN creation, leveraging technology to support these processes, and using performance metrics to evaluate the implementation of innovative initiatives.

The dependent variable examined museum INs using the Camarero and Garrido (2012) scale. The researchers examined how visitors received changes in museum offerings and whether other museums viewed new products as models to follow. It also used new digital technologies to create, share, and promote museum products.

The following statistical analysis methods were used in the study: exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), as well as structural equation modeling (SEM) and bootstrap method. Statistical analysis was conducted in IBM SPSS AMOS 27.

In the first stage of statistical analyses, EFA was used. It was carried out using the maximum likelihood (ML) method with oblimin rotation. EFA was used to check the unidimensionality for the construct of OW. Thanks to EFA, the construct structure of the studied model was established.

The result of EFA was the extraction of the main elements of OW. The first element was built with 13 measurement indicators. After summarizing their meanings, knowledge integration was named and recognized as a reflective character. The factor loadings of the individual measurement indicator accepted values of 0.455-0.966 (Table 1).

The next element was constructed using only one measurement indicator (Item 75 in Table 1). Its factor loading was 0.386. This measurement indicator was removed because its factor loading value did not reach the assumed threshold of 0.4 (Steiner, 1994). Therefore, this factor was not classified as a structural OW element and was excluded from further analysis.

After summarizing the next element (OW2) and defining its meaning, this element was named knowledge integration and recognized as a reflective character. The factor loadings of the five scale elements that describe it ranged between 0.480 and 0.738 (Table 1).

In the second stage, CFA of OW was conducted. This analysis was performed to confirm the two-factor structure of the constructed studied. Initially, it did not fit the empirical data well (χ2/df = 3.07; Comparative fit index (CFI) = 0.894; Root mean square error of approximation (RMSEA) = 0.102; Standardized root mean square residua (SRMR) = 0.055). Therefore, additional relationships between residuals were considered based on modification indices. The result was that the adjustment measures of the two-factor OW model were satisfactory: χ2/df = 2.16; CFI = 0.943; RMSEA = 0.076; SRMR = 0.048. The standardized regression coefficients ranged from 0.52 to 0.89 (Table 2). They indicate statistically significant and positive relationships between individual measurement indicators and the structural factors of OW assigned to them. The CFA results confirm the reliability of the applied knowledge integration (OW) and ingenuity (OW2) measurement scales.

The reliability of the OW measurement tool constructed in this way was also assessed using Cronbach’s α coefficient and the composite reliability (CR) coefficient. In the case of OW1 and OW2, Cronbach's α coefficient assumed a satisfactory value of 0.944 and 0.804, respectively. The value of this coefficient for all OW was also acceptable and was above the accepted threshold of 0.7 (Table 3). Furthermore, OW1 and OW2 assumed appropriate levels of CR. CR was 0.949 and 0.815, respectively. The two-factor reliability of the scale that measures OW was achieved.

A two-factor measurement model of OW was constructed. This form will be included in the structural model that describes the directional relationships between the museums’ OW and their OCs and INs.

In the third stage of statistical analyses, the reliability of the remaining variables included in the research model, that is, OCs and INs, was estimated. For INs, for the entire construct of dynamic capabilities and their second dimension, operational capability, Cronbach's α coefficient assumed the desired value to be above 0.7. However, with regard to strategic capability, the scale reliability was slightly lower, but still acceptable. Detailed results are presented in Table 3.

The independent variable, mediator, and covariate were measured using the usual alternative data after passing the Harman one-factor test. It turned out that the common method of standard method bias (CMB) was not applicable. The factor with eigenvalue (eigenvalue 14.33) of 42.14% did not meet the criteria and was removed, as it explained less than 50% of the variance of the specified construct.

The concepts of OL and AC adequately describe the phenomenon of OW. However, the confirmatory analysis showed that the OL and AC elements, based on which the scales were developed, created new dimensions. The analysis showed the similarity of knowledge processes included in the OL and AC scales. These concepts were not defined consistently but separately and were treated as different organizational processes. Meanwhile, both activities, relying on the definition of OL and AC, concern knowledge processing and often occur simultaneously in practice, thus making their separation difficult. The combination of both OL and AC has helped organize them, while the confirmatory analysis has identified them as two elements of OW. Knowledge integration (OW1) encompasses activities related to the assessment, adaptation, integration, and strategic implementation of knowledge, which requires deep reflection on the available resources and the future needs and goals a museum sets for itself. This group can be described as reflective, which means that assigned activities focus on deep understanding, analysis, and adaptation of acquired knowledge in the context of the organization’s existing resources, goals, and needs of the organization.

The reflective aspect of OW1 means constantly analyzing, thinking critically, and adapting new knowledge to best serve the organization’s long-term goals of the organization. Reflection related to goal orientation is a premise for solving problems with orientation for success (achieving the goal).

Ingenuity (OW2) as an organizational characteristic means the organization’s ability to think creatively and innovatively and to use its resources and knowledge effectively.

An organization characterized by ingenuity can quickly adapt to changing market conditions and implement new arrangements to solve encountered problems. Its cognitive nature consists in actively seeking, absorbing, and creatively using knowledge and information. This allows the museum to respond effectively to the changing needs of visitors, introduce new ideas and technologies, while maintaining its attractiveness and competitiveness.

In summary, OW, which is derived from the concepts of OL and AC, is a construct of two elements: knowledge integration (OW1) and ingenuity (OW2). These are organizational activities aimed at practical problem solving.

The confirmation of Hypothesis H provides the basis for concluding that OW positively impacts IN, with OCs (strategic and operational) mediating this relationship.

INs in the museum are initiated through the mediating role of strategic capability. Strategic activities cause museums to change their offerings and introduce new services. Analysis of the measurement scale of the OC variable (Koufteros, et al., 2014) indicates that establishing specific strategic goals is required. Such a strategic premise is, for example, maintaining the implementation of statutory activities by museums in Poland during COVID-19. This enabled the setting of priorities for activities when implementing INs tailored to current customer needs (Pierścieniak, et al., 2024). However, operational capabilities are also necessary additions to strategic capabilities because they allow the finalization of strategic ideas. According to the scale for the OC variable (Koufteros, et al., 2014), activities such as employee participation, organizational solutions, or technology implementation allow organizations to prepare and implement the given strategy. Statistical verification of the hypothesis allows us to conclude that the impact of wisdom on IN requires a strategy and operational activities that can be implemented in museums.

The value of the presented work, from a scientific perspective, clarifies the concept of OW, understood as a combination of OL processes and AC. Identifying the two key elements, knowledge integration and ingenuity, allows researchers to better understand the complex processes of knowledge management. Another essential aspect is to indicate the direction of the analysis. Creating the concept of OW (from specific to general) is of a generalizing nature of previous knowledge. The very concept of OW can be treated as a metaconcept that connects fragmented knowledge and duplicate elements.

This new knowledge contributes to the ongoing discussions on the definition of OW (Small, 2004). Integrating wisdom into management practices leads to more sustainable and ethical decision making, while fostering a culture of continuous OL and adaptation (Pinheiro, et al., 2012). This integration strengthens organizational resilience, enabling the organization to address complex challenges and promote IN in an ever-changing environment.

Another critical aspect of this research involves simplifying the complex, abstract theories of OL and AC, aligning with the call to make scientific knowledge more accessible to practitioners (Yazdani, et al., 2020). This approach aims to enhance practitioners' understanding and application of these concepts in real-world settings.

Moving away from metaphysical elements and focusing on reflexivity gives space for including values as a key element of OW. Although these factors did not appear as variables in the study, it is worth noting that they indirectly surfaced in knowledge-related activities. Using valuable knowledge in OL and AC provides a basis for evaluating an organization's data from the perspective of its fundamental values in economic, social, or environmental area. Viewing values as the context for all activities related to the survival of knowledge in problem solving will allow managers to understand their strategic nature. This enables individualized interpretation of values in a problem-solving process based on OW.

The research and analyses conducted in the study have limitations. Apart from the poorly diversified research group (museums in one country), the main limitation is the expert interpretation of the confirmatory analysis for the variable "organizational wisdom" (OW).

Further research should aim to qualitatively identify the problem in the business environment. Another limitation is the lack of a significant value element in the definition of OW. Including values in the content of the definition of wisdom will allow for a better understanding of this concept.

Future research will focus on developing a definition and model of OW, grounded in quantitative studies. This research aims to identify the process through which employee knowledge transforms into successful problem solving while considering the role of values in this transformation. Another research direction involves exploring the drivers and barriers that support this process or examining the impact of OW on various aspects of organizational functioning, such as employee satisfaction or the implementation of circular economy practices.