The Mediating Role of Knowledge Sharing on the Relationship Between Competitive Intelligence and Product Development: Evidence from Jordan

There is no shortage of definitions of competitive intelligence. Essentially, this type of intelligence focuses on collecting and analyzing information on market conditions, government regulations, industry competitors, and new products to help organizations attain their goals (Nasri, 2012; Tsuchimoto and Kajikawa, 2022).

A review of the relevant literature and several studies demonstrates that there are many definitions of competitive intelligence. For instance, Kahaner (1998) defined it as “a systematic program for gathering and analyzing information about your competitors’ activities and general business trends to further your own company's goals”. Bergeron and Hiller (2002) proposed that competitive intelligence is “collection, transmission, analysis and dissemination of publicly available, ethically and legally obtained relevant information as a means of producing actionable knowledge […] for the improvement of corporate decision making and action”. Wheelen and Hunger (2012) defines competitive intelligence as “a formal program of gathering information about a company's competitors”. Pellissier and Nenzhelele (2013) suggested that competitive intelligence is “a process or practice that produces and disseminates actionable intelligence by planning, ethically and legally collecting, processing and analyzing information from and about the internal and external or competitive environment in order to help decision-makers in decision-making and to provide a competitive advantage to the enterprise”. Strategic and Competitive Intelligence Professionals (SCIP) defines competitive intelligence as “the process of legally and ethically gathering and analyzing information about competitors and the industries in which they operate in order to help your organization make better decisions and reach its goals” (Scip.org, 2017).

Surveys show that the most critical factor contributing to the growing competitive density in the marketplace is the enhanced capability of competitors (Wheelen and Hunger, 2012). Also, organizations are unable to respond to competitors’ actions such as price change or significant innovation before it appeared in the market (Coyne and Horn, 2008). Therefore, competitive intelligence has become a significant part of the environmental scanning process in organizations. However, literature demonstrated that there are many benefits for competitive intelligence in the organization, such as predicting changes in market condition, determining new or potential entrants to the market, and acquiring knowledge about new technologies, products, and processes that affect the organization's activities (Nasri, 2012; Tsuchimoto and Kajikawa, 2022). Therefore, many scholars call to formalize the competitive intelligence function within an organization to carry out the following tasks which reflect the competitive intelligence steps: “planning and direction, collection of data, analysis and dissemination” (Kahaner, 1998).

Product development is a crucial element of the ability to compete in today's dynamic environment. More specifically, the success of companies in the competitive markets depends on providing customers with new products or features and adding value before competitors do that (Ferreira, et al., 2021).

To many researchers, it is not clear what product development is because researchers use different definitions of the construct. Some researchers have focused on product development from the process lens. From this lens, they have identified “product idea, screen and evaluate, concept development, prototype, testing, and product launch” as sequential steps in the product development process (Chandra and Neelankavil, 2008). Others focus on product development from the strategic management perspective; accordingly, they have identified radical or incremental innovated products as the core of the product development process (Hoonsopon and Ruenrom, 2012). These two perspectives are different, as well the measures used by each are not correlated with one another. Therefore, the focus of this study is product development from a strategic perspective.

To many researchers, it is not clear what product development is because researchers use different definitions of the construct. Some researchers have focused on product development from the process lens. From this lens, they have identified “product idea, screen and evaluate, concept development, prototype, testing, and product launch” as sequential steps in the product development process (Chandra and Neelankavil, 2008). Others focus on product development from the strategic management perspective; accordingly, they have identified radical or incremental innovated products as the core of the product development process (Hoonsopon and Ruenrom, 2012). These two perspectives are different, as well the measures used by each are not correlated with one another. Therefore, the focus of this study is product development from a strategic perspective.

In today's business environment knowledge has become the most important strategic resource (Van Den Hoof and De Ridder, 2004), scholars like (Barquin, 2001; Lin, 2007) describe it as a distinctive competence and performance driver of the organizations. According to Gupta, et al. (2000), the most important part of the knowledge management process is knowledge sharing, it provides the foundation for the success of the knowledge management process as a whole within the organizations (Wang and Noe, 2010).

Knowledge sharing has been defined by many scholars. For instance, Ryu, et al. (2003) defined it as the practice by which an employee transferring his or her obtained knowledge and information to other employees within an organization. Cummings (2003) described knowledge sharing as a processes by which an organization acquires access to its own and other organizations’ knowledge.

This study used a quantitative research method with cross-sectional survey design.

Data were collected via a structured online questionnaire. General managers of chemical manufacturing companies in Jordan were selected as respondents; 178 respondents were selected randomly from the given list of chemical manufacturing companies registered at the Jordan industry chamber.

The target population for this study included chemical manufacturing companies in Jordan. The sample included 178 general managers drawn from the target population. The simple random sampling strategy described by Hair, et al. (2007) was used to select the participants. The sample consisted of 114 males (64%) and 64 females (36%). Approximately 37.6% of the sample were working for 10 years to less than 15 years in their current job group (n = 67). The majority of the sample had a master's degree (78.1%) (see table (1)).

Table (2) shows the means and standard deviations of the measured variables. Participants reported that their organizations highly practice the competitive intelligence activities (M = 3.99, SD = 0.589). Also, participants reported moderate levels of knowledge-sharing behavior within their organizations (M = 3.90, SD = 0.684). Finally, participants reported that their organizations are highly focused on products’ development (M = 4.38, SD = 0.591).

The author performed survey data analyses using the SmartPLS version 3.3.7 software package. Structural equation modeling (SEM) was used to examine the proposed model. More specifically, the partial least squares (PLS-SEM) method was used to estimate the relationship between study variables. The author followed a two-step method in conducting SEM for the data analysis (Hair, et al., 2017). The first step, the measurement model assessment phase, strived to verify the unidimensionality of each construct's measurement items. This phase also included an examination of the reliability and validity of the model's latent constructs (Boateng, 2018). The structural model assessment phase is the final step in the SEM procedures (Boateng, 2018). The aims of this phase include validation of the SEM structural model and examination of latent construct relationships. Figure (1) illustrates the flowchart of the structural equation model steps.

Figure 1

Measurement model assessment includes testing the construct internal consistency, convergent validity, and discriminant validity.

According to table (3), all outer loadings of the reflective constructs’ competitive intelligence, knowledge sharing, and product development are above the determined value of 0.70, which suggests adequate levels of indicator reliability (Hair, et al., 2014). As well, the composite reliability of each construct exceeds the threshold value of 0.7, with values of 0.921 (competitive intelligence), 0.898 (knowledge sharing), and 0.920 (product development). We, therefore, conclude that all constructs have high levels of internal consistency reliability. Moreover, Cronbach's alpha values are above the threshold value of 0.70, which confirms the internal consistency reliability of the model (Hair, et al., 2017).

Convergent validity was measured using average variance extracted (AVE) values. As shown in table (3), the AVE values of competitive intelligence (0.593), knowledge sharing (0.688), and product development (0.624) are above the determined minimum level of 0.50. We, therefore, conclude that the measures of the three constructs have high levels of convergent validity (Hair, et al., 2017).

To test discriminant validity, the Fornell–Larcker criterion method was used (Hair, et al., 2014). As shown in table (4), the square roots of the AVEs for the constructs’ competitive intelligence (0.770), knowledge sharing (0.829), and product development (0.790) are all higher than the correlations of these constructs with other latent variables in the path model. We, therefore, conclude that all constructs are valid measures of unique concepts.

After we have proved that the construct measures are reliable and valid, the next stage handles evaluation of the structural model results. This includes investigating the model's capability to predict and the connections among the constructs.

In the beginning, we need to verify the structural model for collinearity problems by examining the variance inflation factor (VIF) values of all groups of predictor constructs in the structural model. As shown in table (5), the values of VIF are less than the determined value of 5 (Hair, et al., 2014). Thus, the collinearity issue does not exist between the predictor constructs in the structural model, so we can keep investigating the results.

Hence, after reliability and validity are established and collinearity between the predictor constructs does not exist in the structural model, the next step addresses the evaluation of PLS-SEM results. The essential assessment criteria for the quality of the PLS path model estimations include the coefficients of determination (R²) values, path coefficients’ size and significance, f² effect sizes, and predictive relevance (Q²) (Hair, et al., 2014).

As shown in table (6), the R² values of knowledge sharing (0.626) and product development (0.458) can be considered to be moderate (Hair, et al., 2014). Table (7) shows the values of f². According to the results, competitive intelligence has a large effect size of 1.674 on knowledge sharing and a small effect size of 0.048 on product development, and knowledge sharing has a medium effect size of 0.138 on product development (Cohen, 1988).

Finally, as can be seen in table (8), the Q² values of all constructs are considerably above zero. More specifically, knowledge sharing has the highest Q² values (0.418), followed by product development (0.274). These results offer strong support for the model's predictive capability.

Scholars recommend the bootstrapping technique to examine the significance of the structural model. For instance, Hair, et al. (2014) recommend using 5000 subsamples to avoid bias. Table (9) and Figure (2) demonstrate the results of the bootstrapping analysis. The results show that competitive intelligence (β = 0.264, t = 2.907, p = 0.004) has a positive significant effect on product development; thus, the first hypothesis (H1) is supported. Furthermore, results show that competitive intelligence (β = 0.791, t = 23.565, p = 0.00) has a positive significant effect on knowledge sharing; thus, the second hypothesis (H2) is supported. Finally, results show that knowledge sharing (β = 0.448, t = 5.109, p = 0.00) has a positive significant effect on product development; thus, the third hypothesis (H3) is supported.

Figure 2