Organizational existence and success depend on continuous development that contains substantial improvements to existing products or launching new products (Marquis, 2019; Valle and Avella, 2003). Many scholars suggest that product development is a crucial element of an organization's strategy for achieving profitability (Hauser, et al., 2006), satisfying customer needs (Iheanachor, et al., 2021), gaining a competitive advantage (Ayag, and Ozdemir, 2007), growing in the marketplace (Moore, 2011), and maintaining survival (Judson, et al., 2006).
Product development is highly based on innovation. For instance, the term product development is often used interchangeably with the term product innovation in the literature. In this regard, Hauser, et al. (2006) suggested, “a primary goal of innovation is to develop new or modified products for enhanced profitability.” In accordance with this view, Wheelen and Hunger (2012) defined product innovation as “the development of a new product or the improvement of an existing product's performance.”
Product development is important for organizations. Therefore, they modify their products or launch new ones. Unfortunately, scholars reported that the failure rate of improved or new products continues to rise (Derbyshire and Giovannetti, 2017; Iheanachor, et al., 2021; Moore, 2011). This failure leads organizations to lose their resources and customers, and as a result, there will be a sharp decrease in profitability (Iheanachor, et al., 2021).
The success of the product development process primarily rests on a deep understanding of the customer needs and then modifying or introducing products that satisfy these needs (Hauser, et al., 2006; Wheelen and Hunger, 2012). However, product development could fail. The main reason for the failure of new or improved products has been attributed to a lack of accurate information either about customers’ needs, technological advancements, or competitors’ actions (Derbyshire and Giovannetti, 2017; Connell, et al., 2001; Iheanachor, et al., 2021). Therefore, one key area of consideration that could guarantee the success of improved or new products is competitive intelligence.
To avoid improved or new product failure, organizations can integrate competitive intelligence into the product development process and obtain accurate information about customers’ needs, technological environment, and competitors’ actions (Gitelman, et al., 2021; Mehmet Ali Köseoglu, et al., 2021).
To avoid improved or new product failure, organizations can integrate competitive intelligence into the product development process and obtain accurate information about customers’ needs, technological environment, and competitors’ actions (Gitelman, et al., 2021; Mehmet Ali Köseoglu, et al., 2021).
Regardless of competitive intelligence importance and benefits, especially in product development, research indicates that many factors contribute to competitive intelligence failures, like organizational culture, absence of top management support, lack of knowledge sharing, and unclear communication (Garcia-Alsina, et al., 2013; Tsitoura and Stephens, 2012). Scholars reported that ineffective knowledge sharing between managers and employees leads to misunderstanding of the organization's needs required from the intelligence process; as a result, the competitive intelligence process becomes useless (Cartwright, et al., 1995; Deshpande and Zaltman, 1982).
Based on the aforementioned, knowledge sharing is a critical factor for organizations to benefit from the information which comes from competitive intelligence activities. In addition, to ensure successful product development, knowledge obtained through competitive intelligence should be shared and put into action within the whole organization (Du Plessis, 2007; Salunke, et al., 2019).
There are contradictory results regarding the relationship between competitive intelligence and product development or innovation capabilities. While most previous studies confirm that competitive intelligence is related positively to product development processes (Moneme, et al., 2017; Omede, et al., 2020), others have revealed that competitive intelligence has failed to contribute to product development processes because of the lack of knowledge sharing and information behavior (Maungwa and Fourie, 2018; Nemutanzhela and Iyamu, 2011). Therefore, the present study tries to explain this contradiction by using knowledge sharing as a mediating variable in this relationship. In addition, a literature review demonstrated that there is a lack of studies that examined whether knowledge sharing can mediate the relationship between competitive intelligence and product development within the Arab cultural context, especially in Jordan. Hence, this study can be considered one of he first that strives to explore such a relationship in the context of Arab culture. Therefore, the purpose of the present study is to gain a better understanding of the relationship between competitive intelligence and product development through knowledge sharing. Consequently, this study contributes to the literature on the impact of competitive intelligence and knowledge sharing on product development.
The current study was applied to the chemical industry sector in Jordan since the exports of this sector constitute 14.8% of the total industrial exports in Jordan. Also, the revenue of this sector is 2 billion dollars per year, and 120 products from this sector have entered the markets of 105 countries. The reason behind choosing this sector was that it depends heavily on product development to maintain its competitiveness (Jordan Chamber of Industry, 2022).
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.
Product development decision is highly based on competitive intelligence (Fehringer, et al., 2006). Many authors define competitive intelligence as a process of collecting information about competitors, general industry trends, new technologies and products, and customers’ preferences (Nasri, 2012; Pellissier and Nenzhelele, 2013; Tsuchimoto and Kajikawa, 2022). This information facilitates product development and innovation capabilities within organizations (Tanev and Bailetti, 2008). However, scholars provided much empirical evidence regarding the relationship between competitive intelligence and product development. For instance, the results of studies by Amiri, et al. (2017), Bao (2020), Eidizadeh, et al. (2017), and Lin, et al. (2022) revealed that competitive intelligence has a positive effect on product development. Thus, the first hypothesis is
H1: Competitive intelligence affects product development positively.
In a competitive intelligence context, knowledge sharing process starts when employees and top management exchange their interpretation and perception about gathered information (de Almeida, et al., 2016; Kaivo-oja, 2012; Lesca and Lesca, 2011; Schoemaker and Day, 2009). Therefore, scholars such as Hakmaoui, et al. (2022) and Sharma and Djiaw (2011) considered competitive intelligence as an effective tool that encourages knowledge-sharing behavior within the organization.
Thus, the second hypothesis is
H2: Competitive intelligence affects knowledge sharing positively.
Many scholars state that product development depends on knowledge sharing. For instance, authors and researchers such as Berraies, et al. (2015), Elrehail, et al. (2018), and Nonaka and Takeuchi (1995) confirmed that knowledge sharing within an organization builds innovation capabilities, which in turn supports product development initiatives. Moreover, the literature demonstrates that knowledge sharing enhances and facilitates the product innovation capability of the organizations through providing the necessary information about customers’ changing needs and desires (Cummings, 2003; Gurteen, 1999; Liebowitz, 2002; Lin, 2007; Yang and Wu, 2008; Zhihong, et al., 2008). In accordance with this literature, Ferreira, et al. (2021) presented empirical evidence regarding the positive relationship between knowledge sharing and product development. Thus, the third hypothesis is
H3: Knowledge sharing affects product innovation positively.
Customers’ needs and desires are a key input to product development. Therefore, organizations need to collect and analyze information on those needs and desires. In doing so, competitive intelligence is considered an effective tool to acquire required information about customers’ needs and preferences, competitors’ products and innovation, new technologies, and the general trends in the industry (Tsuchimoto and Kajikawa, 2022). To ensure successful product development, employees and top management should share their knowledge, interpretation, and perception about competitive intelligence information (de Almeida, et al., 2016; Lesca and Lesca, 2011). Thus, the fourth hypothesis is
H4: Knowledge sharing mediates the relationship between competitive intelligence and product innovation.
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 (
Demographic characteristics of the study sample
(
Variables | Category | Frequency | Percentage |
---|---|---|---|
Gender | Male | 114 | 64% |
Female | 64 | 36% | |
Length in current job | Less than 5 years | 15 | 8.4% |
From 5 years to less than 10 years | 41 | 23% | |
From 10 years to less than 15 years | 67 | 37.6% | |
15 years and above | 55 | 31% | |
Education | Diploma | 5 | 2.8% |
Bachelor's | 24 | 13.5% | |
Master's | 139 | 78.1% | |
PhD | 10 | 5.6% | |
Total | - | 178 | 100% |
The competitive intelligence scale has been reconstructed by adapting from existing academic literature. The base of eight items asked regarding the competitive intelligence variable is adapted mainly by the research of Sawka, et al. (1995), Calof and Breakspear (1999), Calof and Dishman (2002), Viviers, et al. (2002), and Saayman, et al. (2008). Sample items are “Our company's competitive intelligence capability is an ongoing process” and “Our company analyzes our competitors’ plans and strategies to predict and anticipate their actions.”
Product development was measured with the scale developed based on the work of Gunday, et al. (2011) and Jansen, et al. (2006). The scale consists of seven items. Sample items are “Developing new products with technical specifications and functionalities totally differing from the current ones” and “Increasing manufacturing quality in components and materials of current products.”
Knowledge sharing was measured with the scale developed by McGrane (2016). This scale consists of four items. Sample items are “My organization has a process for sharing knowledge with those involved in making decisions” and “I make an effort to share knowledge with other members of the organization.”
All the responses on the above scales were rated on a 5-point Likert scale ranging between “1 = strongly disagree” and “5 = strongly agree.”
Participants were asked to answer three questions related to their gender, education, and length in their current job.
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).
Means and standard deviations of responses
(
Variables | Mean | Standard deviations | Level |
---|---|---|---|
Competitive intelligence | 3.99 | 0.589 | Moderate |
Knowledge sharing | 3.90 | 0.684 | Moderate |
Product development | 4.38 | 0.591 | Moderate |
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.
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).
Measurement of reliability and convergent validity
(
Convergent validity | Internal consistency | |||||
---|---|---|---|---|---|---|
Latent variable | Item | Factor loading | Average variance extracted (AVE) | Composite reliability | Rho-A | Cronbach's alpha |
>0.70 | >0.50 | >0.70 | - | >0.70 | ||
Competitive intelligence | CI1 | 0.745 | 0.593 | 0.921 | 0.904 | 0.902 |
CI2 | 0.785 | - | - | - | - | |
CI3 | 0.799 | - | - | - | - | |
CI4 | 0.732 | - | - | - | - | |
CI5 | 0.708 | - | - | - | - | |
CI6 | 0.832 | - | - | - | - | |
CI7 | 0.770 | - | - | - | - | |
CI8 | 0.783 | - | - | - | - | |
Knowledge sharing | KS1 | 0.777 | 0.688 | 0.898 | 0.851 | 0.848 |
KS2 | 0.873 | - | - | - | - | |
KS3 | 0.845 | - | - | - | - | |
KS4 | 0.819 | - | - | - | - | |
Product development | PD1 | 0.752 | 0.624 | 0.920 | 0.900 | 0.899 |
PD2 | 0.770 | - | - | - | - | |
PD3 | 0.805 | - | - | - | - | |
PD4 | 0.710 | - | - | - | - | |
PD5 | 0.848 | - | - | - | - | |
PD6 | 0.804 | - | - | - | - | |
PD7 | 0.830 | - | - | - | - |
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.
Measurement of discriminant validity
(
Competitive intelligence | Knowledge sharing | Product development | |
---|---|---|---|
Competitive intelligence | 0.770 | - | - |
Knowledge sharing | 0.791 | 0.829 | - |
Product development | 0.618 | 0.657 | 0.790 |
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.
Collinearity statistics
(
Competitive intelligence | Knowledge sharing | Product development | |
---|---|---|---|
Competitive intelligence | 0.770 | - | - |
Knowledge sharing | 0.791 | 0.829 | - |
Product development | 0.618 | 0.657 | 0.790 |
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 (
As shown in table (6), the
(
Competitive intelligence | ||
---|---|---|
Knowledge sharing | 0.626 | 0.624 |
Product development | 0.458 | 0.451 |
f2 effect sizes values
(
Competitive intelligence | Knowledge sharing | Product development | |
---|---|---|---|
Competitive intelligence | - | 1.674 | 0.048 |
Knowledge sharing | - | - | 0.138 |
Product development | - | - | - |
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.
Construct cross-validated redundancy
(
SSO | SSE | Q2 = (1 − SSE/SSO) | |
---|---|---|---|
Competitive intelligence | 1440.000 | 1440.000 | - |
Knowledge sharing | 720.000 | 418.744 | 0.418 |
Product development | 1260.000 | 915.180 | 0.274 |
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,
Hypotheses testing
(
H | Path | Path coefficients | Remarks | ||
---|---|---|---|---|---|
H1 | CI → PD | 0.264 | 2.907 | 0.004 | Supported |
H2 | CI → KS | 0.791 | 23.656 | 0.000 | Supported |
H3 | KS → PD | 0.448 | 5.109 | 0.000 | Supported |
H4 | CI → KS → PD | 0.354 | 5.073 | 0.000 | Supported |
Table (10) shows the indirect effect is significant since neither of the 95% confidence intervals includes zero (Preacher and Hayes, 2008). The
Mediation analysis
(
Bootstrapped confidence interval | |||||||
---|---|---|---|---|---|---|---|
Path a | Path b | Indirect effect | SE | 95% LL | 95% UL | Decision | |
0.791 | 0.448 | 0.354 | 0.070 | 5.062 | 0.217 | 0.492 | Mediation |
Similar to past research, a relationship was found between competitive intelligence and product development (Amiri, et al., 2017; Bao, 2020; Eidizadeh, et al., 2017; Lin, et al., 2022). As proposed in the first hypothesis, competitive intelligence positively affected product development. The finding can be explained through the idea which states that information that comes from the competitive intelligence activities about customers’ needs and desires, technological advancements, and competitors’ products and innovation serve as key inputs for product development. The findings from the current study also showed the expected relationship between competitive intelligence and knowledge sharing. As proposed in the second hypothesis, competitive intelligence positively affected knowledge sharing. An explanation for this relationship supports the idea of considering competitive intelligence as an effective tool that encourages knowledge-sharing behavior within the organization (Hakmaoui, et al., 2022), since competitive intelligence requires employees and top management to exchange their knowledge, experiences, interpretation, and perception about the collected information (de Almeida, et al., 2016; Kaivo-oja, 2012; Lesca and Lesca, 2011; Schoemaker and Day, 2009).
As proposed in the third hypothesis, a strong relationship between knowledge sharing and product development was found. Knowledge sharing positively affected product development. An explanation for this relationship is that knowledge sharing improves and promotes the product development and innovation capability of the organizations by exchanging the necessary information about customers’ changing needs and desires among the organization members (Cummings, 2003; Ferreira, et al., 2021; Gurteen, 1999; Liebowitz, 2002; Lin, 2007; Yang and Wu, 2008; Zhihong, et al., 2008).
Finally, the findings provide empirical support for the mediating role of knowledge sharing in the relationship between competitive intelligence and product development. Higher levels of competitive intelligence information not only affect product development directly, but also encourage knowledge sharing, which in turn leads to ensuring product development success. Hence, some of the effects of competitive intelligence on product development are explained by knowledge sharing.
The current study has some practical implications for top managers who wish to promote product development efforts. First, top managers must recognize that a competitive intelligence activity not only promotes product development capability, but also encourages knowledge-sharing behavior among employees. Second, it should be noted that product development is not automatically promoted by competitive intelligence. To facilitate product development efforts, top managers should encourage organization members to share their knowledge with each other. Thus, organizations must introduce guidelines and rules for knowledge sharing. Finally, top managers must recognize that a competitive intelligence activity is an ethical and legal practice. Therefore, organizations must raise awareness regarding this practice among their members.
The study findings demonstrated several concluding remarks. First, competitive intelligence has a direct positive effect on product development and knowledge sharing, which means that when competitive intelligence increases, knowledge sharing and product development will also increase. Second, knowledge sharing has a direct positive impact on product development, which means that the higher the level of knowledge sharing, the more product development will take place. Third, knowledge sharing mediates the impact of competitive intelligence on product development, which means that the increase in competitive intelligence will lead to an increase in knowledge sharing, which in turn leads to an increase in product development.
The current study was conducted in one cultural context, using participants from the chemical manufacturing sector in Jordan. So, this could limit the ability to generalize the findings. Therefore, similar studies and research should be conducted in other types of manufacturing and service provider companies in order to generalize the findings. Future research may focus on studying the impact of culture as a moderator or mediator on the relationship between competitive intelligence and product development or the relationship between knowledge sharing and product development.