Innovative capacity and new product performance: Indirect effect of absorptive capacity and knowledge transformation

Abstract

The context in which textile companies operate is highly dynamic, with trends, fashion, and consumer tastes constantly changing. This dynamic nature generates concern among managers about the difficulty of remaining competitive in such a rapidly evolving market. The present research proposes that the performance of new products is a key avenue for companies to meet their commercial and financial expectations. The objectives are to determine the mediating effect of the capacity for the absorption and transformation of knowledge on the relationship between innovation capacity and new product performance in textile companies, where empirical evidence is limited. The empirical study on a sample of 166 textile companies in Arequipa, Perú. The analysis employed partial least squares structural equation modeling using Smart PLS. The results reveal that innovative capacity positively affects the performance of new products. Moreover, the potential absorptive capacity and knowledge transformation, acting as mediating variables, contribute to improved performance of new products. As a contribution to management, it is recommended that company managers cultivate an environment conducive to the acquisition, assimilation, and transformation of knowledge. This approach ensures that innovation capacity is reflected in new product performance.

Keywords:
Innovation capacity; Potential absorptive capacity; New product performance; Knowledge transformation

1. Introduction

In the face of the strong competition manufacturing companies face today, managers recognize the importance of developing new products to gain a foothold in the market (Jin et al., 2019). The success achieved by the new product is analyzed in relation to the customer. It will depend on the perceived usefulness with respect to the adequacy of their needs and expectations (Dabrowski, 2019), and on the part of investors, it defines the degree to which commercial success is achieved and consequently higher profits for the company (Chan & Cho, 2022; Walheiser et al., 2021). Another aspect to consider is the high failure rate of new products launched into the market, with data reporting up to 50% in recent years (Zhan et al., 2019). Drawing on the two perspectives proposed by Yi et al. (2021) , meeting customer expectations is necessary, as is generating profits for organizations. This leads us to investigate the factors that determine better performance, enabling companies to face the currently extremely aggressive global competition and to create novel products.

Previous studies have investigated the effect of dynamic capabilities as an antecedent of good performance in new products through continuous environmental observation and the identification of business opportunities within the dimensions proposed by Wang (2016) , namely adaptive, absorptive and innovative capacity (IC). In the present study, we analyze IC as a determinant of new product performance (NPP), also potential absorptive capacity (PACAP) and knowledge transformation (KT) as mediating variables in the direct relationship. In this regard, the IC describes the propensity and sustained effort to improve procedures for developing new products through creative solutions (Lumpkin & Dess, 1996). Wang (2016) argues that IC employs interrelated procedures to develop new products by improving the quality of those already produced by the firm. Also, evidence indicates that developing innovative products can lead organizations to better economic results (Fouad et al., 2018). On the other hand, there are indications of contradictory results, even demonstrating that innovation failure influences its growth, which could be due to low or extremely high levels of R&D intensity (Kim, 2022) and, in other cases, to other factors. However, there is a positive effect, but the relationship was not statistically significant (Walheiser et al., 2021). Finally, previous studies reveal that innovation is highly complex, difficult to explain in terms of a direct relationship, and more likely to reflect the need to include alternative variables as mediators or moderators (Parra-Requena et al., 2020).

We propose including PACAP as a mediating variable in the relationship between IC and NPP. In this regard, absorptive capacity has evolved from Cohen & Levinthal (1990) proposal to Zahra and George's (2002) definition, which defines it as the ability to recognize new information, assimilate it and finally apply it to commercial activities. In this sense, knowledge absorption capacity can serve as a means for organizations to obtain valuable information that promotes the creation of novel products (Martínez-Sánchez et al., 2020; Miller et al., 2022; Crespi et al., 2022; Laviniki et al., 2021; Moreno et al., 2020). KT is also proposed as a mediating variable because there is evidence that it improves innovation performance and ultimately business results (Yang & Wei, 2019). Based on the above, we consider it essential to continue analyzing the factors that enhance the effect of IC and identify a gap in its effect on NP in textile companies. It is not enough to develop innovation capacity to be successful with new products; it is necessary to have information from the environment, proposing that PACAP and knowledge transformation can be key elements to achieve this.

Under the quantitative approach, the primary objectives of this study are to determine the mediating effect of PACAP and KT on the relationship between IC and NPP in textile firms, a sector with limited empirical evidence. For the empirical applications, structural equation models were utilized, employing the PLS software, which is particularly suitable for assessing the statistical significance of coefficients, on a sample of 166 textile companies in Arequipa, Perú. The analysis unfolds in two stages: first, the measurement model analysis and second, the structural model analysis. We present three main contributions to the literature. We review the background of the theory of dynamic capabilities and NPP, and link the constructs in the context of the textile sector. Second, we analyze the direct effect of IC on NPP. Third, we incorporate PACAP as a mediating variable. Fourth, KT is also incorporated as a mediating variable, expanding knowledge on the determinants of NPP in the textile sector, where the proposed constructs have been little explored.

2. Literature review

2.1 New products performance

Due to the need for organizations to offer new products that meet consumer expectations, the analysis of NPP is proposed as a strategy to maintain the company's competitiveness, defining the variable in terms of the need for commercial success, also contributing to profit generation (Chan and Cho, 2022). Previous studies have shown that risk is high when launching new products, with extremely high failure rates (Hoffman et al., 2010; Cooper, 2019). In this sense, there is uncertainty and resistance from entrepreneurs to introduce new products, limiting the propensity to innovate (Walheiser et al., 2021).

Among the determinants of NPP, we can mention creativity, which generates novel ideas and solutions and contributes to better results as long as it is practical and meets consumer expectations (Chan and Cho, 2022; Zirena-Bejarano et al., 2023; Yi et al., 2021). Also, product design is important, which increases customer preferences and satisfaction, generating sales and profits (Srinivasan and Lilien, 2018). In addition, there is evidence of concrete results from the effect of innovation on the NPP (Fouad et al., 2018; Walheiser et al., 2021), though the evidence is inconclusive compared to studies showing that innovation is not reflected in successful commercialization (Castellion and Markham, 2013).

The decision on performance measures poses a significant challenge due to the absence of consensus to date. Nevertheless, it is widely acknowledged that these measures aim to gauge the company’s performance when introducing a product to the market, facilitating the commercialization of new products (Liu et al., 2015). In this study, we align ourselves with the perspective that underscores the importance of the number of innovations, sales and profitability (Carbonell & Rodríguez-Escudero, 2016; García-Villaverde et al., 2017; Zhang et al., 2009). The lack of consensus on how to measure this construct suggests the need for further research on the determinants of new product success.

2.2 Innovation capacity

Previous studies have recognized that innovation is the main determinant of business survival and competitiveness (Parra-Requena et al., 2020). To address potential absorptive capacity, it is important to draw on the theory of dynamic capabilities as a theoretical background. (Garzón Castrillón, 2012). We have defined dynamic capabilities as skills that the firm must build to reconfigure and integrate internal and external competencies to keep up in highly changing environments (Teece et al., 1997). In this sense, Wang and Ahmed (2007) identify adaptive, absorptive and innovative capabilities as dimensions of dynamic capabilities. We have defined IC as the willingness to engage in the process of creation and experimentation to introduce new products, services or processes (Lumpkin and Dess, 1996). We will analyze IC as a determinant of NPP by finding evidence of such incidence (Fouad et al., 2018; Sun & Lau, 2020; Zhao et al., 2021).

There is evidence that organizations that allocate resources to develop their IC are more likely to succeed in the future, as evidenced by the introduction of new products (Saunila and Ukko, 2013; Ding & Ding, 2022; Zhao et al., 2021). However, it has also been demonstrated that heightened novelty leads to consumers’ unfamiliarity with new products, potentially resulting in rejection at the time of purchase (Dabrowski, 2019). Conversely, there is a large body of research focused on sustainable development goals (Bhutta et al., 2021; Ghobadian et al., 2020). It has also been shown that innovation is a key factor in achieving higher company performance (Chen et al., 2017). Moreover, it can be moderated by various factors (Rodrigo-Alarcón et al., 2014; Jardon & Martinez-Cobas, 2022; Pham et al., 2022; Zirena-Bejarano et al., 2023). Tuan et al. (2016) investigate how product innovation affects the performance of manufacturing companies in developed economies, demonstrating its positive, significant effect on revenue and employment growth. In that line, different aspects of the IC, such as technology and market management, have distinct and significant effects on NPP (Xie et al., 2021; Alraggad and Onizat; Handiwibowo, 2019). Aljanabi (2020) argues that IC provides deep insights that enable organizations to develop the skills needed to create successful new products. Likewise, Kilic (2015) posits that radical and incremental IC drives NPP, concluding that if innovation increases, then NPP will increase. Based on these arguments, we pose the following hypotheses:

H1: IC positively and significantly influences the NPP in companies.

2.3 Potential absorption capacity

Companies today must have updated information as input to carry out their processes. This information must be evaluated, absorbed and transformed, giving rise to the concept of absorption, defined by Chichkanov (2020) as the ability to identify and evaluate new knowledge gathered from the environment to apply it subsequently. Also, Zahra and George (2002) contribute by identifying PACAP as a process comprising the evaluation and internalization of knowledge. The realized absorptive capacity refers to the transformation and application of transformed knowledge. Given the approach to these two dimensions, we focus on PACAP, which comprises knowledge acquisition and assimilation (Zahra and George, 2002). Acquisition recognizes the importance of locating and acquiring knowledge in the environment, and the way it is managed will facilitate the PACAP. On the other hand, we should avoid inertia and resistance to the assimilation of new ideas that block the assimilation of new knowledge (de Araújo et al., 2015). New knowledge must be stored in a repository that is easily accessible to company members, who will retrieve and assimilate it (Algarni et al., 2023). Specifically, we must incorporate the new knowledge into the existing knowledge scheme, known as prior knowledge, generating successful assimilation (Mueller et al., 2020; Cousins, 2018).

Studies demonstrated the influence of absorptive capacity on company performance (Aliasghar et al., 2019; Nguyen et al., 2021; Algarni et al., 2023), product development and profitability (Peng & Li, 2021; Guan & Liu, 2016; Handiwibowo et al., 2021). (2023) argue that potential absorptive capacity increases the ability of the organization to acquire and assimilate external knowledge sources necessary for innovativeness and competitive advantage. Likewise, tacit knowledge, routines and procedures are configured as the means for exploratory learning of new knowledge, markets and new products (Thumbi Njoroge, 2021). Additionally, it is positively related to NPP, underscoring the importance of designing processes and routines to develop this capability (Chen and Chang, 2019; Zirena-Bejarano et al., 2023).

The literature shows the need to study the determinants of new product performance, finding that many times several capabilities are required because it is not enough that they act individually; in the case of absorptive capacity, dynamic capabilities and market orientation are different but complement each other, increasing the NPP (Modolo et al., 2021). (Modolo et al., 2021). In the case of a high absorption capacity, with active participation from customers, suppliers and even competitors, innovation capacity is improved; thus, collaboration with different internal and external actors enhances knowledge exchange, a key predictor of new product performance. So, leveraging knowledge acquisition and assimilation, which nurture IC, will drive NPP. Companies will develop better results if they can assimilate external knowledge by applying it to develop IC independently of the direct effect of the independent variable. Based on the above arguments, the following hypothesis is proposed.

H2: PACAP positively mediates the relationship between IC and NPP in companies.

2.4 Knowledge transformation

KT is defined as the change of an idea, behavior, or knowledge that is embodied in a new product or service for the company that generates it (Nonaka & von Krogh, 2009). Another approach involves integrating new knowledge with existing and recovered knowledge to act in response to new market opportunities (Fernhaber & Patel, 2012). The literature recognizes the importance of transforming tacit knowledge, obtained through individual experience, into codified knowledge that can be incorporated into the product-creation process (Lee, 2021; Zirena-Bejarano et al., 2024a). In addition, information generated from customer interactions is fundamental to innovation; the knowledge derived from this information can improve the performance of new products (Cui & Wu, 2016; Xie et al., 2020). The interaction and flow of information create a source of knowledge that, in turn, generates changes and demands adaptation, leading to product innovation (McLeod, 2020; Guo et al., 2020; Zirena-Bejarano et al., 2023). Lai et al. (2014) argue that organizational members who are good at transforming knowledge tend to use it in practice (Yew, 2021; Sirinaga et al., 2019), improving the capacity for innovation that translates into new products. With these arguments, we pose the following hypothesis.

H3: KT has a positive mediating effect on the relationship between IC and NPP in companies.

3. Methodology

Figure 1 represents the model proposed for the present research. It is possible to identify the direct relationship between the IC variable and NPP, as hypothesized by H1 and the mediation effect of PACAP on the relationship between IC and NPP, as hypothesized by H2. The mediation effect of KT between IC and NPP generates hypothesis H3

Figure 1
Theoretical study model

3.1 Population and sample

We conducted this research study in textile companies in the Arequipa region of Peru. This sector is characterized by being composed mostly of small and medium-sized companies dedicated to the production of camelid fiber articles (sheep, alpaca, llama and vicuña) and cotton (pima, tangüis), recognized worldwide for the diversity of its products: yarns, alpaca tops and combed yarns, in addition to being identified for its important contribution to the Gross Domestic Product (GDP) with figures of 6.4% in 2019, with a clear recovery after the crisis caused by Covid-19.

To determine the sector's population, we requested information under the law of access to information from the Superintendencia de Administración Tributaria (SUNAT) as of October 31, 2022. In response, we obtained the registry of companies according to the International Standard Industrial Classification (ISIC 1313), comprising 234 companies and generated a sample of 166 companies, the selection of which will be explained in the following paragraph.

Regarding the questionnaire, we requested reviews from professionals and academics in the textile industry to gather observations and improve the relevance of the questions, as the items were adapted from scales validated in previous research. To conduct the reliability analysis, we administered 30 questionnaires to textile company representatives and conducted a reliability test using Cronbach's alpha, achieving satisfactory results. Fieldwork began with administering the questionnaire. We administered the questionnaire via random sampling and, after discarding some that were poorly filled out, collected 166 valid questionnaires, exceeding the sample size determined, yielding a response rate of 71% at a 95% confidence level under the most unfavorable condition (p=q=0.5).

3.2 Measures

We developed the instrument by adapting validated scales into a Likert-type questionnaire with 7 response options. (Appendix 1).

Performance of new products: We measured this variable using 3 items that assess the number of innovations, profitability and sales of new products and services, adapted from Carbonell and Rodríguez-Escudero (2016) , García-Villaverde et al. (2017) and Zhang et al. (. The variable was operationalized by multiplying items measuring importance and satisfaction.

Innovation capability: Identifies the ability to use procedures to develop new products and improve the quality of existing ones (Wang, 2016). For the scale selection, we reviewed several published studies and chose the one proposed by Akman and Yilmaz (2008) , as it is the most appropriate for the research and measures the variable with 5 items.

Potential absorptive capacity: The potential absorptive capacity comprises the capacity to acquire and assimilate knowledge and is understood as part of the absorptive capacity (Zahra & George, 2002), The PACAP comprises the capacity to acquire and assimilate knowledge and is understood as part of the absorptive capacity (Flatten et al., 2011), with 7 items, and the item 6 was withdrawn for not reaching the required value.

Knowledge transformation: This variable identifies the transformation of information and knowledge from prior knowledge and has been measured using a 4-item scale (Nonaka & von Krogh, 2009).

Control variables: We have taken into account as control variables for the study, the age of the company, antecedents argue that the age of the company can contribute to the outcome of the research, arguing that older years are associated with greater experience, but also greater rigidity in their structures, which means greater delay in decision making (Lee, 2008), we have determined the age variable by the difference in the year in which the information was collected and the year of founding of the company. The other control variable is company size, measured by the number of workers.

3.3 Analysis techniques

For the study, we administered a questionnaire via Google Forms to textile company managers in the city of Arequipa, Peru. The instrument consists of 40 items that measure the three study variables. The information collected through the questionnaires, was analyzed through structural equation modeling using the partial least squares technique through the Smart PLS software version 3.9, a technique recommended for the area of social sciences (Chin, 1998), we conducted the study in two stages, initially the direct relationship between the IC variable and NPP was evaluated, and in the second stage the PACAP variable was incorporated as a mediating variable, measuring the indirect effect that was generated (Hair et al., 2019),

In addition, we designed the measurement model to evaluate the item factor loadings for the proposed constructs; through software processing, the indicators' reliability and validity were determined. Given that the accepted values were obtained in the evaluation of the measurement model, the structural model was evaluated by examining the causal relationships between the cause and effect variables (Hair et al., 2019). Finally, using the bootstrap technique, which creates subsamples by randomly drawing observations, we tested the statistical significance of the estimated model coefficients in PLS-SEM (Davison & Hinkley, 1997).

4 Results

4.1 Descriptive results

Table 1 presents descriptive statistics for the research and control variables. For processing, we applied various statistical techniques, which allowed us to assess the performance of the descriptive analysis using the mean, standard deviation and correlations between the proposed constructs.

Table 1
Descriptive analysis and correlations

Variables	Mean	SD	1	2	3	4	5	6	7
1. IC	5.599	0.759	1
2. PACAP	5.417	0.803	0.730**	1
3. KT	5.438	0.837	0.711**	0.743**	1
4. NPP	31.930	9.020	0.621**	0.631**	0.629**	1
5. Age	2.320	1.096	0.058	0.069	0.146	0.068	1
6. Size	2.250	0.463	-0.028	-0.017	0.030	-0.085	0.174	1
7. Export	1.630	0.485	-0.053	-0.087	-0.100	-0.172*	-0.363**	-0.237**	1

• Note: ** Correlation is significant at the 0.01 level (bilateral). * Correlation is significant at the 0.05 level (bilateral).
• Source: Own elaboration

4.2 Evaluation of the measurement model.

For the analysis of the measurement model we evaluated the reliability and validity of the variables: IC, PACAP, KT and NPP, performing a systematized procedure through Cronbach's alpha indicator, reaching values above the proposed threshold of 0.70 (Cronbach & Shavelson, 2004), For the analysis of the measurement model we evaluated the reliability and validity of the variables: IC, PACAP, KT and NPP, performing a systematized procedure through Cronbach's alpha indicator, reaching values above the proposed threshold of 0.70 (Chin & Dibbern, 2010). Regarding convergent validity, Table 3 shows AVEs above 0.50, indicating that the indicators of the constructs studied substantially explain the measurement variable (Hair et al., 2011). Regarding discriminant validity, we used the Fornell-Larcker criterion, obtaining values in the diagonal shown in bold that are higher than the values in the same column. Likewise, according to the literature, looking for more demanding indicators that confirm the discriminant validity, the Hetero-trait-monotrait ratio (HTMT) is applied, with higher performance than those that can be observed in the upper rows mentioned above, which shows that the proposed constructs have discriminant validity (Henseler et al., 2015).

Table 2
Reliability analysis, convergent and discriminant validity of the constructs

	Internal consistency		Convergent validity AVE ≥ 0.50	Discriminant validity: Fornell and Larcker and HTMT
	Cronbach’s Alpha ≥ 0.70	Composite reliability ≥ 0.70		1	2	3	4
1. IC	0.861	0.863	0,643	0.802	0.851	0.862	0.726
2. PACAP	0.836	0.840	0.555	0.726	0.745	0.929	0.782
3. KT	0.836	0.837	0.672	0.732	0.776	0.820	0.801
4. NPP	0.885	0.886	0.813	0.635	0.673	0.690	0.902

• Note: All values are within the required parameters.
• Source: Own elaboration

Table 3 shows the discriminant validity of the indicators for the variables IC, PACAP and NPP. The external loadings of each indicator are higher than any of its cross-loadings, as indicated in bold (Hair et al., 2019).

Table 3
Discriminant validity analysis by cross-loading and collinearity analysis

Variables			Cross Loads
	IC	PACAP	KT	NPP
IC
Inno18	0.729	0.519	0.539	0.441
Inno19	0.801	0.595	0.607	0.516
Inno20	0.809	0.575	0.581	0.549
Inno21	0.818	0.596	0.633	0.542
Inno22	0.849	0.621	0.573	0.489
PACAP
Abs04	0.584	0.643	0.497	0.509
Abs05	0.598	0.825	0.661	0.525
Abs07	0.571	0.828	0.602	0.499
Abs08	0.518	0.737	0.530	0.513
Abs09	0.494	0.759	0.615	0.490
Abs10	0.450	0.654	0.553	0.458
KT
Know01	0.629	0.674	0.821	0.558
Know02	0.581	0.651	0.851	0.559
Know03	0.617	0.629	0.849	0.585
Know04	0.572	0.589	0.754	0.558
Newp1	0.573	0.632	0.633	0.899
Newp2	0.575	0.596	0.639	0.909
Newp3	0.568	0.592	0.593	0.897

• Note: Bold values indicate higher values than the other columns, indicating no collinearity problems.
• Source: Own elaboration

As observed, the data in tables 2 and 3 indicate that the measurement model is valid, as the variables and their indicators meet the parameters suggested by the literature (Hair et al., 2019). We proceed with the evaluation of the structural model.

4.3 Evaluation of the structural model

4.3.1 Structural model 1: Influence of inter-organizational relationships on NPP

Collinearity is another indicator to be analyzed; in this case, we use the variance inflation factor (VIF). According to the literature, the values are below the threshold of 3 (Roberts and Thatcher, 2009). As shown in Table 3, none of the indicators reaches critical levels of collinearity.

Table 4 shows the data from the evaluation of the direct relationships of the proposed model (IC and NPP; β = 0.622, p < 0.0001), supporting H1 and showing a positive and significant effect of the IC on NPP, with an R² value of 0.414***.

Table 4
Analysis of the direct relationship model

Relationship	Direct effect	95% confidence interval	R2adj	Significance
IC ➔ NPP	0.622	0.502-0.717	0.414***	0.000***

• ***<0.001
• Source: Own elaboration

4.3.2 Structural Model 2: PACAP mediates the relationship between IC and NPP in companies

We add PACAP and knowledge transformation as mediating variables between the IC and NPP constructs. Table 5 shows that the IC variable positively and significantly affects NPP (β = 0.179, p < 0.056). Similarly, the results show a positive and significant effect of IC on PACAP (β=0.726, p<0.0001) and of PACAP on NPP (β=0.270, p<0.003). On the other hand, there is a positive and significant effect between IC and KT (β=0.732, p<0.0001) and of KT with NPP (β=0.336, p<0.001). Therefore, it is demonstrated that there is a positive and significant relationship between IC and NPP, through PACAP as an indirect effect of 0.196***, thereby supporting hypothesis H2. In addition, it is demonstrated that there is a positive and significant relationship between IC and NPP, through the KT as an indirect effect of 0.246***, being demonstrated the hypothesis H3. The results confirm an increase in the adjusted coefficient of determination from 0.414*** to 0.540***.

Table 5
Analysis of the mediation model

Relationship	Direct effect	95% confidence interval	R2	VAF	Significance (p < 0.05)
IC(PACAP	0.726	0.621-0.797			0.000***
PACAP(NPP	0.270	0.107-0.427			0.003***
IC(KT	0.732	0.614-0.807			0.000***
KT(NPP	0.336	0.150-0.523			0.002***
Relationship	Indirect effect	95% confidence interval			Significance (p < 0.05)
IC(PACAP(NPP	0.196	0.080-0.317		0.316	0.004***
IC(KT(NPP	0.246	0.117-0.382		0.396	0.001***
R2adj			0.540		0.000***

• Note *sig≤0.05; **sig≤0.01; ***sig≤0.001
• Source: Own elaboration

4.4 Evaluating of the Predictive Validity

As a next step, the predictive nature of the proposed integral model is evaluated according to the procedure proposed by (Cepeda Carrión et al., 2016) and developed and implemented in Smart PLS 4 by (Shmueli et al., 2019).

When executing the PLS predict algorithm, we find Q² calculated for all the predictor variables, which should give a positive value. To make the decision, as symmetry analysis is made for all the prediction errors of the indicators calculated by the algorithm. If the symmetry is greater than 1, the root mean square error (RMSE) must be used. If it is smaller, the other option should be used, the mean absolute error (MAE). In the prediction analysis with PLS predict, the aim is to verify if the prediction error of PLS SEM is less than the linear regression (LM) error, In this context, it can be affirmed that the model has predictive power (Shmueli et al., 2019).

The results of the analysis are shown in the table 6. We observe that the predicted values of Q2 are positive, and the prediction errors of each indicator are smaller than the values of LM MAE. It is concluded that the proposed model has high predictive power (Shmueli et al., 2019) and figure 2 show la results of the model with the mediation variables.

Table 6
Results of prediction analysis

Items	Q2 Predict	PLS-SEM RMSE	PLS-SEM MAE	LM RMSE	LM MAE	Skewness
Abs04	0.317	0.965	0.770	0.985	0.787	-0.472
Abs05	0.346	0.889	0.705	0.914	0.724	-0.687
Abs07	0.318	0.894	0.673	0.926	0.709	-1.493
Abs08	0.260	0.841	0.667	0.883	0.697	-0.668
Abs09	0.230	0.932	0.710	0.961	0.736	-0.797
Abs10	0.194	1.027	0.755	1.041	0.784	-1.297
Newp1	0.309	8.319	6.235	8.485	6.313	-0.172
Newp2	0.314	8.377	6.235	8.528	6.314	-0.026
Newp3	0.302	8.327	6.227	8.508	6.339	-0.289
Know01	0.385	0.803	0.598	0.830	0.619	-0.995
Know02	0.324	0.872	0.592	0.898	0.604	-2.085
Know03	0.370	0.771	0.581	0.806	0.615	-0.569
Know04	0.320	0.866	0.640	0.899	0.661	-1.239

Figure 2.
Model performance

5. Discussion of Results

In this article we analyzed how PACAP and KT has a mediating behavior between IC and NPP in the context of companies in the textile sector. We understand that manufacturing companies are forced to design new products that are expected to be successful, in order to maintain their competitiveness in the market (Herlinawati & Machmud, 2020). In that sense, there are researches that show interest in identifying the determinants to achieve good performance in new products launched to the market, seeking success in organizations (Martínez, 2022; Ding & Ding, 2022; Zhao et al., 2021; Xie et al., 2020; Adomako & Tran, 2023; Cendana & Wachidin, 2021; Martínez, 2022). We have also shown that PACAP has an impact on NPP (Najafi-Tavani et al., 2023) and finally the mediating effect of PACAP between IC and NPP was confirmed, from which it can be inferred that PACAP is a driver of IC generating better novel product performance. On the other hand, textile companies that explore and assimilate new knowledge will gain valuable information about customer requirements, incorporate it into pre-existing knowledge. Transformed knowledge leads the way to develop new products that are expected to be successful.

Initial results report a positive and significant effect of innovativeness on new product performance, indicating that firms developing innovativeness tend to achieve better new product performance, confirming the finding of (Ding & Ding, 2022; Zhao et al., 2021; Tax et al., 2021; Adomako & Tran, 2023). It was also demonstrated that potential absorptive capacity impacts new product performance (Najafi-Tavani et al., 2023), and finally, the mediating effect of potential absorptive capacity between innovativeness and new product performance was confirmed. From this, it can be inferred that potential absorptive capacity acts as a driver of innovativeness, contributing to better novel product performance.

External knowledge about the customer should be available to drive innovation capability, ensuring the creation of new products according to their expectations (Handiwibowo et al., 2021). Similarly, regarding knowledge transformation, it has been shown that to reap the benefits of knowledge transfer and transformation, it must be considered in all stages of innovation, from product design to commercialization, ensuring new product performance (Pateli & Lioukas, 2019). This confirms its positive mediating effect on the relationship between innovation capability and new product performance (Lai et al. 2014; Guo et al., 2020).

The transformation of knowledge is an essential source for the development of innovation capacity. It allows taking advantage of information on consumer demand, which, when combined with existing knowledge, generates substantial changes to existing products or the creation on new products. These are expected to be commercially accepted successfully. Thus, textile companies that acquire new knowledge and transform it in response to customer demands for the development of new products can achieve the expected performance.

The finding of the study suggest that organizations should develop skills, with innovation capacity being fundamental. However, according to the finding, it is not sufficient; there is a need to develop exploration and assimilation of knowledge capabilities to achieve greater product performance. Specifically, the importance of acquiring new external knowledge as a key resource is consolidated, transforming it to generate innovation capacity. This, in turn, translates into new products meeting new consumer need and expectations.

5.1 Theoretical implications

This study contributes theoretically by linking the theory of dynamic capabilities, specifically innovation and absorptive capacity, with organizational theory, within the context of textile manufacturing. An additional significant contribution lies in the empirical application to textile companies in developing countries, where there is a lack of precedent studies on the variables in the proposed model. This opens avenues for generating business strategies that can benefit entrepreneurs in this sector. The study establishes that innovation capacity influences the performance of new products (Zirena-Bejarano et al., 2023; Pham et al., 2022). This influence is reinforced through potential absorptive and knowledge transformation in the textile sector of developing countries. Recognizing innovation as a strategic business position that enables adaptation to environmental changes (Martínez, 2022), it is complemented by the necessary potential absorption and knowledge transformation to effectively use market information in the creation of novel, successful products (Hurley et al., 2005; Handiwibowo et al., 2021). The analysis of determinants in a highly competitive industry provides a favorable context for the study.

5. 2 Practical implications

In terms of practical managerial implications, it is recommended that managers of textile companies ensure access to information on global textile trends and participate in networks of manufacturers and related industries. This engagement facilitates access to new knowledge about consumer demands and market requirements. Transforming market information into new knowledge becomes a crucial input for developing innovation capacity. In practice, internationally recognized quality aspects of textile fibers, such as camelid and cotton fiber, used as raw materials, face challenges due to their traditional nature. This traditionalism often fails to align with consumer demands, necessitating the development of innovative capacity. Once this capacity is confirmed to bring in new knowledge, the organizational environment can facilitate substantial transformation or the creation of new products. In the final stage, ensuring efficient communication of information about these novel products to the client is crucial, driving purchases and guaranteeing expected yields.

6. Conclusions

This study highlights the importance of potential absorptive capacit and knowledge transformation as key mediators in the relationship between innovation capability and new product performance in the textile sector of Arequipa, Peru. Likewise, knowledge transformation emerges as a critical process that translates tacit information into practical and novel solutions, further strengthening the link between innovation and new product performance. These findings underscore the relevance of integrating these constructs within the theoretical framework of dynamic capabilities, demonstrating how their interaction not only enhances the understanding of the drivers of business success but also provides a solid foundation for strategic development in the textile sector. By linking dynamic capabilities theory with organizational principles, this analysis contributes to a deeper understanding of how firms can leverage their internal and external resources to adapt to changing environments and meet emerging market demands, which is crucial for sustainable growth in competitive industrial contexts such as Arequipa-Perú

6.1 Limitations and future lines

Despite the provisions made in the development of this study, we should comment that, within the limitations in the elaboration of the study, it is a cross-sectional study, so that longitudinal data cannot be reported, although for the fulfillment of the objectives set, it is considered sufficient. On the other hand, we have carried out tests to validate the scales used; there could be a possibility of not having eliminated certain bias.

Finally, this article suggests some future lines of research focused on analyzing the inclusion of environmental factors in the relationship of the various dynamic capabilities to achieve better product performance and business results.

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• Data Availability
  All data reported in this paper will be shared by the corresponding author upon reasonable request.

Appendix 1

New product performance Importance of the number of product or service innovations, level of importance for your company. Importance of the profitability of new products or services, level of importance for your company. Importance of sales of new products or services, level of importance for your company. Innovation capability We have a corporate culture that promotes innovation. We can use knowledge from various sources to develop products/services efficiently and quickly. We can identify market changes and apply them to our own products, services, and processes quickly. Our employees can contribute to activities such as product/service development, improvement of the innovation process and development new ideas. We can evaluate new ideas from customers, suppliers and take them into account in the development of products and services. Potential absorption capacity We seek relevant information about our industry daily. Our managers motivate employees to use sources of information in our industry. Our managers expect employees to detect information from outside our sector. In our company, ideas and concepts are communicated between departments. Our managers emphasize cross-departmental support to resolve problems. In our company there is a rapid flow of information, if one department obtains important information, it communicates it immediately to all departments. Our managers ask for regular interdepartmental meetings to share new developments, problems and achievements. Knowledge transformation Our employees have the ability to structure and use collective knowledge Our employees enable the absorption of new knowledge, its preparation for the future and its application. Our employees successfully link existing knowledge with new insights Our employees are able to apply new knowledge in their practical work

New product performance

Importance of the number of product or service innovations, level of importance for your company.

Importance of the profitability of new products or services, level of importance for your company.

Importance of sales of new products or services, level of importance for your company.

Innovation capability

We have a corporate culture that promotes innovation.

We can use knowledge from various sources to develop products/services efficiently and quickly.

We can identify market changes and apply them to our own products, services, and processes quickly.

Our employees can contribute to activities such as product/service development, improvement of the innovation process and development new ideas.

We can evaluate new ideas from customers, suppliers and take them into account in the development of products and services.

Potential absorption capacity

We seek relevant information about our industry daily.

Our managers motivate employees to use sources of information in our industry.

Our managers expect employees to detect information from outside our sector.

In our company, ideas and concepts are communicated between departments.

Our managers emphasize cross-departmental support to resolve problems.

In our company there is a rapid flow of information, if one department obtains important information, it communicates it immediately to all departments.

Our managers ask for regular interdepartmental meetings to share new developments, problems and achievements.

Knowledge transformation

Our employees have the ability to structure and use collective knowledge

Our employees enable the absorption of new knowledge, its preparation for the future and its application.

Our employees successfully link existing knowledge with new insights

Our employees are able to apply new knowledge in their practical work

Edited by

• Editor-in-chief:
  Márcia D’Angelo

Data availability

All data reported in this paper will be shared by the corresponding author upon reasonable request.

Publication Dates

• Publication in this collection
  17 Aug 2026
• Date of issue
  2026

History

• Received
  17 Oct 2023
• Reviewed
  06 Apr 2025
• Accepted
  21 Apr 2025