Enhancing Firm Performance: How Entrepreneurial Orientation and Information Technology Capability Interact

Abstract

Applying information technology capabilities (ITC) to business processes is critical in the current data and information age. However, various bottlenecks and barriers restrict the adoption of ITC. To address these obstacles, fostering an entrepreneurial orientation (EO) can be beneficial. Drawing on the dynamic capabilities view, this study develops and tests a model that empirically analyzes how EO influences the adoption of ITC. The proposed model also assesses the separate and joint effects of EO and ITC on firm performance (FP). The research hypotheses were tested using a survey of 157 Chilean firms, and the PLS-SEM method was applied. The findings indicate that EO promotes the development of ITC and that the synergistic relationship between the two positively affects FP. These findings provide valuable insight for managers in terms of aligning their strategic approach with technological capabilities to enhance their company’s success.

1. Introduction

In the era of data and information, businesses strive to remain competitive in the long term. To achieve this, they must adopt tools efficiently and effectively to fully leverage one of the most valuable assets: data [1]. The exponential growth in the availability and utilization of data has led organizations across various industries to invest heavily in data analytics initiatives. Despite this push towards adopting advanced technologies, these investments will likely be ineffective without first investing in the necessary information technology (IT) for data collection, storage, and processing [2]. However, previous research maintains that, in isolation, technology investments do not generate sustainable competitive advantages, which are essential for long-term success under conditions of accelerated change [2,3]. To achieve such competitive advantages, it is imperative that companies have a clear understanding of their current organizational capabilities and also develop unique and difficult-to-replicate capabilities, integrating managerial, technical, and human capital aspects synergistically [2,4,5]. In particular, researchers indicate that organizations must strengthen their IT capabilities (ITC), that is, an organization’s capacity to effectively manage and integrate its IT resources to achieve its objectives [6].

Despite the importance of integrating IT solutions, businesses worldwide face considerable challenges and obstacles, the nature and magnitude of which largely depend on the economic context in which they operate [7]. While these challenges are universal, they are exacerbated in emerging and developing economies by factors such as resource limitations, access to capital, and technological lag [8]. The situation is further aggravated by the skepticism of managers and decision-makers regarding the potential benefits derived from the adoption of new technologies and by the fact that relevant IT skills are primarily concentrated in developed economies [1,9]. Collectively, these factors substantially hinder the development of ITC.

A potential solution to the problem of ITC development, especially in emerging and developing economies, is encouraging an entrepreneurial orientation [9]. Entrepreneurial orientation (EO) can be defined as a firm’s tendency to implement strategies based on proactivity, innovation, and risk-taking [10]. Proactivity enables firms to anticipate technological trends, identify and exploit market opportunities, and adapt swiftly in highly uncertain environments [11,12]. This uncertainty is often greater in emerging and developing economies than advanced ones [13]. EO catalyzes the development of creative solutions and the exploration of innovative ideas, overcoming resource scarcity and limited access to advanced technology and human capital, which are essential for ITC development [11]. Moreover, investment in ITC can lead to uncertain outcomes, underscoring the importance of being willing to take risks [11,14]. Overall, it is argued that the three dimensions consolidate EO as a vital capability in the development of ITC in highly volatile environments [11].

Strong ITCs can be fundamental to the development of advanced capabilities such as small or big data analytics and artificial intelligence capabilities [15]. We argue that ITCs are particularly well-suited for research in companies within emerging and developing economies, where they are more prevalent than other advanced technology capabilities.

Researchers have recognized ITC as a key mediating factor in various organizational dynamics. Evidence suggests that the adoption of ITC enhances the impact of different organizational capabilities, such as artificial intelligence, on firm performance [16]. However, this phenomenon has hardly received any scholarly attention, presenting an opportunity for research aimed at unraveling how ITC can amplify other organizational capabilities.

Empirical evidence shows the individual effects of ITCs and EO on firm performance (FP) [5,17,18], defined as the measure of how well a company achieves its financial and operational goals [19]. However, research must address the combined effects of ITCs and EOs on FP. A pioneering study by Sun et al. [20] demonstrated a positive effect of EO on both ITC and FP in South Korean companies. Given the contextual nature of EO and ITC, further research on firms outside of advanced economies is warranted. Additionally, while Sun et al. [20] examined ITC in conjunction with environmental scanning under the umbrella of dynamic capabilities, we identified a gap in understanding ITC’s specific role in the EO–FP relationship. We aim to fill this gap by analyzing the mediating role of ITC in the EO–FP relationship among companies from emerging and developing economies.

In practical terms, it is valuable to understand how ITC and EO interact and affect FP. Adopting an EO catalyzes more innovative and proactive decision-making, enabling firms to overcome challenges such as limited access to capital, a shortage of skilled labor, and the technological gap [9]. EO fosters a risk-taking culture, which is essential for the early implementation of ITC, enabling companies to position themselves ahead of their competitors [21]. Integrating ITC can result in a better understanding of both internal competencies and market dynamics, facilitating rapid adaptation to changes. This adaptation translates into increased customer value, product and service innovation, and supply chain optimization [22]. Overall, the synergy between EO and ITC boosts economic performance at the firm level and drives macroeconomic development through job creation and a more skilled workforce.

This study focuses on industrial companies operating in emerging economies. These firms play roles that influence both domestic markets and the global economy. The manufacturing industry, for example, contributes about 16% to the global GDP on average [23,24]. A Deloitte survey shows that 76% of manufacturing companies are embracing digitalization trends, and 86% of their leaders foresee the importance of factories integrating technologies like AI, IoT, 5G networks, data analytics, and cloud computing for future competitiveness [25]. This highlights the role of ITC in enabling automation and digital transformation. By adopting new technologies, industrial firms not only transform themselves but also facilitate the incorporation of their economies into globalization and technological advancement, contributing to economic development by leveraging available talent. In this context, the following research questions are posed:

• RQ1. What is the effect of EO on ITC?
• RQ2. What is the effect of ITC on FP?
• RQ3. What are the separate and joint effects of EO and ITC on FP?

To address these questions, we draw on the Dynamic Capabilities View (DCV), as EO and ITC are seen as dynamic capabilities that help firms gain competitive advantages and achieve long-term success [4]. Empirical evidence was gathered from firms in Chile, which is particularly interesting as it is the best-prepared country in Latin America for transitioning into a digital technology-based economy [26] and represents one of the most productive entrepreneurial ecosystems in the world [27]. It is worth mentioning the presence of the Data Observatory, an initiative aimed at positioning Chile as a global leader in data sciences [28]. We argue that firms should strengthen their ITC before pursuing data science initiatives. Analyzing EO and ITC in the Chilean context offers valuable insights, enabling companies to better understand and capitalize on their business environment.

Based on the background presented, this research aims to explain the separate and joint effects of EO and ITC on the performance of industrial firms established in emerging economies. To this end, three specific objectives were proposed. First, construct the variables EO, ITC, and FP, including the dimensions that constitute them. Second, the model will be tested to determine the effect of EO and ITC on FP. Finally, based on the results, proposals are developed for industrial companies and governmental institutions to enhance EO, technological development through ITC, and, consequently, the performance of the companies.

This paper is structured as follows: The Section 2 presents the literature review and the research hypotheses. Section 3 describes the sample, analyzes the validity of the measurement scales, and introduces the methodology used in this study. Section 4 presents the findings obtained. In Section 5, the results are discussed. Finally, Section 6 summarizes the main contributions of this study, analyzes the theoretical and practical implications of the results, and outlines the main limitations of the research as well as some potential future research directions.

2. Literature Review and Hypotheses

2.1. Theoretical Framework

The Dynamic Capabilities View (DCV) extends the Resource-Based View to explain how firms manage to remain competitive in turbulent environments in the long term [29,30]. It is crucial to note that there is a bifurcation in the literature regarding the definition of DCV [4]. Teece et al. define dynamic capabilities as “the ability to integrate, build, and reconfigure internal and external competencies to address and possibly shape rapidly changing business environments” [29] (p. 516). Conversely, Eisenhardt and Martin define dynamic capabilities as “the organizational and strategic routines through which firms achieve new resource configurations” [30] (p. 1107). The key difference is that Teece et al. see dynamic capabilities as crucial for sustainable competitive advantage in volatile markets, while Eisenhardt and Martin argue they will collapse in such environments [4,29,31]. This contrast arises because Eisenhardt and Martin label as dynamic capabilities what Teece et al. call ordinary capabilities. Ordinary capabilities are also referred to as static or zero-level capabilities, whereas dynamic capabilities are sometimes called higher-order or second-order capabilities [4,31]. We adopt Teece’s conceptualization, distinguishing only between ordinary and dynamic capabilities.

Ordinary capabilities enable firms to achieve efficiency and competitive advantages in stable or low-competition environments, focusing on cost control and best practices, which are easily imitated via the consulting industry [4]. Examples include managerial quality, operational capacity, and marketing capability. On the other hand, dynamic capabilities focus on adapting to technological and market changes and orchestrating resources to drive innovation [4]. These capabilities are built over each company’s history and specific context, making them imperfectly attainable [4]. Examples include knowledge management, environmental scanning, and marketing agility. However, the categorization of capabilities is context-dependent [29,31]. The same capability can be considered ordinary in some studies and dynamic in others. For instance, innovation capability is viewed as an ordinary capability by Zhou et al. [32] but as a dynamic capability by Schweitzer [33].

Dynamic capabilities enable firms to grow and be profitable in the long term, and “when linked to a good strategy and valuable, rare, inimitable, and non-substitutable resources (VRIN resources), they are the necessary and sufficient elements for long-term financial success” [4] (p. 334). Therefore, the DCV is an appropriate theoretical framework for studying FP. Dynamic capabilities stem from entrepreneurial action, managerial creativity, and the ability to communicate vision within the organization [4]. This makes EO a key dynamic capability for ensuring positive and sustainable FP. EO helps firms identify technological opportunities and develop ITC. In turn, ITCs enable the reconfiguration of decision-making and the mobilization of resources in companies, aligning with the definition of dynamic capabilities. In the literature, the DCV is commonly used as a theoretical framework when studying FP, EO, and ITC. The rest of this section will delve into the three constructs and the relationships between them and formulate the hypotheses.

2.2. Firm Performance

Firm performance (FP) is one of the most prominent concepts in organizational and strategic management literature [19]. Generally, FP is considered to reflect how well a company achieves its goals and objectives [34], but its complex and multifaceted nature has led to divergences and debates regarding its theoretical conceptualization and empirical measurement [19], causing inconsistencies that impact scientific rigor [35]. Venkatraman and Ramanujam assert that “improving performance is at the heart of strategic management” [19] (p. 801), emphasizing the importance of academic efforts to understand FP for practical implications. Consequently, various research efforts have sought to enrich the understanding and operationalization of the concept. Below, the main conceptualizations and measurements of performance are detailed.

Miller et al. [35] identify three approaches to conceptualizing and working empirically with performance: the latent multidimensional approach, the separate constructs approach, and the aggregated construct approach. In their meta-analysis, they identified a prevalent inconsistency where most articles conceptualize FP from a latent multidimensional approach but measure it empirically with the separate constructs approach [35]. Recognizing this discrepancy, we seek to align theoretical conceptualization with empirical measurement, considering congruence with the objectives, suitability to the theoretical framework, feasibility, and contextual relevance. We chose the latent multidimensional approach as it provides a holistic understanding that reflects the interconnected and complex nature of modern business dynamics. This approach conceptualizes FP as an abstract and general construct, measured empirically by evaluating both financial and non-financial dimensions. It provides analytical flexibility, allowing the synergistic influence of EO and ITC on FP to be captured by considering it as an underlying construct influenced by a network of interconnected factors rather than isolated outcomes. It is worth mentioning that more recent works have supported the multidimensional nature of firm performance measurement [36]. Identifying capabilities that lead to superior FP is valuable for both academics and practitioners. Next, we will discuss EO, which is widely recognized in the literature as a vital capability [17,18].

2.3. Entrepreneurial Orientation and Firm Performance

Entrepreneurial Orientation (EO) is an organizational attribute that exists when a company is entrepreneurial and has various interpretations and implications depending on the school of thought. This diversity in interpretations has led to different conceptual frameworks, which have fragmented the EO literature [37,38]. Two main schools of thought are identified in the EO literature. On one hand, Miller [39] and Covin and Slevin [40] conceptualize EO as a unitary construct that reflects common qualities among entrepreneurial firms manifested through risk-taking, innovation, and proactivity. On the other hand, Lumpkin and Dess [12] conceptualize EO as a multidimensional construct focusing on unique qualities that distinguish some entrepreneurial firms from others [37]. Lumpkin and Dess expanded the original three dimensions by adding autonomy and competitive aggressiveness. It is worth noting that these two conceptualizations are not contradictory or incompatible [37] and that there is a consensus on viewing EO as a behavioral phenomenon at the company or strategic business unit level [10,38].

We adopted the approach proposed by Miller [39] and Covin and Slevin [40] due to its wider adoption in the literature [38,41]. In the literature, the most common way to study EO is by analyzing its relationship with FP [10], with vast empirical evidence of this relationship using the three dimensions from Miller/Covin and Slevin. An example is the empirical study conducted by Elidjen et al. [42], who evidenced that EO positively affected the performance of Indonesian firms. In the Chilean context, Alonso-Dos-Santos and Llanos-Contreras [43] supported the positive influence of EO on the performance of businesses in a post-disaster scenario.

Given that EO, understood as a dynamic capability, is highly contextual and immersed in organizational routines, it seems crucial to avoid a universalist approach [20]. Expanding the scope of companies examined in the literature to include Chilean industrial companies characterized by innovative culture, diversity, and dynamism would significantly enrich the understanding of how EO can influence performance. Research in similar contexts is very scarce. Considering the strong empirical support from prior studies on the link between EO and firm performance, the following hypothesis is proposed for the companies that will be analyzed:

Hypothesis 1 (H1).

EO has a direct and positive effect on FP.

Explaining only the direct effect of EO on FP provides an incomplete picture. To fully understand the role of EO in organizations, it is necessary to examine the indirect effect it can have through other variables [10]. The literature suggests that EO can lead to the creation and enhancement of other dynamic capabilities, one of which is IT capabilities [20].

2.4. Information Technology Capabilities, Entrepreneurial Orientation, and Firm Performance

The exponential technological advancement, coupled with the increased availability of IT infrastructure, has democratized access to IT tools and resources across all industries [44]. This transformation has catapulted the relevance of IT capabilities (ITC) in both the business and academic spheres [22]. The proliferation of practitioners and researchers in this field has resulted in a variety of interpretations and definitions of ITC, which was originally introduced by Ross et al. [6], defining it as an organization’s ability to utilize technology to achieve strategic objectives. Bharadwaj [22] expanded this definition, describing ITC as a firm’s ability to mobilize and deploy IT-based resources in synergy or conjunction with other resources and capabilities to differentiate from the competition. Another conceptual contribution from Bharadwaj [22] is the identification of three dimensions in ITC: IT infrastructure, IT human resources, and IT-enabled intangibles.

Inspired by Bharadwaj’s conceptualization, numerous studies have emerged that decompose ITC into various dimensions [45]. Among them, Tippins and Sohi [46] propose dimensions such as IT objects, IT knowledge, and IT operations. On the other hand, Lyver and Lu [47] suggest that IT infrastructure flexibility, IT integration, IT-business alignment, and IT management are key dimensions. Although conceptualizations vary, with some being more granular than others, there are points of convergence. Most conceptualizations contemplate the management of IT-based resources, both tangible and intangible, and their integration with other knowledge, skills, and technical capabilities [5]. Therefore, we are focused on three categories: IT managerial capability, IT technical capability, and human capital support.

ITCs require an investment with uncertain returns, especially considering the skill gaps among employees in emerging markets, which pose substantial hurdles to unlocking the full potential of technological advancements. Scholars have shown that these and other barriers can be overcome if organizations nurture an EO, favoring an effective resource orchestration conducive to ITC development [20]. In particular, entrepreneurial-oriented companies tend to take the risks associated with adopting cutting-edge technologies; a proactive attitude can empower companies to cultivate the ITC necessary for uncovering market opportunities, and a willingness to implement innovative solutions can expand existing ITC, promoting their adaptability and growth [9]. Consequently, the second research hypothesis is formulated:

Hypothesis 2 (H2).

EO has a direct and positive effect on the development of ITC.

ITCs have been considered in the literature as a dynamic capability since they enable the detection of opportunities by leveraging information networks, especially in volatile markets characterized by environments with great uncertainty [20,48]. ITC is a key driver of efficiency and productivity in organizations, and there is considerable empirical evidence of the positive effect of ITC on FP [20]. Kamdjoug et al. [49] provided evidence of the effect of ITC on the performance of Cameroonian companies. Sun et al. [20] found supporting results in the context of Korean companies. Similar to the aforementioned cases, Erkmen et al. [5] found evidence of managerial and technical ITC on the performance of Turkish companies. The three previous articles apply the resource-based view. We aspire to follow in their footsteps, but from a different perspective, that of the DCV. Within this framework, the ensuing hypothesis is articulated:

Hypothesis 3 (H3).

ITC has a direct and positive effect on FP.

As mentioned, to better understand the role of EO in organizations, it is crucial to examine its indirect effect on FP or combine it with other variables [10]. This effect operates through a mediating variable, in this case, ITC. Existing literature has focused on the relationship of EO with other constructs that imply technological capabilities commonly present in developed economies, such as big data analytics capabilities [9]. A knowledge gap is identified concerning the role of ITC in the EO–FP relationship in emerging economies [20].

In the literature, ITCs have also been recognized as a key mediating factor in various organizational dynamics. It has been shown that ITC mediates the relationship between dynamic capabilities and organizational agility [50] as well as between alliance management capabilities and business resilience [51]. In the technological domain, Awamleh and Bustami [16] demonstrated that the adoption of ITC acts as a mediator between artificial intelligence and competitive advantage. These findings underscore the strategic importance of ITC as an element that amplifies other organizational capabilities, significantly contributing to business success. However, this topic remains relatively unexplored, indicating a promising avenue for research focused on examining the mediating role of ITC. We propose that EO fosters the development of ITC, and in turn, ITC enables a company to analyze and synthesize various types of information, as well as identify and exploit opportunities in their competitive environments. In essence, ITCs act as amplifiers of the EO–FP relationship. Consequently, the following hypothesis is formulated:

Hypothesis 4 (H4).

ITC mediates the relationship between EO and FP.

The proposed model and its hypotheses are shown in Figure 1.

3. Methods

3.1. Population, Sample, and Data Collection

A cross-sectional data collection system was adopted to empirically test the hypotheses and the research model through a survey administered between June and November 2023. The survey targeted general managers/chief executive officers, area managers, area supervisors, and mid-level executives. These key informants were selected under the assumption that they have a holistic view of the business and are familiar with the company’s strategy [14].

A convenience sampling method was utilized, which is a non-probabilistic technique based on the selection of accessible subjects willing to participate, favoring proximity and accessibility for the researcher [52]. The survey was approved by the Scientific Ethics Committee of the Universidad Católica del Norte (resolution 015/2023).

The sample size was based on a power analysis [53] with a power factor of 0.8 and an alpha of 0.05. The evaluation conducted using the G*Power software version 3.1 resulted in a minimum sample size of 107 [54]. Data were collected by interviewers trained in the instrument and conducted in person at companies across various communes in the Antofagasta Region. A total of 157 valid responses were collected.

Table 1. Sample characteristics.

Respondent Variables	Frequency	Percentage (%)
Age
<30	35	22.3%
30–39	28	17.8%
40–49	46	29.3%
50–59	34	21.7%
>60	14	8.9%
Gender
Male	104	66.2%
Female	53	33.8%
Position in the Company
General Manager/CEO	62	39.5%
Area Manager	25	15.9%
Area Supervisor	29	18.5%
Mid-Level Executive	28	17.8%
Other Managerial Positions	13	8.3%
Industry Experience
2 to 3 years	23	14.6%
4 to 6 years	19	12.1%
More than 6 years	111	70.7%
Less than one year	4	2.5%
Company Variables	Frequency	Percentage (%)
Industry
Mining and Engineering Solutions	84	53.5%
Trade and General Services	73	46.5%
Annual Sales
At least 2400–25,000 UF	81	51.6%
26,000 UF–100,000 UF or more	76	48.4%
City
Antofagasta	64	40.8%
Calama	28	17.8%
Mejillones	17	10.8%
Other Communes II Region	48	30.6%

presents a summary of the demographic characteristics of the sample.

3.2. Measurement Scales

All measurement scales included in the questionnaire derive from scales validated in existing literature and have been specifically adapted to the context of industrial firms established in an emerging economy, such as Chile’s (see Table 2). The dimensions of the three constructs were measured subjectively using a 7-point Likert scale, a method widely accepted in business research [1], as it is more feasible than obtaining objective financial data [55]. For the questionnaire’s development, various instruments to measure each construct were pre-selected, and the most suitable ones were chosen considering their alignment with the research objectives, the phrasing of each question, and the Cronbach’s alpha coefficients obtained in respective studies. The process for selecting the scales for each construct is detailed below.

3.3. Variables

A five-item scale was chosen to measure FP, using a multidimensional latent approach that combines operational and financial dimensions into a single construct. This method aligns with the chosen theoretical framework. It is considered convenient as it can be adapted to different industries, contexts, and performance indicators. A comparative analysis was carried out on four scales, including Chen et al. [21], Tippins and Sohi [46], Aydiner et al. [56], and Shen et al. [57]. The scale developed by Shen et al. [57] was selected due to its alignment with the research goals, clarity of wording, and high reliability.

In the case of EO, relying on proxy indicators from secondary data does not always capture the construct, compromising its validity [37]. This limitation is a primary motivation for measuring EO through primary data, using tailor-made surveys that ideally evaluate the construct [37]. We adapted the scales from Covin and Slevin [40] and Ciampi et al. [14], considering the three dimensions: risk-taking, innovation, and proactivity.

For ITC, five validated scales from various authors were reviewed: Erkmen et al. [5], Lyver and Lu [47], Kala Kamdjoug et al. [49], Bharadwaj [22], and Lu et al. [58]. The scale from Erkmen et al. [5] was selected, which measures IT as a second-order construct composed of three first-order constructs: managerial capability, technical capability, and human capital support. Table 2 shows Cronbach’s Alpha, authors, and contexts from which the scales were adapted.

Table 2. Measurement scales.

Construct	Authors		Context
EO CA 1 = 0.890	Covin and Slevin, 1989 [40] and Ciampi et al., 2021 [14]		Business model innovation in the United Kingdom
FP CA = 0.899	Shen et al., 2020 [57]		Performance of manufacturing companies in China
MITC CA = 0.90	Adapted from Han et al., 2008 [59]	Erkmen et al., 2020 [5]	Sustainable IT in Turkey
TITC CA = 0.93
HCS CA = 0.92	Adapted from González-Benito, 2007 [60]

¹ CA = Cronbach’s Alpha.

Table 2. Measurement scales.

Construct	Authors		Context
EO CA 1 = 0.890	Covin and Slevin, 1989 [40] and Ciampi et al., 2021 [14]		Business model innovation in the United Kingdom
FP CA = 0.899	Shen et al., 2020 [57]		Performance of manufacturing companies in China
MITC CA = 0.90	Adapted from Han et al., 2008 [59]	Erkmen et al., 2020 [5]	Sustainable IT in Turkey
TITC CA = 0.93
HCS CA = 0.92	Adapted from González-Benito, 2007 [60]

¹ CA = Cronbach’s Alpha.

3.4. Statistical Techniques

Structural equation models are a family of statistical techniques that have become very popular in the business and social sciences [61]. Their ability to model latent variables, account for various forms of measurement error, and test complex theories makes them useful for a wide range of research questions [62]. In this study, a structural equation model with partial least squares (PLS-SEM) is proposed to test the reliability, validity, and hypotheses. PLS-SEM first creates proxies as linear combinations of observed variables and then estimates the model parameters using these proxies [61]. A primary reason for the appeal of PLS-SEM is that the method allows researchers to estimate very complex models with many constructs and indicator variables. Moreover, PLS-SEM generally offers a lot of flexibility in terms of data requirements and the specification of relationships between constructs and indicator variables [63].

To evaluate the indicators and the structural model, the procedures suggested in the previous literature were followed [61,64,65]. In the case of the multidimensional construct ITC, composed of three first-order constructs: managerial IT capability (MITC), technical IT capability (TITC), and human capital support (HCS), a two-step approach was used [65], wherein in the first stage, the aggregated scores of the first-order dimensions are estimated, and in the second stage, the aggregated scores are used to model the second-order construct. To assess the measurements and the structural model, the procedures suggested in the previous literature were employed [61,64,65]. For the analysis of the mediator, the procedures suggested by Nitzl et al. [66] were used. The data were analyzed using SmartPLS 4 software [67].

4. Results

The procedure carried out to obtain the results was based on the steps for estimating a component-based model proposed by Wright et al. [65], consisting of the following: running the first-order model (1), assessing reliability (2), evaluating convergent validity (3), assessing discriminant validity (4), creating a new data file with the scores of the latent variables (5), constructing the second-order factor with the latent variables as indicators (6), running the structural model (7), and evaluating the results of the structural model (8).

4.1. Measurement Model Evaluation

To analyze the instrument, the criteria of reliability, convergent validity, and discriminant validity were evaluated.

Table 3. Loadings, Cronbach’s Alpha (CA), composite reliability (Rho_A, Rho_C), average variance extracted (AVE).

Construct	Items	Loadings	CA	Rho_a	Rho_c	AVE
MITC	We understand the IT needs and expectations within our company (MITC1).	0.754	0.921	0.944	0.940	0.759
	We have the capacity to meet the IT needs and expectations within our company (MITC2).	0.885
	We have the ability to leverage IT as a core strategic competence (MITC3).	0.920
	The IT strategy is aligned with the business strategy (MITC4).	0.892
	We have the capability to continuously update the IT strategy in accordance with changes in the business environment (MITC5).	0.894
TITC	We possess IT technical knowledge and skills (TITC1).	0.889	0.927	0.937	0.944	0.773
	We understand the role of IT in improving business performance (TITC2).	0.873
	We have our scheme for IT standardization (TITC3).	0.893
	We have the capacity to integrate IT (TITC4).	0.899
	We understand IT trends (TITC5).	0.839
HCS	In our company, employees undergo training programs on new software and systems (HCS1).	0.902	0.918	0.927	0.942	0.802
	In our company, employees undergo training programs on new equipment and tools (HCS2).	0.894
	In our company, we prefer to hire qualified individuals who can effectively use new technologies and systems (HCS3).	0.901
	In our company, we emphasize the use of information and communication technologies to support communication flow with our customers and suppliers (HCS4).	0.884
EO	In the last two years, our company has typically initiated actions to which competitors later responded (EO1).	0.710	0.712	0.750	0.818	0.531
	In the last two years, our company has been very often the first to introduce new products/services, administrative techniques, and operational technologies (EO2).	0.790
	In the last two years, our company has had a strong inclination towards high-risk projects (with chances of very high returns) (EO3).	0.705
FP	Our company has experienced considerable growth in customer satisfaction over the last two years (FP1).	0.852	0.901	0.906	0.927	0.718
	Our company has experienced considerable growth in its competitive position in the market over the last two years (FP2).	0.905
	Our company has seen significant improvements in product/service quality over the last two years (FP3).	0.886
	Our company has seen significant improvement in product/service innovation over the last two years (FP4).	0.746
	Our company has experienced considerable growth in its overall financial performance over the last two years (FP5).	0.839

displays the loadings of each observed variable, Cronbach’s alpha coefficient (CA), composite reliability (CR), and the average variance extracted (AVE) for each construct. The results for Cronbach’s alpha (CA) and composite reliability (CR) ensure the scales’ reliability. The Cronbach’s alpha results range between 0.712 and 0.936, surpassing the recommended value of 0.7 for scale robustness. Moreover, two measures of composite reliability are reported: Rho_A, ranging between 0.750 and 0.944, and Rho_C, with values between 0.818 and 0.944. Both measures exceed the recommended threshold of 0.7 [61], indicating that the constructs exhibit a high level of internal consistency. To assess convergent validity, the loadings of each item and the AVE were examined. The item loadings were greater than 0.7 [64]. The average variance extracted ranges between 0.531 and 0.802, above the accepted level of 0.5 [68]. The results suggest adequate convergent validity for all latent constructs.

To assess discriminant validity, the Fornell-Larcker criterion test was utilized [64]. Fornell and Larcker [64] suggest that discriminant validity can be assessed by examining whether the square root of the average variance extracted (AVE) is greater than the correlations among other constructs. As shown in

Table 4. Discriminant Validity, Fornell–Larcker criterion.

	HCS	MITC	TITC	FP	EO
HCS	0.895
MITC	0.711	0.871
TITC	0.692	0.839	0.879
FP	0.397	0.563	0.458	0.847
EO	0.547	0.570	0.509	0.586	0.729

, all the values on the diagonal exceed the correlations between constructs. The results indicate adequate discriminant validity.

4.2. Second-Order Construct Evaluation

To assess the second-order construct in PLS, the loadings, average variance extracted (AVE), composite reliability, and Cronbach’s alpha of the latent scores of the first-order constructs in ITC were evaluated. The statistical analysis represented in

Table 5. First-order loads in the second-order ITC construct.

Second Order Construct	Items	Loads	AVE	CR	CA	Rho_A
ITC	MITC	0.940	0.899	0.909	0.937	0.832
	TITC	0.924
	HCS	0.870

evaluates the reliability and validity of the second-order construct, asserting that ITC is a reflective second-order construct composed of three first-order latent constructs as proposed by Erkmen et al. [5], including IT management capability, IT technical capability, and human capital support.

Discriminant validity for the second-order construct was assessed using the Heterotrait–Monotrait ratio (HTMHT) and Fornell–Larcker criteria, as shown in

Table 6. Discriminant Validity. Heterotrait—Monotrait ratio (HTMHT) criterion.

	ITC	FP	EO
ITC
FP	0.572
EO	0.704	0.703

and

Table 7. Discriminant Validity, Fornell–Larcker criterion.

	ITC	FP	EO
ITC	0.912
FP	0.523	0.847
EO	0.592	0.587	0.729

, respectively. Both tables confirm sufficient discriminant validity.

4.3. Structural Model

To evaluate the structural model with the second-order variable following the steps proposed by Wright et al. [65], the aggregated scores were used to model the second-order construct of ITC. The evaluation of the model’s goodness of fit should be performed at the beginning of the model evaluation before examining the structural model [61]. To assess the goodness of fit, the SmartPLS 4 software was used [67], which provides the standardized root mean square residual (SRMR) as the appropriate measure for model fit. For the proposed model, the SRMR value is 0.079, a result that indicates a good model fit [69]. The proposed structural model is evaluated through the path loadings and the R² values. The PLS bootstrapping technique was applied using 10,000 samples, as suggested by Streukens and Leroi-Werelds [70], and the path loadings and p-values for the relationships described in the hypotheses were calculated. The results and paths are shown in Figure 2.

Table 8. Results of Hypothesis.

Hypothesis	Path	Path Coefficient	p-Value	Result
H1	EO → FP	0.426	***	Supported
H2	EO → ITC	0.592	**	Supported
H3	ITC → FP	0.271	**	Supported

** p < 0.01; *** p < 0.001.

shows the results of the hypotheses, and all the proposed direct relationships are supported by appropriate path coefficients and p-values.

The R² values represented in Figure 3 suggest that the model explains 39.2% of the FP and 35.1% of the ITC.

4.4. Mediation Result

To examine whether the effect of EO on FP through ITC is explained through the mediating effect, a bootstrapping approach was used, a resampling procedure that does not assume the normality of the sample distribution. Figure 2 shows that EO has a significant total effect on FP. When the mediator is added (see Figure 3), EO experiences a reduction in effect but still maintains a significant direct effect on FP.

Figure 2. Model with total effect; *** p < 0.001.

Figure 2. Model with total effect; *** p < 0.001.

Figure 3. Model with two-step mediation design; ** p < 0.01; *** p < 0.001.

Figure 3. Model with two-step mediation design; ** p < 0.01; *** p < 0.001.

The assessment of the mediation results according to the procedures suggested by Nitzl et al. [66] indicates that the proposed mediator represents a partial mediation, specifically a complementary partial mediation. This validates Hypothesis 4, corroborating that a portion of the effect of EO on FP is mediated by ITC, while EO continues to explain a portion of FP that is independent.

5. Discussion

The present research aims to provide empirical evidence of the positive effect of EO on the adoption of ITCs and the positive effect of both constructs on FP. Additionally, the mediating role of ITC in the relationship between EO and FP is analyzed. For this purpose, the DCV was adopted as the theoretical framework, which is widely used in strategic management and particularly useful in understanding the role of IT in creating business value. The proposed objectives were successfully met; the main findings of the study are discussed below.

Through the DCV approach, the proposed hypotheses were validated, confirming that dynamic capabilities have positive effects in moderately dynamic environments [71], as is the specific case of Chile. Firstly, it was found that EO has a direct and positive effect on firm FP (H1 accepted). This result aligns with previous meta-analyses that demonstrated a strong association between EO and FP [17,18]. The findings suggest that companies adopting a strategic stance based on three dimensions from Miller [39]/Covin and Slevin [40]—risk-taking, proactivity, and innovation—tend to perform better, supporting previous literature such as Elidjen et al. [42] and Alonso-Dos-Santos and Llanos-Contreras [43].

Secondly, it was found that EO has a direct and positive effect on the development of ITC (H2 accepted), which aligns with the perspective that EO is a vital management capability within the domains of technology [1]. The results confirm that EO promotes the proactive search for technological innovations and the pursuit of market opportunities related to IT that can potentially bring substantial returns [72]. This also aligns with the findings of Sun et al. [20], who were pioneers in providing empirical evidence of the positive effect of EO on ITC.

Thirdly, it was demonstrated that ITC has a direct and positive effect on FP (H3 accepted), validating that ITC can help companies outperform their competitors by enabling timely access to information and knowledge [20]. These findings align with previous research by Kamdjoug et al. [49], Sun et al. [20], and Erkmen et al. [5].

Lastly, the findings show that ITC mediates the relationship between EO and FP (H4 accepted), suggesting that EO influences the development of ITC, which in turn positively affects FP. The partial complementary mediation indicates that ITC plays a key intermediary role as a bridge, amplifying the positive effects of EO, but EO also influences FP independently and possibly through other mechanisms. It is important to note that previous research, such as that conducted by Sun et al. [20], has analyzed how ITC and knowledge management together have a mediating effect on the EO–FP relationship. However, to the best of our knowledge, this is the first time that the specific mediating effect of ITC in this relationship has been independently analyzed. This distinction corroborates the vital importance of combining EO and ITC to enhance FP. In line with the findings of Sun et al., our results suggest that firms with a strong EO can enhance their performance by effectively developing their own ITC capabilities [20].

6. Conclusions

This research has been motivated by the growing interest of companies in integrating IT solutions, especially given the challenges and obstacles inherent in their implementation in some economies. Starting from the premise that to fully capitalize on advanced information and communication technologies—such as data analytics, big data, cloud computing, blockchain, and artificial intelligence—it is essential to implement and develop basic capabilities, specifically ITC. The present study facilitates a deeper understanding of a strategic stance through which the development of such technological capabilities can be enhanced, specifically EO, which is a dynamic capability that prepares organizations to leverage emerging technologies. Furthermore, this work expands the understanding of the interrelationships between ITC, EO, and FP.

6.1. Theoretical Implications

The current research offers theoretical contributions at the intersection of strategic management and information systems literature. Firstly, this research is consistent with the literature by validating EO and ITC as dynamic capabilities that enable companies to achieve superior performance in volatile environments [29].

Furthermore, this research addresses the interaction between the three variables analyzed. Although Sun et al. [20] were pioneers in this analysis, their research focused on South Korea, a nation renowned for innovation and technological development. Since the impact of ITC and EO can vary greatly depending on the socio-economic context, our research expands on Sun and colleagues’ findings by examining their applicability in less advanced countries such as Chile.

Thirdly, we address a gap in the existing literature regarding the positive mediating effect exerted by ITCs in the EO–FP relationship, suggesting that EO facilitates ITC’s adoption in firms, which in turn translates into an improvement in performance. More specifically, companies with a high degree of innovation, proactivity, and risk-taking are more likely to invest in the necessary assets to effectively manage and integrate their IT resources, including human capital and technical and managerial capabilities. Together, these elements constitute ITC, which equips companies to handle information and knowledge in a timely manner, leading to financial and operational outcomes superior to the competition [20].

6.2. Managerial Implications

The findings offer valuable insights for managers and decision-makers. Firstly, the results suggest that organizations can improve their performance by encouraging innovation, proactivity, and a willingness to take risks. In other words, companies should adopt an EO. It is worth mentioning that our research took the Miller/Covin and Slevin approach, so we argue that top management should encourage innovation, and incentive programs that recognize and encourage ideas could be an option. Likewise, promoting intrapreneurship initiatives stemming from these ideas is recommended. In terms of proactivity, actively seeking financing from traditional (such as bank loans or issuing shares) and non-traditional sources (like crowdfunding, forming strategic alliances with other companies, and angel investor networks, among others) is advisable. Regarding risk-taking, companies may explore investing in cutting-edge solutions offered by promising startups that leverage emerging technologies. Such investments could be a catalyst for ITC development.

In line with the above, companies must develop a robust ITC. This process involves the synergistic interaction of three fundamental pillars. Firstly, strengthening IT management capabilities is achievable through the effective integration of emerging technologies in strategic planning and business decision-making processes. Secondly, the consolidation of IT technical capability requires continuous investment and the maintenance and periodic updating of advanced technological infrastructure. Finally, support for human capital entails hiring highly skilled personnel in the field of IT and continuously training and developing such personnel, both at the technical and managerial levels. These elements, collectively, form a comprehensive strategy that enhances the company’s innovative and competitive capacity in the technological realm. Applying these strategies would enable companies to establish a synergistic relationship between EO and ITC, leading to improved firm performance.

6.3. Limitations and Future Research Areas

Despite its contributions, our research is not without limitations. Readers should generalize the results cautiously, considering that the data were collected from industrial companies located in a mining region of northern Chile. It is essential for future research to broaden the scope to include various industries and countries. Moreover, this study groups companies of all sizes, which limits the visibility of significant differences between these groups. A multigroup analysis in future research is recommended to explore discrepancies between companies based on size or to investigate a specific segment. Additionally, future research studies could also take a longitudinal approach to provide insights into how the relationships between EO, ITC, and FP evolve over time and incorporate probabilistic sampling methods, thereby increasing the generalizability of the findings and reducing selection bias.

Another critical aspect is the low level of digitalization observed in smaller companies within the analyzed region. The limited exposure to IT among some respondents may lead to a biased valuation of ITC. Future research should focus on strategies to enhance the implementation of information technologies in areas with low digitalization. Future studies could also explore effective strategies for the implementation of IT in low-digitalization zones.