Does Innovation Mediate the Relationship Between ICT Use and Competitive Advantage? Investigating the Perceptions of Practitioners in the Pharmaceutical Industry

Abstract

The aim of this study is to examine whether ICT and innovation resources can be utilized to facilitate the competitiveness of both pharmaceutical firms and contract research organizations (CROs). By using structural equation modelling based on survey data collected from 683 pharmaceutical practitioners in China, we find that there is a positive relationship between ICT and competitive advantage. Innovation mediates the documented relationship. When comparing hypothesized relationship across different respondents, CROs’ respondents exhibit a full-mediation effect of innovation, while pharmaceutical firms’ respondents present a partial-mediation effect. Our study sheds new light on the “ICT use – competitive advantage” nexus from the innovation perspective, and offers valuable insights for managers and policymakers in better leveraging ICT and innovative resources to achieve competitive advantage.

Keywords

ICT innovation competitive advantage pharmaceutical firms contract research organizations resource-based and dynamic capabilities view

Introduction

Given the rapid changing nature of business landscape, innovation is recognized as a crucial instrument for enterprises, essential not only for surviving in the market but also for gaining continuous competitive advantage. Fundamentally, innovation stems from resources that are valuable, rare, and nonsubstitutable (J. Barney, 1991; Zhang et al., 2023), and positively associates with organizational performance. In fact, numerous companies fail to effectively innovate and utilize innovation to improve performance (Rousseau et al., 2016). This is particularly important for the pharmaceutical industry as the sustainable growth of pharmaceutical organizations largely relies on ongoing research productivity of new medication. Many large economies have allocated significant funds to support innovation in the pharmaceutical sector. For instance, China has spent over 5.9 trillion Chinese yuan (approx. 0.91 trillion U.S. dollar) on healthcare in 2019, accounting for over 6.6% of the total China’s GDP (National Health Commission of China, 2020). Of which, the expenditure related to pharmaceuticals procurement arrived at 991,300 million Chinese yuan (approx. 153,400 million U.S. dollar), representing over 16.8% of the total healthcare costs (National Healthcare Security Administration of China, 2020).

Pharmaceutical firms commonly invest substantial resources in research and development (R&D) process, but they still face high degree of technological and market uncertainties (Rogers et al., 2002). To mitigate pharmaceutical firms’ financial and labor pressures of R&D, a pattern of organization emerges, termly contract research organizations (CROs). They are outsourcing providers for pharmaceutical firms’ research activities. Generally, CROs help pharmaceutical firms accomplish medication research projects at a lower cost. Today, CROs commonly engage in pharmaceutical firms’ four stages of R&D activities, including drug discovery, pre-clinical research, clinical trial, and authority review (Hassanzadeh et al., 2014; Q. Wu & He, 2020). Given their increasingly strategic connections with pharmaceutical firms, CROs play a critical role in facilitating and expanding the pharmaceutical R&D process (Lowman et al., 2012). This role is particularly significant in emerging markets such as China, where the pharmaceutical industry is experiencing rapid growth. CROs are essential for navigating the complex regulatory landscape, managing large-scale clinical trials, and providing the infrastructure necessary to support innovation. Despite the growing recognition of CROs as strategic partners in pharmaceutical R&D, much of existing studies have focused primarily on pharmaceutical firms, with limited attention given to the specific role of CROs in driving innovation, particularly in emerging markets. This gap in the literature hinders a comprehensive understanding of how CROs contribute to innovation in the fast-growing pharmaceutical industry.

Information and communications technology (ICT) encompasses all digital technologies that aids business in utilizing information. It has been widely incorporated into corporate strategy, allowing firms to establish robust network with cooperative alliances (Rohrbeck, 2010; Sheng et al., 2013). ICT facilitates business functions across both internal operations and customer services, thereby driving efficiency and enabling data-driven decision-making throughout the supply chain. In the pharmaceutical industry, ICT is extensively used to manage inventory, automate supply chain processes, and enhance the customization of medical products and services, ultimately reducing waste and enhancing overall operational performance (Balta et al., 2021; May, 2013; Murray et al., 2011). Even though ICT can be regarded as either a tangible or intangible resource, prior research argued that physical ICT system is easily imitated by rivals, which has no effect on firm performance improvement (Yang et al., 2015). Teece et al. (1997) claim that tangible information technology assets represent the most volatile source of competitive advantage. Accordingly, whether ICT used in the pharmaceutical industry is a nonsubstitutable resource, and whether it can influence the organization’s competitive advantage are worthy of further investigation.

The strategic management literature largely uses both resource-based view (RBV) and dynamic capabilities view (DCV) to explain the interrelationships among ICT use, innovation and competitive advantage. Specifically, RBV considers resources as heterogenous and non-duplicatable that can used by organizations to achieve competitive advantage (J. Barney, 1991; Newbert, 2008). DCV is an extension of RBV, emphasizing the crucial role of capabilities in constructing, integrating, and reconfiguring resources in a highly changing environment (Reuter et al., 2010; Teece et al., 1997). Both of these theories constitute the grounds for enterprises to gain and sustain competitive advantage. We therefore use RBV and DCV as theoretical underpinnings to address the following two questions:

(1) How does ICT use influence competitive advantage in the pharmaceutical industry?

(2) How does innovation mediate the relationship between ICT use and competitive advantage in the pharmaceutical industry?

Our study makes the following contributions. First, even though previous studies have discussed the benefits of ICT or innovation on business performance (e.g., Dymitrowski & Mielcarek, 2021; Yang et al., 2015; Yunis et al., 2018), little research probe into the interplays among ICT, innovation, and competitive advantage. Through combining RBV and DCV, we examine whether innovation can catalyze ICT resources in facilitating organization’s competitive advantage. Our results indicate that innovation acts as a channel mechanism, thus theoretically and empirically complementing the strategic management literature. Second, while many studies have investigated innovation in pharmaceutical firms, research focusing on CROs, a critical actor in the pharmaceutical R&D process, remains limited. In fact, pharmaceutical R&D spending in China is growing at a rate faster than the global average (Xu et al., 2021), highlighting the increasing significance of this sector. In this context, we examine the differential impact of ICT use, innovation, and competitive advantage between respondents from pharmaceutical and those from CROs. This comparison provides valuable insights into how these two types of organizations leverage ICT and innovation to foster competitive advantages. Our study advances the understanding of the pharmaceutical R&D ecosystem and offer a unique perspective on the dynamic interplay among various stakeholders within the pharmaceutical industry.

The rest of the paper is organized as follows. Section 2 constitutes the institutional background. Section 3 depicts the theoretical underpinnings. Section 4 demonstrates the methods. Section 5 reports the results. Section 6 unfolds the discussion and concludes the study.

Institutional Background: Innovation and ICT Use in Chinese Pharmaceutical Industry

With the announcement of “New Drug Generation and Enhancement Program” in 2008, the Chinese government has increasingly emphasized the essential role of drug development, and regarded biopharma as one of the key emerging sectors (Hughes, 2010). The central government invested roughly 2 million U.S. dollars to discover new drugs under the national agenda so-called “12th Five-Year Development Plan” for 2011 to 2015 (Xia & Gautam, 2015). Pharmaceutical firms in China also substantially invest in R&D activities. The amount of R&D expenditure increased from 43 million U.S. dollars to 8987 million between 2005 and 2018, making China become one of the largest economies with fastest growth in medical R&D investments (National Bureau of Statistics of China, 2019a).

Nevertheless, more than 70% of pharmaceutical manufacturers in China are small-scale enterprises with less than 30 employees (National Bureau of Statistics of China, 2019b). These pharmaceutical firms are lack of sufficient financial and human resources to support technical innovation, and may outsource R&D activities to CROs (Ni et al., 2017). CRO market value in China was merely 0.71 million U.S. dollars in 2007, but dramatically increased over years with about 8.3 million U.S dollars in 2020 (Ministry of Science and Technology of China, 2021). CROs can build a bridge among pharmaceutical companies, academic research units and hospitals where pharmaceutical firms can restructure preclinical and clinical process and significantly cut R&D costs (Shi et al., 2014).

The Chinese pharmaceutical industry has experienced rapid digital transformation in last decade. ICT-pharmacy creates a variety of online pharmaceutical platforms, making the information of drug to be more transparent and drug selection to be more market-oriented (Deloitte, 2016). Pharmaceutical firms can utilize E-commerce data to evaluate drug flow and sale volume, and store user health files in the cloud platform. CROs can also enhance R&D capacity by using valuable and updated Big Data (Wang et al., 2014). Patient users may use E-platform to achieve instant medical consultation and better choose the most cost-effective drugs. Thus, the use of ICT facilitates real-time information exchange and online interactions among doctors, patients, scientists, and administrative staffs. Traditionally, Chinese healthcare largely relies on hospital setting, but ICT-pharmacy creates an integrated E-medical mechanism which covers instant diagnosis, health advisory, rehabilitation assistance, and post-diagnosis patient monitoring (Zhou & Sun, 2022).

Theoretical Underpinnings

Innovation in Context

Innovation is frequently incorporated into the visions, strategies, and objectives of organizations. It is a multifaceted concept that involves the creation and implementation of new ideas, processes, products, or services, all of which generate value for both organizations and society. Schumpeter (1934), often regarded as the founder of innovation theory in economics, define innovation as the economic impact of technological changes and the application of new combinations of products and services to address business problems. Urabe et al. (1988) further expand this view, arguing that innovation is not a one-time event but a continuous and cumulative process. It encompasses numerous organizational decision-making steps, ranging from the generation of new ideas to their eventual implementation. More recently, Tidd and Bessant (2005) provide a comprehensive perspective on innovation, defining it as the integration of new ideas and productive forces that enables organizations to meet changing market demands and enhance their competitive position. This definition aligns with the broader view that innovation is critical for the survival and sustainable growth of organizations in a dynamic environment (OECD, 2005).

Specifically, innovation can be categorized in various ways according to its scope and focus. One of the most widely recognized types is product innovation, which involves the development of new products or significant improvements to existing ones. In recent decades, the emphasis in product innovation has shifted from internal process to external actors, highlighting the growing importance of user-centered and use-led innovations (Von Hippel, 2005). Another popular type is process innovation, which entails the adoption of new or significantly improved production or delivery methods. The global rise of design thinking and lean thinking has played pivotal role in facilitating process innovation, driving a paradigm shift in many industries. This shift has moved manufacturing process from standardized approach to more flexible system (Lager et al., 2015). A more recent type of innovation is business model innovation, which refers to the transformation of organizational methods in business practices. According to Amit (2012), business model innovation represents a new dimension of innovation, with ICT acting as a catalyst for the creation of new business models.

The pharmaceutical industry invests heavily in R&D and is widely recognized as one of the most innovative sectors in the market. This innovation is driven by technological advancements and the adoption of new methodologies, which are essential for addressing evolving healthcare needs and improving patient outcomes (DiMasi et al., 2016). Traditionally, the industry has focused on product innovation, particularly through the introduction of new drugs and therapeutic classes. With the growing demands for treatment tailored to the individual needs of patients, personalized medicine or precision medicine has emerged as a key focus within product innovation (Schork, 2015). In addition to product innovation, the pharmaceutical industry also emphasizes process innovation, which aims to enhance the efficiency and competitiveness of drug development, manufacturing, and distributing. This type of innovation is crucial for meeting regulatory requirements and responding to market dynamics. Scholars have highlighted the significance of continuous manufacturing as an example of process innovation. It allows for the production of pharmaceutical products in a continuous flow rather than in batch processes (S. L. Lee et al., 2015). Today, technological innovation has further reshaped the drug discovery process and market access through the use of artificial intelligence (AI), machine learning, big data analytics, biotechnology, and other cutting-edge techniques (Vamathevan et al., 2019). These advancements have not only enhanced the pace of drug development but also expanded the scope of possibilities for addressing complex healthcare challenges.

Driving Competitive Advantage: Resource-Based View (RBV) and Dynamic Capabilities View (DCV)

Competitive advantage refers to the capacity achieved through exclusive attributes and resources to outperform rivals within the same industry or market. To understand how an organization establish and maintain competitive advantage, RBV is a commonly used approach that mainly analyses and interprets resources available for the use of organization (J. Barney, 1991). Resources are characterized as heterogenous, and thus difficult for competitors to replicate. There are various types of resources according to the theoretical framework of RBV, including assets, organizational process, information, and knowledge under the firm’s control (J. Barney, 1991; Lin & Wu, 2014). RBV defines these resources as valuable, rare, inimitable, and nonsubstitutable (J. B. Barney, 1986). Nevertheless, many scholars criticize that RBV only considers resources as static and may hardly be applied in the era of dynamic and turbulent economy.

As an extension of RBV, DCV is more relevant to today’s highly volatile environment where marketplace and industry landscape are rapidly changing. Teece et al. (1997) suggest that dynamic capabilities are a set of processes, routines, and strategic actions that firms can develop, structure, and reconstitute resources based on actual market situations. DCV can better explain why firms are superior to their rivals by securing sustainable competitive advantages in dynamic environments. Lin and Wu (2014) further point out that learning capability is the core of DCV. Through a learning mechanism, firms can absorb both internal experiences and external information that effectively transform valid resources into improved corporate performance. Without learning, firms are unable to effectively identify emerging opportunities, reallocate existing resources, or drive innovation. Accordingly, learning can be regarded as a foundation element of dynamic capabilities (Lin & Wu, 2014).

According to DCV, the ability of a firm to sustain its competitive advantage depends on its capacity to learn from past experiences, absorb new knowledge, and apply that knowledge to innovation. Through learning, firms can innovate products, services and business models, continuously evolving their capabilities in response to external changes (Tidd & Bessant, 2005). In this framework, the learning process underpins innovation, which is a key outcome of dynamic capabilities. More specifically, a firm’s capacity for innovation is closely linked to its ability to engage in continuous learning and adaption (Moustaghfir & Schiuma, 2013). Innovation, in turn, often requires organizational learning and the development of new knowledge and capabilities, which enhances a firm’s competitive advantage by ensuring its responsiveness to both external market conditions and internal resource dynamics.

The role of ICT lies in facilitating the acquisition, integration, and dissemination of valuable resources, all of which are essential for enhancing a firm’s learning capabilities. A firm’s capacity to learn from ICT-driven data and insight further strengthen the innovation process. There is a bulk of studies investigating whether ICT is a valuable resource in enhancing firms’ competitiveness and explore how dynamic capabilities exert mediating effects. Focusing on the healthcare industry in Taiwan, Sheng et al. (2013) document that ICT competencies can improve knowledge transfer and alleviate knowledge barriers, thus securing organizational competitive advantage. Yang et al. (2015) manifest that ICT investment enhances British SMEs’ performance. This improvement can be realized through the integration of e-commerce resources with other organizational resources and capabilities. Using 102 Colombian high-tech firms as the sample, Acosta-Prado and Tafur-Mendoza (2022) examine the relationships among ICT use, dynamic capabilities, and sustainable performance. They find that ICT use is positively correlated to sustainable performance, and dynamic capabilities such as technological innovation mediate the documented relationship. Mikalef et al. (2020) employ survey data from Norwegian firms, and report that big data analytics capability enables firms to obtain competitive advantage. This positive impact is not direct but fully mediated by dynamic capabilities measured by both marketing and technological capability. Lin and Wu (2014) shed light on Taiwanese firms, and propose that dynamic capabilities mediate the positive relationship between valuable resources and corporate performance.

Hypothesis Development

Referring to RBV, the valuable, rare, and nonsubstitutable resources possessed by firms may lead to organizational competitive advantage. ICT resources can decrease costs used in business operating, inventory management, marketing, and advertising (Hamad et al., 2018; Krell & Matook, 2009). Yunis et al. (2018) argue that ICT is positively associates with firms’ long-term competitiveness because it can generate, add, incorporate and release key resources. Specifically, ICT can be seen as critical resources from the eyes of pharmaceutical firms. This is because medical information technology hardwares and softwares are not easily accessible in the marketplace (Hamad et al., 2015; Lai et al., 2006; N’Da et al., 2008). Extant studies confirm the positive effect of ICT use on productivity at the firm level (Bugamelli & Pagano, 2004; Greenan et al., 2001; Tran et al., 2014), and this relationship is stronger in the service sector (Stare et al., 2006). Consequently, the first hypothesis is developed as follows:

H1: The use of ICT positively influences competitive advantage.

DCV highlights the importance of developing and renewing resources. To be specific, dynamic capabilities help firms achieve alignment between self-competencies and evolving environmental conditions, wherein ICT is a corporate capability in creating innovative processes and products (Hitt et al., 2016). ICT enables firms to enhance knowledge management in rapidly changing business environments that triggers innovation (Almatrooshi et al., 2016; Kleis et al., 2012). Firms can efficiently transfer new knowledge and facilitate information exchange, leading to a closer connection among customers, collaborative partners, and suppliers (Podrug et al., 2017; Zhang et al., 2023). ICT-adoption firms thus enjoy faster response to market changes and less market failures and have more innovative opportunities residing in the network of external stakeholders. Extant studies contend that ICT use can pave the way for innovative process, new product and services, and even new business models (Brynjolfsson & Saunders, 2010; Scherngell et al., 2020). Accordingly, the second hypothesis is formulated as follows:

H2: The use of ICT positively influences innovation.

RBV characterizes resources as the capabilities of a firm that depend on their uniqueness. They can be both tangible and intangible, which are utilized by firms as inputs. These inputs are subsequently integrated and transformed by capabilities to generate innovative form of competitive advantage (J. Barney, 1991). According to DCV, firms with higher capabilities of innovation are more flexible and adaptive to the changing environment, and more easily exploit new marketing opportunities (Dymitrowski & Mielcarek, 2021; Jiménez-Jiménez & Sanz-Valle, 2011). Innovation enables firms to obtain valuable and non-replicable resources, and thus positively associates with value generation (Anning-Dorson, 2018; McGrath et al., 1996). A bulk of studies have found that innovation is a strategic tool for service organizations to retain and acquire value (Darroch & McNaughton, 2002; Grawe et al., 2009). Amit and Schoemaker (1993) manifest that innovation alleviates uncertainties, where innovative firms can better predict future of the market. Therefore, the third hypothesis is constructed as follows:

H3: Innovation positively influences competitive advantage.

DCV suggest that dynamic capability act as mediators that convert resources into enhanced performance (L. Y. Wu, 2007). Leveraging ICT enhances a firm’s dynamic capabilities, enabling it to engage in organizational learning through interactions with suppliers, manufacturers, retailers, and other cooperative alliances (Brynjolfsson & Saunders, 2010). This learning process catalyzes knowledge transfer, facilitates problem definition, and foster solution creation (Brockman & Morgan, 2003), which in turn, enhances both innovation and performance. Innovation is a product of organizational learning and knowledge creation and it accelerates the impact of ICT on business performance (Al-Ansari et al., 2013). Firms that adopt ICT tools, such as cloud computing, big data, and cyber-physical system, may foster a competitive advantage by internalizing innovative knowledge and leveraging learning from both external and internal resources (Sheng et al., 2013). In sum, innovation mediates the relationship between ICT use and competitive advantage because it is the direct outcome of organizational learning, which is facilitated by ICT. Based on this, the fourth hypothesis is organized as follows:

H4: Innovation mediates the relationship between ICT use and competitive advantage.

Method

Respondents

The survey respondents in this study were drawn from two distinct groups within the pharmaceutical industry: pharmaceutical firms and CROs. The rationale for choosing these two groups lie in their differing roles in innovation focus and ICT use. Pharmaceutical firms are central to driving innovation within the industry, as they are directly involved in R&D activities such as drug discovery and product development (Rogers et al., 2002). They also rely heavily on ICT to manage complex research processes and enhance market access. In contrast, CROs offer outsourced services to pharmaceutical firms, focusing primarily on areas such as clinical research and trials, and regulatory affairs. While not directly involved in the R&D, CROs play a critical role in supporting pharmaceutical firms by providing specialized expertise (Hassanzadeh et al., 2014; Lowman et al., 2012). The use of ICT in CROs is more focused on improving operational efficiency, reducing cost reduction, and enhancing the quality of clinical trial data, rather than being centered on product development or commercialization. By surveying respondents from these two types of organizations, we can explore the differential impacts of ICT use and innovation on competitive advantage within the pharmaceutical industry.

Our respondents include practitioners from both pharmaceutical firms and CROs, all of whom are actively engaged in innovation and ICT-related activities. Within pharmaceutical firms, the practitioners represent a wide range of roles, including those directly involved in R&D activities such as drug discovery, clinical trials, and product development. Additionally, the sample includes practitioners working in technology support functions, such as statisticians, ICT support specialists, and digital marketing experts. Executive support roles, such as management, accountants, and regulatory specialists, are also included. For respondents from CROs, the practitioners are primarily involved in clinical research, trials, and regulatory affairs. Although CROs do not directly involve in the R&D, their contribution to clinical trials and data management are essential in supporting the innovation processes of pharmaceutical firms.

Data Collection

A pilot draft questionnaire was initially emailed to a total of 16 experts including 3 academics from higher education institutions, 6 managers of CROs, and 7 executives of pharmaceutical firms. Given that the questionnaire was preliminary designed in English and then translated into Chinese, six experts with bilingual background were asked to check the consistency of language expression and evaluate whether all texts were clearly interpreted. The content validity of the scale was also pretested and assessed by experts. After improving some measurement scales, the final version of the questionnaire was generated in August 2022.

The survey questionnaire was physically constructed using an online survey tool, namely Sojump, one of the most adapted electronic survey platforms in China. Offering user-friendly survey tools such as questionnaire formulation, data collection, and statistical analysis, Sojump is characterized by simplicity, accessibility, and inexpensiveness (Zhang et al., 2017). We contacted coordinators of 15 CROs and 17 pharmaceutical firms, requesting them to distribute the link of questionnaire to corresponding personnels. The link was established through WeChat, the most frequently used social communication tool in China (Liu et al., 2018). Respondents can answer the questionnaire from smart phone since September 2022.

A total of 914 questionnaires were distributed, of which 708 were returned and 25 questionnaires were unusable because of repeated IP address and same answer for consecutive questions. Finally, 683 questionnaires were valid, yielding a response rate of 75%. Followed by Fowler (1988), the non-response bias test was employed to compare the early and late respondent on the mean value of each scale and demographics characteristics. The results depict that no significant difference exists between the two groups, indicating that the likelihood of non-response bias is minimal. The percentage of respondents from CROs and pharmaceutical firms is 55.64% and 44.36%, respectively. Respondents are primarily male (70.1%), and aged over 35 (64.1%). All respondents hold at least a bachelor’s degree (48.4% for bachelor’s degree, 41.7% for master’s degree, and 9.9% for Ph.D. degree). The majority of respondents work in R&D position (60.0%) followed by technology-support (31.2%) and executive-support (8.8%). The average years of experiences are 9.1 years. The sample consists of 50% of low and middle managers, 26.3% of top managers, and 23.7% of non-managerial workers. Following Forrest (1992) and Bagchi-Sen et al. (2004), we categorize firms size based on the number of employees. Small-sized firms have less than 50 employees. Medium-sized firms have 51 to 135 employees. Beyond 135 employees are large-sized firms. About 66.3% of respondents work in medium and large firms. The demographics characteristics of respondents is shown in Table 1.

Table 1.

Demographics Characteristics of Respondents.

Variables	% of respondents
Place of work
CRO	55.64
Pharmaceutical firm	44.36
Gender
Male	70.1
Female	29.9
Age
18–25	6.3
26–30	16.5
31–35	13.1
36–40	32.0
Over 40	32.1
Education
Bachelor degree	48.4
Master degree	41.7
Ph.D. degree	9.9
Task of work
R&D activities (e.g., drug discovery, clinical trials, product development, etc.)	60.0
Technology support (e.g., statistician, ICT support, digital marketing, etc.)	31.2
Executive support (e.g., management, accountant, regulatory specialist, etc.)	8.8
Position of work
Non-managerial work	23.7
Low and middle management (e.g., supervisors, department heads, area managers, plant managers)	50.0
Top management (e.g., presidents, vice-presidents, board of directors, CEO, CFO, CMO, etc.)	26.3
Working experience (years)
0–3	12.4
4–6	23.4
7–10	34.0
11–15	24.9
Over 15	5.3
Firm size
Small (1–50 employees)	33.7
Medium (51–135 employees)	42.3
Large (136 employees or more)	24.0

Measures

The survey questionnaire is structured in two sections. The first section gathers background information about the respondents. The second section evaluates respondents’ perceptions, capturing their insights and understanding of ICT use innovation and competitive advantage within the pharmaceutical industry. These perceptions were assessed through a set of survey items, with each construct comprising multiple items. All items were measured on a five-point Likert scale, which ranges from 1 (indicates strongly agree) to 5 (indicates strongly disagree).

Innovation is measured by a six-item scale validated by Calantone et al. (2002), Palacios-Marqués et al. (2016), Sok and O’Cass (2011), and Lin and Wu (2014). Likewise, we refined some items in order to better capture innovation characteristics in pharmaceutical sector (Hassanzadeh et al., 2014). The measurement of innovation consists of three categories, including arising new ideas and knowledge by continuous learning and adaption, developing new clinical product and services by learning from industry knowledge and insights, and fostering creative business models by learning from market dynamics and adapting organizational processes.

The scale of competitive advantage consists of eight items developed by Chen et al. (2006) and Gürlek and Tuna (2018). The measurement comprises the extent to which competitive advantage links with cost-efficiency, quality of clinical products and services, R&D capacity, managerial capability, profitability, sustainable growth, market position, and the image of the organization. In summary, the measurement framework is shown in Table 2. The details of measurement scale are depicted in Appendix Table A1.

Table 2.

The Framework of Measurement.

Theoretical construct	Category of measurement	Number of items used in the questionnaire
ICT use	Supports R&D	1
	Supports medical information processing	2
	Supports collaboration and communication	3
Innovation	Arises new ideas and knowledge by continuous learning and adaption	2
	Develops new clinical products and services by learning from industrial knowledge and insights	2
	Fosters creative business models by learning from market dynamics and adapting organizational processes	2
Competitive advantage	Links with cost efficiency	1
	Links with quality of clinical products and services	1
	Links with R&D capacity	1
	Links with managerial capability	1
	Links with profitability	1
	Links with sustainable growth	1
	Links with market position	1
	Links with organization image	1

Statistical Analyses

All analyses of this study were proceeded using SPSS version 23 and AMOS Graphics version 23. Descriptive statistics and Pearson’s correlation coefficient were initially examined. Following the modeling procedure outlined by Anderson and Gerbing (1988), we employed a two-step approach to evaluate used scales and test proposed hypotheses. The first step relates to the measurement model, in which the confirmatory factor analysis (CFA) is utilized to assess the reliability and validity of latent constructs. The second step is the structural model which assesses associated paths representing the hypotheses.

In terms of the structural model, the mediation effect of innovation on the “ICT use -competitive advantage” nexus was examined. More precisely, we firstly examined the significance of direct path coefficient through including both independent variable and dependent variable while excluding mediator variable. Second, we included mediator variable and examine the significance of indirect path coefficient from independent variable to dependent variable. Because indirect effects always do not follow normal distribution, we specified a 2,000 bootstrap sample (Efron, 1992; Fritz & MacKinnon, 2007).

To compare structural relationships of respondents between CROs and pharmaceutical companies, a multigroup analysis was utilized. The first step is to examine whether the variances across different groups attribute to the measurement differences. As such, a chi-square difference test for a set of four models suggested by Byrne (2006) was conducted. Once the group-invariance is found, separate structural models for CROs’ respondents and pharmaceutical firms’ respondents were implemented, respectively.

Results

Measurement Model

Table 3 demonstrates descriptive statistics, Cronbach’s Alpha coefficients, and correlations for variables. The mean values of ICT use, innovation, and competitive advantage fall between 2.195 and 3.016, indicating that respondents generally perceive analyzed variables as relatively important. The values of all Cronbach’s Alpha coefficient are between .771 and .864, far exceeding the commonly used threshold of .60 (Sekaran & Bougie, 2003). In addition, all scales are correlated in the expect direction.

Table 3.

Descriptive Statistics, Cronbach’s Alpha Coefficient, and Correlation for Variables.

Variable	Mean	SD	Cronbach’s Alpha coefficient	1	2	3
1. ICT use	2.417	0.717	.864	1
2. Innovation	2.195	0.861	.819	0.362***	1
3. Competitive advantage	3.016	0.537	.771	0.448*	0.393***	1

***

p < .01, *p < .1.

Following Anderson and Gerbing (1988), we initially utilized the confirmatory factor analysis (CFA) to assess the extent to which used items can reliably measure underlying latent constructs and reduce measurement errors. The convergent and discriminant validity were thus examined. As shown in Figure 1, the factor loadings for all items are above the commonly used threshold of 0.7, indicating convergent validity for all three constructs (Hulland, 1999). The composite reliabilities of all constructs are greater than 0.8, indicating that used items have sufficient internal consistency and therefore are robust. The average variance extracted (AVE) for all constructs shown in Table 4 exceed the threshold of 0.5 (Bagozzi & Yi, 1988), meaning that the construct can explain more than half of items’ variance. The results of discriminant validity are also demonstrated in Table 4, of which the square roots of AVE exceed inter-construct correlations between all construct pairings (Fornell & Larcker, 1981).

Figure 1.

The results of the structural model.

Table 4.

Results of CFA and Discriminant Validity.

Variable	Composite reliability	Average variance extracted (AVE)	Discriminant validity
	Composite reliability	Average variance extracted (AVE)	ICT	Innovation	Competitive advantage
ICT use	0.852	0.615	0.784
Innovation	0.913	0.633	0.379	0.796
Competitive advantage	0.827	0.608	0.495	0.264	0.780

Structural Model

To examine the interrelated causal associations among the constructs and obtain the path coefficients, the hypothesized structural model was examined. As shown in Figure 1, the primary model is related to direct effect of independent variable on dependent variable without including mediator variable. The fit indices for this model are acceptable with χ²/df = 2.373, CFI = 0.947, TLI = 0.960, RMSEA = 0.025 (Hair et al., 2009). The path from ICT use to competitive advantage is statistically significant at the 10% significance level with a positive standardized coefficient at .258. This result indicates that ICT use positively impacts competitive advantage of pharmaceutical industry, thus supporting H1.

When adding mediator variable into the structural model, the indirect path coefficient was further estimated. Similarly, all indices are adequate to a satisfactory fitness level with χ²/df = 2.519, CFI = 0.951, TLI = 0.958, RMSEA = 0.024. The path coefficient from ICT use to innovation is .379 at the 1% significant level, revealing that ICT use has a positive effect on innovation, and thus H2 is supported. The path from innovation to competitive advantage is statistically significant at the 1% significance level. The path coefficient is .281, suggesting that innovation positively influences competitive advantage, as proposed in H3. Finally, we included innovation into the model and examined the mediating effect on the “ICT use—competitive advantage” nexus. The result indicates that ICT use still has a significant and positive effect on competitive advantage (path coefficient = .304, p < .01). Therefore, innovation exerts a partial mediating effect, as hypothesized in H4.

Multigroup Analysis

To further assess differences of structural relationships between CROs’ respondents and pharmaceutical firms’ respondents, we conducted a multigroup analysis consisting of two steps. First, measurement invariance was examined to test whether measurement parameters perform in the same way for both groups. Following Byrne (2006), we utilized four models to examine the group-invariance. The first one is the baseline model, of which the result of chi-square (χ²) value is 124.57 (p = .23). The goodness-of-fit of the model is NFI = 0.923, CFI = 0.962, RMSEA = 0.027, revealing that factor structures remain invariant across groups. The second model constrains the factor loadings equal, which yields a result of Δχ² = 5.13 (p = .64). No significant difference in chi-square value is found between this model and the baseline model, indicating that the factor loadings are still invariant across groups. The third model adds error variances equal, yielding a result of Δχ² = 21.08 (p = .29). It implies that no chi-square difference in the used models, and therefore error variances are still unchanged across groups. In the last model we added factor variance and covariance equal. The result is Δχ² = 51.59 (p < .05), indicating a significant increase in chi-square value. Therefore, either factor variance, factor covariance, or both are not invariant across groups. It leads us to conclude that measurement invariance exists while structural relationships vary across two groups of respondents.

Furthermore, the structural model for each group was examined. As shown in Table 5, ICT use has a significant direct positive effect on competitive advantage for pharmaceutical firms’ respondents, but not for CROs’ respondents. Regarding the indirect effect, the path from ICT use to innovation is statistically significant in both groups, while the path from innovation to competitive advantage is significant only for CROs’ respondents. Concerning the mediating effect of innovation, ICT use has a significant indirect positive effect on competitive advantage for both groups, with the magnitude, and significance of path coefficient being stronger for CROs’ respondents.

Table 5.

Results of Path Coefficients for CROs’ Respondents and Pharmaceutical Firms’ Respondents.

Multi-group analysis	CROs’ respondents	Pharmaceutical firms’ respondents
Direct effect
ICT use → competitive advantage	0.219	0.354**
Indirect effect
ICT use → innovation	0.487***	0.205*
Innovation → competitive advantage	0.526***	0.297
ICT use → competitive advantage	0.423***	0.318**

***

p < .01, **p < .05, *p < .1.

Based on these findings, we conclude that a full-mediation effect of innovation is observed for CRO’s respondents. This is demonstrated by the lack of a significant direct effect ICT use on competitive advantage, as well as a significant and stronger indirect effect through innovation. In other words, innovation fully mediates the relationship between ICT use and competitive advantage for CROs. In contrast, a partial-mediation effect of innovation is found for pharmaceutical firms’ respondents, where the direct effect of ICT on competitive advantage remains significant, and innovation mediates this relationship to a lesser extent. Therefore, for pharmaceutical firms, innovation partially mediates the relationship between ICT use and competitive advantage.

Discussion and Conclusions

Using RBV as theoretical underpinning, the current study finds that ICT positively affects competitive advantage. We highlight the importance of ICT as a valuable resource that helps organizations achieve both greater level of innovation and superior performance (Almatrooshi et al., 2016; Kleis et al., 2012; Yunis et al., 2018). By assessing practitioners’ perceptions, we contend that the use of ICT can bring about competitive advantage for pharmaceutical organizations including CROs and pharmaceutical companies, thus complementing extant empirical studies on ICT from the productivity perspective (Bugamelli & Pagano, 2004; Greenan et al., 2001; Tran et al., 2014).

Second, our research documents that the impact of ICT use on competitive advantage is significantly strengthened through the mediating effect of innovation. Within the framework of DCV, innovation plays a critical role in translating ICT resources into competitive advantage in the pharmaceutical industry. Specifically, ICT facilitates information sharing and knowledge network among key stakeholders, such as manufacturers, healthcare providers, patients, and research institutions, thereby enhancing the firm’s learning capabilities. This, in turn, enables the creation of innovative solutions (Lin & Wu, 2014; Tidd & Bessant, 2005). Our three-category measures of innovation, including new ideas and knowledge, new clinical products, and services and creative business models, reflecting the multidimensional nature of innovation. When firms absorb new knowledge from ICT, internalize it, and then apply it creatively to develop new products and services, ICT resources help pharmaceutical organizations identify and leverage key capabilities that contribute to sustainable competitive advantage (Teece et al., 1997; L. Y. Wu, 2007). By contending the mediating role of innovation, our study offers new insight to the “ICT use—competitive advantage” nexus. It also provides a more nuanced understanding of how pharmaceutical firms can leverage dynamic capabilities to achieve competitive advantage, thereby complementing extant studies (Hamad et al., 2018; Krell & Matook, 2009).

Third, we argue that the hypothesized relationships differ between CROs and pharmaceutical firms’ respondents. Specifically, a full-mediation effect of innovation is observed for CROs’ respondents, as the direct effect of ICT use on competitive advantage is not significant, while the indirect effect mediated through innovation is significant. Conversely, a partial-mediation effect is found for pharmaceutical firms, where the direct effect of ICT on competitive advantage is significant, but the mediating effect of innovation is less pronounced. One possible explanation for these differences is rooted in the nature of CROs as knowledge-intensive business services (KIBS). KIBS organizations typically address client needs through complex problem-solving process. They may acquire knowledge from clients or other related parties, and transform knowledge into business solutions that ultimately boost innovation (Rodriguez et al., 2017). This focus on knowledge transformation may make innovation, particularly ICT-driven innovation, a central driver of their competitive advantage. As such, CROs may place greater emphasis on innovation, which explains the stronger mediating effect of innovation observed in this group compared to pharmaceutical firms.

Our research provides both theoretical and practical implications. From academic perspective, we empirically confirm the positive role of innovation in the “ICT use—competitive advantage” linkage, thus providing new insights into the service innovation and ICT management literature. Our study also makes contributions by shedding light on the pharmaceutical industry and CROs which are less examined in extant studies. With respect to managerial implications, the full-mediation effect of innovation in CROs’ respondents indicates that CROs have higher concerns with innovative transformation of ICT resources. Thus, we advise CROs to strategically move from ICT adoption to ICT-based innovation. With respect to pharmaceutical firms, innovation has no effect on competitive advantage, implying that managers widely underestimate the importance of innovation. They are advised to invest more resources in innovation, and further collaborate with CROs in terms of drug discovery, preclinical work, and clinical trials. As for policymakers, initiatives and guidelines in encouraging ICT-based innovation are recommended to formulate.

We acknowledge some limitations of this research. First, we assessed proposed variables using questionnaire items, which may lead to leniency errors and overestimation of model relationships. Future research can consider quantitative indicators, and develop a more comprehensive measurement framework. For instance, financial performance indicators may better measure competitive advantage. Dimensions of balanced scorecard (Asiaei & Bontis, 2019) and intellectual capital (Yu et al., 2017) can also be included in the measurement. Second, our study does not consider all stakeholders of pharmaceutical industry. Future study can extend to surveying hospital doctors, online medical enterprises, patients, and academic institutions. Future research can also compare this issue across different geographic contexts.

Footnotes

Appendix

Table A1.

The Details of Measurement Scale.

Theoretical construct	Items used in the questionnaire
ICT use	The pharmaceutical organization needs to provide ICT support for clinical development process
	The pharmaceutical organization needs to provide ICT support for collaborative works regardless of time and place
	The pharmaceutical organization needs to provide ICT support for communication among organization members
	The pharmaceutical organization needs to provide ICT support for searching for and assessing necessary medical information
	The pharmaceutical organization needs to provide ICT support for simulation and prediction
	The pharmaceutical organization needs to provide ICT support for systematic storing
Innovation	The pharmaceutical organization frequently tries novel ideas and knowledge, learning from both success and failures
	The pharmaceutical organization seeks out new ways to develop clinical products and services, learning from industry trend and past experiences
	The pharmaceutical organization is creative in its methods of operating, learning from internal processes and adapting to new insights
	The pharmaceutical organization is often the first to the market with new clinical products and services, leveraging learned insighted from market and R&D efforts
	Innovation in the pharmaceutical organization is perceived as too risky and is resisted, reflecting challenges in learning and adapting to new ideas
	The introduction of new clinical products and services in the pharmaceutical organization has increased over last 5 years, reflecting a continuous learning process and enhanced innovation capabilities
Competitive advantage	The pharmaceutical organization has competitive advantage of low cost compared to competitors
	The quality of clinical products or services that the pharmaceutical organization offers is better than that of competitor’s clinical products or service
	The pharmaceutical organization is more capable of R&D and innovation than competitors
	The pharmaceutical organization has better managerial capability than competitors
	The pharmaceutical organization’s profitability is better
	The growth of the pharmaceutical organization exceeds that of competitors
	The pharmaceutical organization is the first mover in some important fields and occupies an important position
	The organization image of pharmaceutical organization is better than that of competitors

Acknowledgements

The authors intend to thank Shirley Zhao, Wang Zhang, Michael Zhang, Vivian Li, Yangman Ou, Vicky Wang, Emily Dong, Qiaoyi Luo, Zhenbin Chen, Sandy Xue, Yifan Fang, Ziman Zhang, and anonymous HR officers of CROs and pharmaceuticals firms for their helps in distributing questionnaire to respondents. The authors also wish to thank all those who contribute their time to participate in the questionnaire

ORCID iD

Minghui Yang

Petra Maresova

Ethical Considerations

Given the minimal risk associated with questionnaire-based research, formal ethical approval was not required for this study.

Consent to Participate

Informed consent was obtained from all participants prior to their participation in the online questionnaire. Participation was voluntary, and all respondents were informed about the study’s purpose, their right to withdraw at any time without consequence, and the confidentiality of their responses. Participants gave their consent by voluntarily completing the questionnaire. All data were anonymized to ensure participant privacy and confidentiality.

Funding

The author(s) disclosed receipt of the following financial support for or publication of this a/or publication of this article: This work was supported by the Internal Research Project Excellence 2023 at the Faculty of Informatics, and Management, University of Hradec Kralove, Czech Republic and the the Major Project of Department of Education of Guangdong Province (Grant number 2022ZDZX059).

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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