The Role of Digital Transformation and Digital Competencies in Organizational Sustainability: A Study of SMEs in Lima, Peru

Abstract

Digitalization has significantly transformed businesses in the 21st century, but there are gaps in understanding how it affects human resource management and organizational culture in SMEs in Lima, Peru. This study aims to fill this gap by analyzing the influence of digital transformation, digital competencies, and digital HR management on the organizational culture of SMEs in Lima and how these factors contribute to organizational sustainability. Using a quantitative approach and structural equation modeling (SEM), 307 business leaders were surveyed between January and March 2024. The results show that digital transformation and digital competencies significantly influence digital HR management, which positively impacts organizational culture and promotes sustainable practices. Additionally, it was found that digital HR management mediates the relationship between digital transformation and organizational culture, as well as between digital competencies and organizational culture. It is crucial to invest in digital technologies and foster digital competencies to improve HR management and promote a sustainable organizational culture. In conclusion, digitalization redefines organizational culture and reinforces sustainability, especially in SMEs in Lima, Peru, providing new scientific value by demonstrating these mechanisms of change.

1. Introduction

Digitization, understood as the process of converting analog information and activities into digital formats, has revolutionized how businesses operate and organize in the 21st century [1,2,3]. This transition has become a necessity to compete in a globalized market [4]. However, its adoption is not merely the implementation of new technologies; at its core, it is a cultural transformation that modifies how organizations think, work, and relate [5,6]. In this transition, small and medium-sized enterprises (SMEs), due to their flexibility and adaptability, have an opportunity to leverage these changes to improve their market position [7]. This research focuses on SMEs in Lima, Peru, a population that, given its geographic location and socio-economic characteristics, presents a unique landscape for studying digitization and its influence on organizational culture.

This study provides a novel perspective on the influence of digital transformation (DT) and digital competencies (DC) on digital human resource management (DHRM) and organizational culture (OC) in SMEs in Lima, Peru. The originality of this study lies in identifying the mediating role of DHRM between DT and OC. Additionally, it highlights how DC not only enhances DHRM but also promotes a sustainable and adaptable OC.

The phenomenon of DT is a reality that has permeated all areas and business sectors [8]. However, there is a gap in understanding how this transformation, alongside DC [9,10] and DHRM [11], influences OC in SMEs. It is precisely this knowledge gap that this study aims to address. SMEs, which constitute a substantial part of the Peruvian economy, face distinct challenges and opportunities compared to large corporations in their digitalization process [12]. Understanding how these changes impact their OC is essential for implementing more effective strategies and ensuring the survival, growth, and expansion of these companies in the new digital landscape [13]. The general question of this study is as follows: how do DT and DC influence DHRM and OC in SMEs in Lima, Peru? To break down this overarching inquiry, specific questions are posed:

RQ1.

How does Digital Transformation impact Digital Human Resource Management in SMEs in Lima, Peru?

RQ2.

How do Digital Competencies affect Digital Human Resource Management in SMEs in Lima, Peru?

RQ3.

In what ways does Digital Human Resource Management influence Organizational Culture in SMEs in Lima, Peru?

RQ4.

Does Digital Transformation affect Organizational Culture through Digital Human Resource Management in SMEs in Lima, Peru?

RQ5.

Do Digital Competencies influence Organizational Culture through Digital Human Resource Management in SMEs in Lima, Peru?

The main objective of this study is to analyze the influence of digitalization on the OC of SMEs in Lima. To achieve this, the study seeks to establish how DT, DC, and DHRM affect the OC of these companies. It is important to note that the study population is composed exclusively of SME business leaders. The hypotheses guiding this work suggest a significant influence of digitalization on OC in SMEs in Lima. Specifically, it is proposed that DT, DC, and DHRM have a significant influence on OC [6,11,13]. Confirming this relationship will not only provide a deep understanding of the challenges and opportunities facing SMEs in their digitalization process but also serve as a foundation for developing recommendations and strategies that business leaders can implement to support DT in the sector.

In brief, digitization is not just a technological trend but a profound shift in how organizations operate and define themselves. In the context of Lima, Peru, this research aims to shed light on the relationships between digitization and OC in SMEs, providing valuable insights for academics and entrepreneurs.

This study will be organized into several sections. First, the theoretical framework will be developed, analyzing key concepts related to DT, DC, and DHRM. This section will lay the theoretical foundation for understanding the interactions between these variables and OC. Next, the methodology employed will be described, detailing the methodological approach, the population and sample, as well as the data collection and analysis techniques used. Subsequently, results obtained through statistical analysis will be presented, allowing for the answering of research questions and the testing of hypotheses. The Section 5.6 will focus on interpreting the results and their relationship with existing literature. The implications of the findings will be analyzed, exploring potential reasons behind identified patterns. Additionally, study limitations will be addressed, and recommendations for future research will be provided.

Lastly, conclusions will be drawn summarizing the main findings and their relevance in the context of SMEs in Lima, Peru. The practical implications of this study for Lima’s businesses will be highlighted, along with specific recommendations to strengthen OC in a digital environment. The document will conclude with a bibliography supporting all research efforts.

2. Theoretical Framework

2.1. Digital Transformation (DT)

DT is a widely discussed concept in the academic world, with various authors offering their definitions and approaches to understanding this phenomenon. It is not a new concept, as the first studies related to digitization date back to 1968. Authors such as Bonačić et al. and Souček et al. initially explored the application of digital technologies, laying the groundwork for understanding the potential of digitization in various areas [14,15].

According to Lu et al. [16], DT is defined as the adoption of digital technologies to be implemented in business operations. This definition underscores the importance of investing in digital technologies, which becomes a cornerstone for organizations to achieve effective online presence. Its goal is to enhance transparency, accountability, and efficiency in data and information management, which can be monitored and adjusted through Measurement and Evaluation of Results.

Cui et al. [17] expand the definition by highlighting the components of DT. This includes the adoption of advanced technologies such as Big Data, Artificial Intelligence, Mobile Internet, Cloud Computing, Internet of Things, and Blockchain in organizational business operations. These components are crucial within a Strategic Digital Transformation Plan that guides companies through their change process. Bhatt and Bae [18] contribute a unique perspective by noting that DT involves collaboration between humans and artificial intelligence algorithms to enhance both efficiency and decision-making. Within this context, Digital Marketing plays a role in this collaboration, as marketing strategies now rely on artificial intelligence and data analytics.

Salazar et al. [19] emphasizes that DT involves reengineering businesses. This reengineering refers not only to the digitization of technical tools but also to the design of strategies and tactics that enhance organizations’ Online Presence. L. Wang [20] underscores the strategic use of digital technology. Here, it can be argued that the Strategic Digital Transformation Plan and Investment in Digital Technologies converge to ensure organizations use technology to generate real and tangible value.

In summary, DT is a multidimensional concept that encompasses the adoption of advanced digital technologies, collaboration between humans and algorithms, business reconfiguration, and the incorporation of technology to enhance organizational economics efficiently. These perspectives provide a solid theoretical framework for understanding DT comprehensively, considering dimensions such as Strategic Planning, Technology Investment, Digital Marketing, Online Presence, and Measurement and Evaluation.

2.2. Digital Competencies (DC)

DC, in their various dimensions, play a crucial role in the contemporary world, where digital evolution and awareness are central aspects. Kaigo [21] describes these competencies as the skills and knowledge that enable participation in the digital environment, involving effective access and use of information online. This relates to the dimension of “Adoption and use of digital tools”.

van Laar et al. [10] expand on this concept by focusing on skills beyond the mere use of information and communication technologies (ICT). This includes the ability to communicate, collaborate, be creative, think critically, and solve problems, connecting these competencies with 21st-century skills. This perspective is related to the dimension of “Online presence and use of digital tools for management and communication”. Ferreira et al. [22] emphasize the applicability of these competencies in the workplace, highlighting the urgency of managing digital tools, aligning with the dimension of “Adoption and use of digital tools”. They also stress adaptability to the demands of the labor market, which is also linked to digital evolution.

Shakina et al. [23] address DC from a corporate perspective, emphasizing the importance of skills for innovation in a digital business environment. This is connected to the dimensions of “Adoption and use of digital tools” and “Online presence and use of digital tools for management and communication”. Ren and her team [24] add an educational and cultural dimension by pointing out that DC are influenced by family culture and parental mediation, especially in the context of media education and digital inequalities among adolescents. This connects DC with the dimension of “Digital evolution or Digital awareness”.

Hwang et al. [25] highlight the importance of DC in businesses, including skills such as interaction and effective use of online platforms. These skills are related to the dimension of “Online presence and use of digital tools for management and communication”. Finally, Yang and his team [26] address DC from an educational perspective, identifying the skills and capabilities that teachers need to integrate ICT into their pedagogical practices. This includes the management of digital tools and information literacy, which relates to the dimension of “Online presence and use of digital tools for management and communication”. In summary, DC encompass various dimensions, including digital evolution or digital awareness, the adoption and use of digital tools, online presence and the use of digital tools for management and communication, and digital security, as highlighted by contributions from several authors in this context.

2.3. Digital Human Resource Management (DHRM)

Human Resource Management has always been a fundamental element in organizations, with its study origins attributed to Flunder in 1970 [27] (“Human Resource Management”) and later enriched by Delery and Doty in 1996 with their discussion on theorizing in this field [28]. However, the digital age has introduced new dimensions in human resource management, giving rise to the term “Digital Human Resource Management”. Vahdat [29] points out that Digital Management involves the use of information technology to address human resource management challenges. In this sense, Alhamad et al. [30] also mention that “Digital Human Resource Management” (E-HRM) entails the use of electronic systems in HR administration, aiming for efficiency in HR practices. These two definitions are related to the HR Technology dimension.

Vrontis et al. [31] suggest that this management refers to the application of automation technologies in employee administration with the aim of enhancing organizational performance. This approach aims to overcome technological and ethical challenges associated with automation. This definition relates to the Process Automation dimension. Following Alhan’s vision [32], the integration of digital technology into HR management activities reaffirms communication and collaboration within the HR department. This integration aims to improve efficiency, thereby contributing to organizational success. This vision relates to the Digital Communication and Collaboration dimension.

Garg et al. [33] indicate that “Digital Human Resource Management” involves integrating machine learning (ML) technologies into HRM functions to enhance efficiency in areas such as hiring and performance management. Through these technologies, organizations can gain valuable insights into their employees and make more informed decisions. This definition relates to the Data Analytics and Decision-Making dimension.

DHRM encompasses various dimensions, including HR Technology (HR Tech), Process Automation, Digital Training and Development, Digital Communication and Collaboration, and Data Analytics and Decision-Making. These dimensions intertwine to form an approach to human resource management in the digital age, where information technology is used to address challenges in employee administration, improve organizational performance, foster creativity and innovation, strengthen internal communication, and enable decision-making. Collaboration between HRM experts and technology is essential to harnessing the opportunities offered by this field.

2.4. Organizational Culture (OC)

OC has been defined as the set of shared values, beliefs, attitudes, and behaviors that characterize an organization. However, with the advent of digitalization, organizations have faced the urgency to adapt and evolve, leading to the conceptualization of what we now term “Digital Organizational Culture”. Preston and Allmand [34] were pioneers in addressing the relationship between OC and digital technology. They focused on how digitalization has transformed the way information professionals fulfill their roles within organizations. A key aspect highlighted here is the dimension of Adaptability and Change due to the reconfiguration of professionals’ roles in the digital environment.

Pangarso et al. [35] highlighted how an organization achieves the incorporation and adaptation of digital technology, focusing on empowered leadership. This encompasses the dimensions of Leadership and Decision-Making due to the leadership’s crucial role in adaptation, as well as Adaptability and Change due to the integration of digital technology. Wang et al. [36] focused on how an organization values digitalization during its innovation. The dimensions of Mission and Values Orientation, as well as Leadership and Decision-Making, are relevant here, given the assessment of digitalization and the leadership impact.

Pradana et al. [37] clarified the shared values and practices related to digitalization. Here, Mission and Values Orientation, as well as Work Climate and Interpersonal Relationships, stand out for shared values and practices. Laaksonen and Porttikivi [38] addressed OC from a digital communication perspective. This perspective highlights the dimension of Communication and Open Communication, observing how interaction is managed online.

Melanie Pfaff et al. [39] propose a more comprehensive perspective of OC in the digital age, where not only technologies are adopted but strategy and culture are also adapted. This view integrates several dimensions such as Adaptability and Change for technology adaptation, Mission and Values Orientation for adapting strategy and culture, and Ethics and Social Responsibility for promoting values. In conclusion, it is evident that OC in the digital age encompasses various dimensions that determine how organizations adapt to the digital environment. Each author contributes a unique perspective, focusing on different aspects of OC, thus highlighting distinct dimensions.

3. Hypothesis Development

DT is defined as the adoption of advanced digital technologies to optimize business operations [16,17]. This adoption includes tools such as Big Data, Artificial Intelligence, Cloud Computing, and other technological systems that facilitate automation and enhance efficiency in human resource management [29,30]. Theory suggests that implementing these technologies enables better talent management, process optimization, and more informed decision-making. Empirical studies have shown that the implementation of digital technologies in human resource management significantly improves operational efficiency and decision-making [18,33]. In the context of SMEs in Lima, the adoption of DT can provide technological solutions that facilitate human resource management, from hiring to performance evaluation, thereby enhancing organizational efficiency and effectiveness. Therefore, the following hypothesis is proposed:

Hypothesis 1 (H1).

DT has a positive impact on DHRM.

DC encompass the skills and knowledge necessary to effectively utilize digital technologies in the workplace [10]. These competencies span digital communication, online collaboration, creativity, and critical thinking, all essential for modern human resource management [22,23]. Theory suggests that employees with advanced DC can better adapt to technological tools, thereby enhancing human resource management and development. Empirical research has highlighted that DC are crucial for the adoption and effective use of technologies in human resource management [25,26]. In Lima’s SMEs, training in DC can lead to more efficient human resource management, given the highly competitive and technological environment. Therefore, the following hypothesis is proposed:

Hypothesis 2 (H2).

DC have a positive impact on DHRM.

DHRM involves the use of information technologies to enhance human resource administration, including process automation, data analytics, and digital training [30,31]. The theory posits that effective human resource management can positively influence OC, promoting values of innovation, adaptability, and collaboration. Empirical studies have shown that the implementation of E-HRM and digital training enhances OC by fostering greater adaptability and internal cohesion [32,33]. In the context of SMEs in Lima, efficient DHRM can promote an OC that values innovation and adaptability, which is crucial for addressing market challenges.

Hypothesis 3 (H3).

DHRM has a positive impact on OC.

The theory suggests that DT, by enhancing efficiency and effectiveness in human resource management, can positively impact OC [18]. Process reengineering and the integration of advanced technologies not only optimize talent management but also foster a culture of innovation and adaptability [20]. Empirical research has demonstrated that DT, mediated through effective human resource management, can reshape OC [17,31]. In Lima’s SMEs, the implementation of digital technologies can facilitate positive cultural changes, enhancing organizational adaptability and resilience. Therefore, the following hypothesis is proposed:

Hypothesis 4 (H4).

DT has a positive impact on OC through DHRM.

DC are essential for the effective adaptation and utilization of technologies in human resource management [10,23]. Theory suggests that developing these competencies not only enhances human resource management but also positively influences OC, promoting values of innovation and collaboration [22]. Empirical studies have shown that enhancing DC among workers improves human resource management and, in turn, has a positive impact on OC [25,26]. In the context of SMEs in Lima, fostering DC can be an effective strategy to enhance both DHRM and OC, facilitating greater adaptability and efficiency in a competitive environment. Therefore, the following hypothesis is proposed:

Hypothesis 5 (H5).

DC have a positive impact on OC through DHRM.

The conceptual model was built based on the proposed hypotheses and is presented in Figure 1 below.

The analysis of the conceptual model and the five proposed hypotheses significantly contributes to the methodological approach of this study. Each hypothesis is designed to examine a specific relationship between the key variables of the study: DT, DC, DHRM, and OC. The formulation of these hypotheses allows for a structured evaluation of how digitalization influences various aspects of management and organizational culture in SMEs. This methodological approach, based on structural equation modeling (SEM), facilitates the identification of causal and mediating relationships between the variables. Thus, the analysis of the hypotheses not only provides an understanding of the internal dynamics of SMEs in the context of digitalization but also supports the validity and reliability of the quantitative method employed.

4. Materials and Methods

4.1. Methodological Approach

This study focuses on quantitatively analyzing the interrelationship among DT, DC, DHRM, and OC in SMEs in Lima, Peru. The adopted methodological approach utilizes Structural Equation Modeling (SEM) to estimate how exogenous variables impact endogenous variables, aiming to explore causal relationships [40]. The study design is non-experimental and cross-sectional, following the methodology of Hernández and Mendoza [41], aimed at testing hypotheses through a survey conducted between January and March 2024.

The selection of statistical tests in this study was based on the nature of the data and the objectives of this research. For example, Bartlett’s test of sphericity was used to assess the suitability of the data for exploratory factor analysis (EFA). This test is appropriate when verifying whether the correlations between variables are significantly different from zero, which is a prerequisite for conducting an EFA. Additionally, confirmatory factor analysis (CFA) was employed to validate the structure of the proposed theoretical model. The choice of these tests is grounded in their ability to evaluate the validity and reliability of the constructs measured in the study. Furthermore, techniques such as structural equation modeling (SEM) were used to explore causal relationships between variables, due to their ability to handle multiple dependencies and provide a comprehensive view of the interrelationships in the conceptual model.

4.2. Population and Sample

Accurately identifying the exact number of active small and medium-sized enterprises (SMEs) in Metropolitan Lima posed a challenge for researchers, as the most recent information, supported by Peru’s National Institute of Statistics and Informatics (INEI), dates back to 2013 [42]. Faced with this limitation, a database was compiled consisting of recognized professionals who met the inclusion criteria, many of whom had professional relationships with the research team members.

The next phase involved on-site visits to various companies, where there was willing collaboration from decision-makers who facilitated new contacts. The target population of this study comprises business leaders operating in SMEs within Metropolitan Lima. A total of 307 business leaders were surveyed between January and March 2024. The selection of participants was based on non-probabilistic sampling, utilizing convenience and snowball sampling techniques. Although the academic degree of participants is mentioned, this information was collected solely for descriptive purposes and not as an inclusion criterion. It is important to emphasize that all participants are active business leaders, not students.

Initially, the plan was to collect 500 valid surveys, but due to logistical and time constraints, 307 valid surveys were ultimately obtained. Although the sample size was smaller than the initial target, it is considered adequate and sufficient to apply the structural equation modeling. According to Hair et al. [43], it is recommended to have at least 10 observations per variable included in statistical analysis to ensure robust and reliable results. Given that the study involves analyzing multiple variables (each corresponding to survey items), the 307 observations collected provide a solid foundation for exploring relationships between the variables of interest and deriving meaningful conclusions about the target population.

4.3. Instrument Design and Validity

For data collection, an instrument consisting of four scales was developed and organized using a five-point Likert scale. This survey was specifically designed to measure the latent variables proposed in the research model: Digital Transformation (DT), Digital Competencies (DC), Digital Human Resource Management (DHRM), and Organizational Culture (OC), totaling 21 items, also known as observed variables [43]. It is worth noting that this instrument was built upon a solid theoretical foundation, as outlined in this article. Its items were created to assess the Peruvian context (see

Table 1. Items of the Data Collection Instrument.

Category	Item	Reference
Digital Transformation (DT)	DT1. Does your company have a clear strategic plan for digital transformation that aligns with its long-term business goals?	[44]
	DT2. Does your company invest in digital technologies such as enterprise management software, data analytics tools, and e-commerce systems?	[44,45]
	DT3. Does your company use digital marketing tools to promote its products and services online?	[46]
	DT4. Does your company have an active online presence through an optimized website, social media profiles, and an SEO strategy?	[46]
	DT5. Does your company regularly measure and evaluate the outcomes of digital transformation implementation?	[45,46]
Digital Human Resource Management (DHRM)	DHRM1. How effective is the technology your company uses to manage human resources?	[47]
	DHRM2. How effective is the automation of processes related to digital human resource management in your company?	[47]
	DHRM3. How effective is the digital training and development provided by your company to enhance employees’ skills?	[47]
	DHRM4. How effective is the digital communication and collaboration within your company?	[47]
	DHRM5. How effective is the use of data for making strategic decisions related to digital human resource management?	[47]
Organizational Culture (OC)	OC1. Does your company consistently adhere to a mission and values that all employees know and share?	[48]
	OC2. Is internal communication in your company transparent, and do employees feel comfortable expressing their ideas as well as concerns?	[47,48]
	OC3. Does leadership in your organization set a strong example and encourage employee participation in important decision-making?	[47,48]
	OC4. Is the work environment in your company collaborative and friendly, where diversity and inclusion are valued?	[48]
	OC5. Does your company recognize and reward outstanding performance, and are reward systems fair and equitable?	[48]
	OC6. Does your company adapt quickly to market changes, promote flexibility, and encourage continuous innovation?	[47,48]
	OC7. Does your company operate ethically in all areas of business and promote social responsibility and sustainability?	[48]
Digital Competencies (DC)	DC1. At what level do you believe your company is in terms of digital evolution?	[45,46]
	DC2. At what level do you believe your company is in terms of the adoption and use of digital tools in its processes?	[46]
	DC3. Which level most accurately describes your company’s online presence using digital tools for management and communication?	[46]
	DC4. Which of these levels best describes your company’s digital security situation?	[44,46]

).

4.4. Validation of the Instrument

The instrument underwent several statistical techniques for validation before its final application. Initially, it was validated by five experts in the field who are knowledgeable about the study variables and have recognized academic backgrounds. Subsequently, a pilot test was conducted using the Google Docs website and the “Forms” tool (https://docs.google.com/forms/d/e/1FAIpQLSedi2CaAdt4SxMZolreDYaCWCKq4_ebGi0wQTMgD2CwT2xpsg/viewform accessed on 5 January 2024). Through this medium, the survey link was sent to 50 individuals, resulting in 48 valid responses.

It is worth noting that these participants belong to a different database than previously mentioned. Based on the data obtained, Cronbach’s Alpha was calculated to assess instrument consistency [49]. A global Cronbach’s Alpha of 0.941 was obtained, with item values ranging between 0.934 and 0.943, indicating very good internal consistency and reliability [50].

4.5. Factorial Analysis

Next, to further increase evidence of validity and reliability, factorial analyses were performed, both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), applied to data from 307 surveyed individuals comprising the sample. These statistical techniques provide robust tools to explore and confirm latent constructs [51]. EFA stands out as a solid method in research, used to identify factors explaining variance in a set of observed variables, as per Nunnally and Bernstein [52]. This technique not only aids in generating hypotheses but also assists researchers in detecting latent constructs requiring deeper study. Meanwhile, CFA has been used to explore the structural connections underlying different variables in theories or hypotheses [51].

4.6. Structural Model Evaluation

Preliminary data analysis was conducted by calculating basic descriptive statistics to understand data distribution. With these prepared data, SmartPLS4 version 4.1.0.3 software was used to perform Structural Equation Modeling (SEM) analysis [43,50]. At this stage, collinearity among predictors in the structural model was checked by calculating Variance Inflation Factor (VIF) values. VIF values less than 5 were considered indicative of no significant collinearity issues. Significance evaluation of paths between variables in the structural model was carried out using path coefficients, sample mean, standard deviation, t-statistics, and p-values. Relationships with p-values less than 0.05 were deemed statistically significant. To determine the proportion of variance explained by predictors in the model, R² and adjusted R² values were calculated for dependent variables.

Effect size analysis of each predictor on dependent variables was conducted using f² values, considering small, moderate, and large effects for f² values greater than 0.02, 0.15, and 0.35, respectively. Additionally, predictive relevance of the model was assessed using Q² predict values, where positive Q² predict values indicated adequate predictive relevance [53]. To ensure reliability and validity of the structural model, additional tests such as internal consistency analysis and convergent and discriminant validity were conducted. Finally, results from these analyses were interpreted to understand the relationships between DC, DHRM, DT, and OC in the context of SMEs in Lima, Peru.

4.7. Statement of Generative AI and AI-Assisted Technologies in the Writing Process

We have employed various programs and tools, currently including AI, to enhance our research, such as Microsoft Word 365 version 2407 for grammar and style suggestions, Microsoft Excel 365 version 2407 for data analysis, DeepL version 24.1.2.11804 for accurate translation, ChatGPT model GPT-4o for comparing translations, and Google Search version 127.0.6533.100 for efficient information retrieval. It is important to note that these resources do not replace our interpretation of the data or the extraction of scientific conclusions.

5. Results and Discussion

The demographic profile of the 307 business leaders is presented in

Table 2. Demographic profile.

Demographics		Frequency	Percentage
Gender	Male	212	69.06
	Female	95	30.94
Age	24–30	34	11.07
	31–40	69	22.48
	41–50	130	42.35
	51–60	53	17.26
	61–71	21	6.84
Academic degree	Bachelor	88	28.66
	Magister	101	32.9
	Graduate	58	18.9
	Technician	31	10.1
	Ph.D.	17	5.54
	Specialist	12	3.9

and can be analyzed across three categories: gender, age, and academic degree. Regarding gender, many participants are male, totaling 212 participants, representing 69.06% of the total, while female participants number 95, representing 30.94%. In terms of age, most participants fall within the 41–50 age range, with 130 individuals making up 42.35%. The second largest group is aged 31–40, comprising 69 participants, or 22.48%. They are followed by participants aged 51–60, totaling 53 individuals, or 17.26%. The least represented age ranges are 24–30, with 34 participants (11.07%), and 61–71, with 21 individuals (6.84%).

Regarding academic degrees, most participants hold a master’s degree, with 101 individuals representing 32.90%. Other significant academic degrees include Bachelor’s, with 88 participants (28.66%), and Graduate, with 58 individuals (18.90%). Smaller proportions are observed in technical degrees, with 31 participants (10.10%), Doctorate, with 17 individuals (5.54%), and Specialist, with 12 participants (3.90%). The study’s demographic profile indicates a predominance of males in the age range of 41 to 50 years old, predominantly holding a master’s degree.

5.1. Exploratory Factor Analysis (EFA)

In the analysis of assumptions checks conducted with jamovi version 2.5 in this study [54], Bartlett’s Test of Sphericity and the Kaiser–Meyer–Olkin (KMO) Measure of Sampling Adequacy can be interpreted (

Table 3. Bartlett’s Sphericity Test.

χ2	gl	p
5152	210	<0.001

).

Bartlett’s Sphericity Test evaluates whether the correlations between variables are significantly different from zero. A significant result (p < 0.001) indicates that the correlations between variables are suitable for conducting an Exploratory Factor Analysis (EFA). In this case, the Chi-square value is very high (5152) and the p-value is less than 0.001, suggesting a significant correlation between variables. This confirms the adequacy of the data for EFA (

Table 4. Sampling Suitability Measure (KMO).

	MSA
Global	0.924
DT1	0.916
DT2	0.889
DT3	0.881
DT4	0.884
DT5	0.940
DHRM1	0.934
DHRM2	0.923
DHRM3	0.930
DHRM4	0.966
DHRM5	0.959
OC1	0.941
OC2	0.843
OC3	0.900
OC4	0.893
OC5	0.940
OC6	0.946
OC7	0.896
DC1	0.932
DC2	0.909
DC3	0.936
DC4	0.969

MSA: Measure of Sampling Adequacy.

).

The Kaiser–Meyer–Olkin (KMO) measure assesses the suitability of data for factor analysis. A global KMO value above 0.8 is considered commendable, with values between 0.8 and 0.9 being very good and values exceeding 0.9 being excellent. In this instance, the overall KMO is 0.924, which is excellent. This indicates that the sampling is adequate and the data are suitable for factor analysis [55,56].

For each variable:

• All individual indicators (DT, DHRM, OC, DC) have MSA values above 0.8, which is excellent.
• The highest values are for DHRM4 (0.966) and DC4 (0.969), indicating a high suitability for analysis.
• The lowest value is for OC2 (0.843), which is still within an acceptable range.

Both tests indicate that the data are suitable for conducting Exploratory Factor Analysis. Bartlett’s Test of Sphericity shows significant correlation between the variables, while the overall KMO and individual values indicate that the data are appropriate and have high sampling adequacy. These results support the decision to proceed with EFA in this study.

Table 5. Factor Load.

	Factor				Uniqueness
	1	2	3	4
OC2	0.884				0.273
OC4	0.841				0.307
OC3	0.786				0.343
OC5	0.730				0.411
OC6	0.671				0.412
OC7	0.595				0.557
OC1	0.433				0.615
DC2		0.978			0.110
DC3		0.938			0.114
DC1		0.888			0.142
DC4		0.738			0.240
DHRM3			0.870		0.268
DHRM2			0.867		0.190
DHRM1			0.725		0.296
DHRM4			0.485		0.376
DHRM5			0.456		0.362
DT4				0.798	0.353
DT3				0.744	0.498
DT5				0.712	0.309
DT2				0.672	0.453
DT1				0.450	0.568

provides a detailed view of the factorial loadings of the variables in relation to four factors extracted using the principal axis factoring method and oblimin rotation [57].

Additionally, uniqueness values are presented for each variable, indicating the proportion of variance of each variable not attributed to the factors. Uniqueness values provide information on the specific variance of each variable not explained by the factors. Lower values indicate better explanation of variable variance by the factors. Factor loadings reflect the correlation between observed variables and latent factors, with higher loadings indicating a stronger association between the variables and corresponding factors.

The first factor, termed Organizational Culture (OC), is strongly linked to variables related to organizational culture, such as OC2 (0.884), OC4 (0.841), OC3 (0.786), OC5 (0.730), OC6 (0.671), OC7 (0.595), and OC1 (0.433). On the other hand, the second factor, Digital Competencies (DC), is clearly defined by variables like DC2 (0.978), DC3 (0.938), DC1 (0.888), and DC4 (0.738), all related to digital competencies. The third factor, Digital Human Resource Management (DHRM), shows a notable association with variables such as DHRM3 (0.870), DHRM2 (0.867), DHRM1 (0.725), DHRM4 (0.485), and DHRM5 (0.456), indicating its relationship with digital management of human resources. Finally, the fourth factor, Digital Transformation (DT), is strongly associated with variables like DT4 (0.798), DT3 (0.744), DT5 (0.712), DT2 (0.672), and DT1 (0.450), all related to digital transformation.

Prior to this analysis, Cronbach’s Alpha coefficients were checked to determine the reliability and sufficiency of the data. The Cronbach’s Alpha values for the four constructs (DC: 0.956; OC: 0.897; DHRM: 0.913; DT: 0.851) are all above 0.85, indicating high internal consistency and reliability of the scales used to measure these constructs in the current study [49]. These results suggest that the items used to measure each construct are highly correlated and suitable for use in research. Given that the Cronbach’s Alpha coefficient exceeds 0.7, this suggests that the provided data are sufficient, and the responses are reliable.

5.2. Confirmatory Factor Analysis (CFA)

Table 6. Factor Loads.

Factor	Indicator	Estimator	SE	Z	p
Factor 1	DT1	0.648	0.0553	11.7	<0.001
	DT2	0.823	0.0549	15.0	<0.001
	DT3	0.702	0.0568	12.4	<0.001
	DT4	0.858	0.0582	14.7	<0.001
	DT5	0.935	0.0534	17.5	<0.001
Factor 2	DHRM1	0.923	0.0525	17.6	<0.001
	DHRM2	0.998	0.0538	18.5	<0.001
	DHRM3	0.908	0.0528	17.2	<0.001
	DHRM4	0.895	0.0543	16.5	<0.001
	DHRM5	0.970	0.0588	16.5	<0.001
Factor 3	OC1	0.502	0.0481	10.4	<0.001
	OC2	0.771	0.0435	17.7	<0.001
	OC3	0.814	0.0476	17.1	<0.001
	OC4	0.728	0.0417	17.4	<0.001
	OC5	0.706	0.0463	15.3	<0.001
	OC6	0.639	0.0427	15.0	<0.001
	OC7	0.560	0.0465	12.0	<0.001
Factor 4	DC1	1.056	0.0489	21.6	<0.001
	DC2	1.078	0.0494	21.8	<0.001
	DC3	1.032	0.0473	21.8	<0.001
	DC4	1.005	0.0524	19.2	<0.001

presents the results of Confirmatory Factor Analysis conducted using jamovi version 2.5 [54], including factor loadings (Estimator), standard errors (SE), Z values, and p-values for each indicator in relation to their respective factors. The factors are DT (Digital Transformation), DHRM (Digital Human Resource Management), OC (Organizational Culture), and DC (Digital Competencies).

Factor loadings for Factor 1 (Digital Transformation) are high, especially for DT5 (0.935), DT4 (0.858), and DT2 (0.823), indicating strong associations with the DT factor. All p-values are significantly less than 0.001, confirming the validity of these relationships. Factor loadings for Factor 2 (Digital Human Resource Management) are also high, particularly for DHRM2 (0.998), DHRM5 (0.970), and DHRM1 (0.923). This suggests a strong relationship between these indicators and HR management. All p-values are significantly less than 0.001.

Factor loadings for Factor 3 (Organizational Culture) range from moderate to high, with OC2 (0.771) and OC3 (0.814) being the highest, indicating strong associations with OC. All p-values are significantly less than 0.001. Factor loadings for Factor 4 (Digital Competencies) are very high, all exceeding 1, indicating a strong association between the indicators and DC. All p-values are significantly less than 0.001, confirming the robustness of these relationships. The CFA shows that all indicators load strongly on their respective factors, with p-values significantly less than 0.001, indicating that the relationships between observed variables and latent factors are statistically significant. This supports the factorial structure proposed in the current study.

5.3. Evaluation of the Measurement Model

To evaluate the measurement model, reliability and validity indices are analyzed (see

Table 7. Reliability and validity of the construct.

	Cronbach’s Alpha	rho_a	rho_c	AVE
Digital Competencies	0.956	0.956	0.968	0.884
Organizational Culture	0.897	0.899	0.919	0.621
Digital Human Resource Management	0.913	0.913	0.935	0.741
Digital Transformation	0.851	0.856	0.894	0.628

). Firstly, internal reliability is examined through Cronbach’s Alpha and Composite Reliability using SmartPLS4 version 4.1.0.3 [58]. For the DC construct, Cronbach’s Alpha is 0.956 and Composite Reliability (rho_c) is 0.968. These values indicate excellent internal reliability [49]. OC shows a Cronbach’s Alpha of 0.897 and Composite Reliability (rho_c) of 0.919, suggesting good reliability as well. In the case of DHRM, both Cronbach’s Alpha and Composite Reliability are 0.913 and 0.935, respectively, demonstrating high internal consistency. Finally, DT has a Cronbach’s Alpha of 0.851 and Composite Reliability (rho_c) of 0.894, meeting acceptable reliability criteria.

In terms of convergent validity, we used Average Variance Extracted (AVE) as an indicator. DC show an AVE of 0.884, which is excellent. OC, although lower, also meets the criterion for convergent validity with an AVE of 0.621. DHRM has an AVE of 0.741, reflecting good convergent validity. Similarly, DT has an AVE of 0.628, meeting the required threshold for convergent validity. In conclusion, all measurement model constructs demonstrate high levels of reliability and convergent validity. This suggests that the indicators used are consistent and appropriate for measuring the defined theoretical constructs.

To verify that the constructs are distinct from each other using the Fornell-Larcker criterion, one should compare the square root of the AVE, found on the diagonal of the matrix, with the correlations between the constructs, represented by the off-diagonal values (

Table 8. Fornell-Larcker Criterion.

	Digital Competencies	Organizational Culture	Digital Human Resource Management	Digital Transformation
Digital Competencies	0.940
Organizational Culture	0.435	0.788
Digital Human Resource Management	0.753	0.563	0.861
Digital Transformation	0.675	0.414	0.637	0.793

). According to the Fornell-Larcker criterion, the square root of the AVE for each construct should be greater than any correlation between that construct and the others [50,59].

In the case of DC, the square root of AVE is 0.940. When comparing this value with the correlations with the other constructs—OC (0.435), DHRM (0.753), and DT (0.675)—it is observed that the square root of AVE is greater than all the correlations. This indicates that DC is a distinct construct from the others. For OC, the square root of AVE is 0.788. Comparing this value with the correlations with DC (0.435), DHRM (0.563), and DT (0.414), it is also noted that the square root of AVE is higher than the correlations, confirming the distinction of OC from the other constructs.

Regarding DHRM, its square root of AVE is 0.861. The correlations with the other constructs, DC (0.753), OC (0.563), and DT (0.637), are all lower than the square root of AVE, confirming that DHRM is a distinct construct. Finally, for DT, the square root of AVE is 0.793. The correlations with DC (0.675), OC (0.414), and DHRM (0.637) are lower than the square root of AVE. This suggests that DT is a separate construct from the others. In conclusion, the square root of AVE for each construct is greater than the correlations between that construct and the others, fulfilling the Fornell-Larcker criterion for discriminant validity [59]. Therefore, it can be concluded that each construct is unique and captures a different concept in the proposed model.

5.4. Structural Model Evaluation

To assess collinearity among predictors in the structural model, SmartPLS4 version 4.1.0.3 was used to analyze the Variance Inflation Factor (VIF) values presented in

Table 9. Variance Inflation Factor.

	VIF
Digital Competencies → Organizational Culture	2.714
Digital Competencies → Digital Human Resource Management	1.838
Digital Human Resource Management → Organizational Culture	2.485
Digital Transformation → Organizational Culture	1.976
Digital Transformation → Digital Human Resource Management	1.838

. The VIF value indicates the extent to which the variance of a regression estimator increases due to collinearity among predictors. VIF values less than 5 indicate that there are no significant collinearity issues.

First, we observe that for the relationship between DC and OC, the VIF is 2.714. This value, being less than 5, suggests that the collinearity between DC and the other variable in the model is low, and should not significantly affect the analysis results. Similarly, for the relationship between DC and DHRM, the VIF is 1.838, again within the acceptable range, indicating minimal collinearity.

Regarding the relationship between DHRM and OC, the VIF is 2.485. This value is also less than 5, indicating manageable collinearity with the other variables in the model and not compromising the analysis’ stability. Likewise, for the relationship between DT and OC, the VIF is 1.976, showing low and acceptable collinearity. Finally, the relationship between DT and DHRM has a VIF of 1.838, confirming no significant collinearity issues in this relationship.

In summary, all VIF values are less than 5, ensuring no collinearity problems among the model predictors. This implies that the independent variables are not highly correlated with each other, and therefore, the results of the structural model can be considered reliable. To analyze

Table 10. Path coefficients.

	O	M	STDEV	|O/STDEV|	p-Values
Digital Competencies → Organizational Culture	−0.020	−0.020	0.083	0.235	0.814
Digital Competencies → Digital Human Resource Management	0.594	0.591	0.053	11.263	0.000
Digital Human Resource Management → Organizational Culture	0.514	0.517	0.071	7.261	0.000
Digital Transformation → Organizational Culture	0.100	0.101	0.076	1.306	0.192
Digital Transformation →Digital Human Resource Management	0.236	0.240	0.058	4.096	0.000

, we assessed the significance of paths (relationships) between variables in the structural model. Key indicators include the original sample coefficient (O), sample mean (M), standard deviation (STDEV), t-statistics (|O/STDEV|), and p-values. A p-value less than 0.05 indicates that the relationship is statistically significant.

First, we consider the relationship between DC and OC, which has a coefficient of −0.020. The sample mean is also −0.020 with a standard deviation of 0.083. The t-statistic is 0.235 and the p-value is 0.814, indicating that this relationship is not significant, as the p-value is greater than 0.05. In contrast, the relationship between DC and DHRM shows a coefficient of 0.594. The sample mean is 0.591 with a standard deviation of 0.053. The t-statistic is 11.263 and the p-value is 0.000, indicating a highly significant relationship, given that the p-value is much less than 0.05.

The relationship between DHRM and OC has a coefficient of 0.514. The sample mean is 0.517 with a standard deviation of 0.071. The t-statistic is 7.261 and the p-value is 0.000, indicating a highly significant relationship. For the relationship between DT and OC, the coefficient is 0.100, the sample mean is 0.101, and the standard deviation is 0.076. The t-statistic is 1.306 and the p-value is 0.192. This suggests that the relationship is not significant, as the p-value is greater than 0.05. Finally, the relationship between DT and DHRM has a coefficient of 0.236. The sample mean is 0.240 with a standard deviation of 0.058. The t-statistic is 4.096 and the p-value is 0.000, indicating a significant relationship.

In summary, the significant relationships in the model are Digital Competencies → Digital Human Resource Management, Digital Human Resource Management → Organizational Culture, and Digital Transformation → Digital Human Resource Management. The non-significant relationships are Digital Competencies → Organizational Culture and Digital Transformation → Organizational Culture, as their p-values are greater than 0.05. This suggests that DHRM plays a crucial role as a mediator in the model, significantly influencing OC.

To assess the variance explained by the model, we observe in

Table 11. R squared.

	R2	Adjusted R2
Organizational Culture	0.322	0.316
Digital Human Resource Management	0.598	0.595

the values of R² and adjusted R². The dependent variable OC has an R² of 0.322 and an adjusted R² of 0.316, indicating that 32.2% of the variance in OC is explained by the predictors in the model, with a slight correction to 31.6% when adjusting for the number of predictors. Regarding the dependent variable DHRM, the R² is 0.598 and the adjusted R² is 0.595, suggesting that 59.8% of the variance in DHRM is explained by the predictors, with a minimal adjustment to 59.5% when adjusting for the number of predictors. These values indicate that the model has a moderate to high ability to explain the variance in these dependent variables [53].

To assess the size of each predictor’s effect on the dependent variables, we analyze the f² values from

Table 12. F square.

	f2
Digital Competencies → Organizational Culture	0.000
Digital Competencies → Digital Human Resource Management	0.477
Digital Human Resource Management → Organizational Culture	0.157
Digital Transformation → Organizational Culture	0.007
Digital Transformation → Digital Human Resource Management	0.075

. The relationship between DC and OC has an f² of 0.000, indicating that DC have no significant effect on OC. In contrast, the relationship between DC and DHRM shows an f² of 0.477, suggesting a large and significant effect of DC on DHRM.

The relationship between DHRM and OC has an f² of 0.157, indicating a moderate effect. This suggests that DHRM contributes significantly to explaining variance in OC. On the other hand, the relationship between DT and OC has an f² of 0.007, indicating that DT has a very small, practically insignificant effect on OC.

Finally, the relationship between DT and DHRM has an f² of 0.075, suggesting a small but significant effect of DT on DHRM. In summary, the results indicate that DC have a highly significant effect on DHRM and that DHRM, in turn, has a moderate effect on OC. DT has minor effects on both dependent variables, with its impact being more significant on DHRM than on OC.

To assess the predictive relevance of the model, Q² predict values from

Table 13. Predictive relevance of the model.

	Q2 Predict	RMSE	MAE
Organizational Culture	0.200	0.906	0.670
Digital Human Resource Management	0.591	0.644	0.507

are used. The Q² predict for OC is 0.200, indicating moderate predictive relevance for this construct. The RMSE (0.906) and MAE (0.670) values for OC suggest reasonable accuracy in predictions, though there is room for improving predictive precision. On the other hand, the Q² predict for DHRM is 0.591, indicating high predictive relevance for this construct. The RMSE (0.644) and MAE (0.507) values are lower than those for OC, suggesting higher precision and lower error in predictions for DHRM. In other words, the model shows significant predictive capability, especially for DHRM, whereas for OC, it demonstrates moderate predictive relevance.

5.5. Interpretation of Path Coefficients

The coefficients of the paths are interpreted below to determine if hypotheses H1, H2, H3, H4, and H5 are supported [50]. The coefficient for the relationship between DT and DHRM is 0.236, with a p-value of 0.000, indicating that this relationship is significant. Therefore, H1 is supported. The coefficient for the relationship between DC and DHRM is 0.594, with a p-value of 0.000, showing that this relationship is highly significant. Hence, H2 is supported. The relationship between DHRM and OC has a coefficient of 0.514 and a p-value of 0.000, indicating that this relationship is significant. Therefore, H3 is supported.

Although the direct relationship between DT and OC is not significant (coefficient of 0.100, p-value of 0.192), the indirect relationship through DHRM is significant. This is evidenced by the significant relationship between DT and DHRM (coefficient of 0.236, p-value of 0.000), and the significant relationship between DHRM and OC (coefficient of 0.514, p-value of 0.000). Therefore, H4 is supported based on mediation.

The direct relationship between DC and OC is not significant (coefficient of −0.020, p-value of 0.814). However, the indirect relationship through DHRM is significant, given that both the relationship between DC and DHRM (coefficient of 0.594, p-value of 0.000) and the relationship between DHRM and OC (coefficient of 0.514, p-value of 0.000) are significant. Therefore, H5 is supported based on mediation.

In short, hypotheses H1, H2, H3, H4, and H5 are supported by the results obtained, although H4 and H5 are supported through mediation via DHRM. Path coefficients and their significances support these conclusions, reflecting the importance of DHRM in mediating the effects of DT and DC on OC. Figure 2 below shows the hypothesis testing results.

5.6. Discussion

DT shows a significant impact on DHRM, with a coefficient of 0.236 and a p-value of 0.000, supporting hypothesis H1. This finding aligns with the studies by Cui et al. [17] and Wang [20], emphasizing how the adoption of advanced technologies such as Big Data and Artificial Intelligence in business operations enhances human resource management. In the context of SMEs in Lima, this implies that companies should prioritize investment in digital technologies and reconfigure business processes to optimize HR management, considering these firms face unique challenges related to limited resources and the need to quickly adapt to technological changes.

DC have a highly significant impact on DHRM, with a coefficient of 0.594 and a p-value of 0.000, supporting hypothesis H2. This finding aligns with the work of van Laar et al. [10] and Shakina et al. [23], which emphasizes the importance of digital skills such as communication, collaboration, and effective use of digital platforms in the workplace. In SMEs in Lima, this suggests that companies should focus their training programs on developing DC to enhance HR management and organizational productivity. The need for digital skills is particularly critical in this context, where competitiveness and efficiency can determine the company’s survival.

DHRM has a positive and significant impact on OC, with a coefficient of 0.514 and a p-value of 0.000, validating hypothesis H3. This aligns with the findings of Vrontis et al. [31] and Garg et al. [33], highlighting how process automation and the integration of advanced technologies in HR management can transform OC. In Lima’s SMEs, this result indicates that the implementation of E-HRM systems and ongoing training in digital tools can strengthen cultural values and practices within organizations, fostering a more adaptive and innovation-oriented OC.

Although the direct relationship between DT and OC is not significant, hypothesis H4 is supported through the mediation of DHRM. This suggests that DT, by enhancing HR management, indirectly influences OC. This finding is complemented by Bhatt and Bae’s studies [18], which discuss the collaboration between humans and algorithms to enhance organizational efficiency and decision-making. Practical implications for SMEs in Lima indicate that these companies should focus on how DT can improve HR management to positively impact their OC, considering the importance of adaptability in a competitive and changing environment.

Similarly, DC indirectly impact OC through DHRM. Although the direct relationship is not significant, mediation is key, supporting hypothesis H5. This finding resonates with the work of Ferreira et al. [22] and Yang et al. [26], emphasizing the need for DC in both the workplace and educational contexts for cultural adaptation. Practically, within the context of SMEs in Lima, this suggests that investment in digital competency development can be an effective strategy to influence OC through improved human resource management, thereby facilitating adaptation to market and technological changes.

Previous studies have underscored the importance of DT in business reengineering and the adoption of advanced technologies. The findings of this research confirm and extend these discoveries, showing that DT, through effective Human Resource management, can significantly transform OC in SMEs in Lima. This implies that organizations must not only adopt digital technologies but also strategically integrate them into Human Resource management, harnessing opportunities for efficiency and adaptability improvement that these technologies offer.

The findings confirm the relevance of DC in human resource management and their indirect impact on OC. This aligns with studies by van Laar et al. [10] and Hwang et al. [25] on the necessity of digital skills for workplace adaptation and efficiency. Within Lima’s SME context, companies should invest in digital training for their employees to foster an adaptive, innovation-oriented OC, crucial in a rapidly evolving technological and economic environment.

This research emphasizes the central importance of DHRM as a mediator between DT, DC, and OC. This finding aligns with studies by Vrontis et al. [31] and Alhamad et al. [30] on HR process automation and digitalization. In Lima’s SMEs, organizations must adopt E-HRM technologies and digital training approaches to enhance human resource management and consequently their OC, better adapting to market demands and technological opportunities. It is essential to clarify that the sample of this study is composed of business leaders, which guarantees that the results obtained are directly applicable to the target population defined in the objectives and hypotheses of the study.

5.6.1. Comparison with Previous Research

This study provides new findings on the influence of DT and DC on DHRM and OC in SMEs in Lima, Peru. Compared to previous research, studies such as those by Cui et al. [17] and Wang [20] have demonstrated that the adoption of digital technologies improves efficiency and decision-making in human resource management. Our study confirms these results in the context of SMEs in Lima, but also identifies that DHRM mediates the relationship between DT and OC, an aspect that has not been widely explored previously.

Regarding DC, previous research by van Laar et al. [10] and Shakina et al. [23] has emphasized their importance for adaptation and efficiency in the workplace. This study reinforces these findings and adds that DC not only enhance DHRM but are also crucial for promoting a sustainable OC in SMEs in Lima.

With respect to the mediation of DHRM, previous research such as that by Vrontis et al. [31] and Garg et al. [33] has explored how digitalization can influence human resource management. Our study provides a new perspective by demonstrating that DHRM acts as a mediator between DT, DC, and OC, offering a deeper understanding of the underlying mechanisms.

5.6.2. Theoretical Contribution

By exploring the interrelationship between DT, DC, and their impact on DHRM and OC, this work offers new theoretical perspectives. A key contribution is the identification of the mediating role of DHRM, demonstrating how it mediates the relationship between DT and OC. This finding provides an understanding of the mechanisms driving organizational change in the context of digitalization. Furthermore, this study shows how digitalization and DC can promote sustainable practices in SMEs, contributing to organizational sustainability. On the other hand, it offers practical guidelines for entrepreneurs interested in sustainability through digitalization.

5.6.3. Practical Contributions

This study suggests that SMEs in Lima, Peru, should invest in digital technologies to improve human resource management and foster an adaptive OC. It is crucial to develop DC through training programs, as these skills enhance efficiency and promote innovation. The adoption of DHRM practices, such as process automation and data analytics, can increase transparency and organizational cohesion. Additionally, digitalization can facilitate sustainable business practices, such as reducing paper usage and optimizing energy consumption. Policymakers should design support programs that include subsidies and tax incentives to facilitate digitalization and the development of DC in SMEs.

5.6.4. Generalization and Applicability of Findings

Although this study was conducted in the context of SMEs in Lima, Peru, the findings have potential implications for other cities and regions. The dynamics of digitalization and its impact on DHRM and OC can vary depending on local factors such as the level of technological development, access to digital infrastructure, and government policies. However, the trends observed in Lima may be relevant to other regions with similar socioeconomic characteristics. It is crucial to consider that the adaptability and flexibility of SMEs to digitalization, as observed in this study, could be applicable to SMEs in other urban contexts. Future research could explore these relationships in different geographical settings to validate and expand the generalization of these findings.

5.6.5. Engineering Application and Managerial Vision of the Studied Problem

From an engineering perspective, the adoption of advanced technologies such as Big Data, Artificial Intelligence, and process automation optimizes operational efficiency and strategic decision-making. These technologies enable more precise resource management, reduce operational costs, and improve the quality of products and services. From a managerial standpoint, these innovations facilitate the creation of an adaptable and resilient organizational environment capable of quickly responding to market changes. Furthermore, investment in digital training and the implementation of digital human resource management strategies not only promote a culture of innovation but also ensure the long-term sustainability and competitiveness of SMEs.

6. Conclusions

This study confirms that digital transformation (DT) and digital competencies (DC) significantly influence digital human resource management (DHRM), positively impacting the organizational culture (OC) of SMEs in Lima, Peru. DT indirectly affects OC through DHRM, and DC are crucial for the effectiveness of human resource management and the promotion of an adaptive and innovative OC. The sample, composed exclusively of business leaders, ensures that the results are representative and relevant to the business sector, thus validating the hypotheses and objectives. The study presents several limitations, such as the use of non-probability sampling, which may restrict the generalization of the results; the cross-sectional design limits the ability to establish definitive causal relationships; self-reported data may introduce response biases; and the exclusive focus on SMEs in Lima restricts the applicability of the findings to other geographical and cultural contexts.

To improve future research, it is suggested to implement longitudinal studies to understand the long-term effects of digitalization on OC, expand the sample to include SMEs from different regions and economic sectors, and incorporate more diverse data collection methods, such as in-depth interviews and qualitative analyses, to complement the quantitative data. Specific future directions include investigating how digitalization impacts innovation, job satisfaction, and talent retention in SMEs; analyzing the interaction between digitalization and employees’ demographic characteristics; and exploring the role of government policies in facilitating digital transition and the development of DC in SMEs. Business leaders should strategically invest in DT and DC training to transform operations and OC, promoting greater adaptability and efficiency. Policies should facilitate digital transition and the development of digital skills in the business environment.