Driving Innovation Through Customer Relationship Management—A Data-Driven Approach

Abstract

Customer relationship management (CRM) is a key factor driving innovation and organizational growth. The present study investigated the relationship between data-driven CRM (DDCRM) and innovation in Taiwan. We developed a research model involving CRM theory, innovation theory, and the technology adoption model (TAM) theory to account for the cultural and organizational contexts of Taiwan and investigate this relationship. The study distributed questionnaires to employees and stakeholders within Taiwanese firms to understand their firms’ innovation and CRM practices. The results indicate that technology adoption and organizational culture have mediating effects and industry dynamics and organizational size have moderating effects on the relationship between DDCRM and innovation. That is, adopting new technology and having an organizational culture that supports innovation and company-wide collaboration can enhance the effects of implementing DDCRM practices. In addition, certain industries (e.g., the technology industry) are more likely to effectively leverage DDCRM practices to drive innovation, and although large organizations have more resources and can therefore more easily implement CRM systems, small and medium-sized enterprises (SMEs) can more quickly adapt and innovate on the basis of CRM insights. These findings highlight the importance of DDCRM in driving innovation and reveal key factors influencing the effectiveness of CRM in doing so. The study features comprehensive suggestions of operable strategies and measures for Taiwanese SMEs, hopefully assisting them in gaining a market advantage and elevating their innovation capabilities by leveraging DDCRM practices.

1. Introduction

Buhl et al. [1] pointed out that in the modern business environment, in which global markets are becoming increasingly competitive and technology is continually reshaping industries, CRM has emerged as a pivotal factor driving innovation and sustaining organizational growth. Manyika et al. [2] described that CRM encompasses a spectrum of activities aimed at understanding, managing, and nurturing customer interactions throughout their user journeys. This entails capturing and analyzing customer data from various touchpoints, including sales transactions, marketing engagements, and customer service interactions. Santouridis and Tsachtani [3] suggested that the organizational resources allocated to CRM exert a robust positive influence across all stages of the customer life cycle.

The influence of CRM is particularly pronounced in global and dynamic regions such as Taiwan, which has a strong technology sector, robust manufacturing base, a myriad of SMEs, and a focus on entrepreneurship.

Melo et al. [4] pointed out that SMEs are responsible for 90% of all business and 50% of employment globally. But there is no globally standardized definition of the term, it varies across nations. In Taiwan, according to regulations and data issued by the Ministry of Economic Affairs [5], an SME refers to an enterprise where either its paid-in capital is no more than approximately USD 3 million or its employee number is less than 200. SMEs accounted for 98% in total of domestic businesses, and employed 80% of total local labors in 2023.

Guerola-Navarro et al. [6] indicated that entrepreneurship and cultural factors as well as consumer preferences play significant roles in shaping CRM strategies in Taiwan. Local consumers are discerning; they place considerable importance on interpersonal relationships and trust. Therefore, they value personalized interactions and tailored experiences. Meanwhile, the adoption of various technologies is widespread in Taiwan. Domestic organizations consequently often accumulate vast repositories of customer data across disparate systems, which leads to these organizations obtaining siloed information and fragmented insights. Nevertheless, the volume and diversity of data generated in this region present both opportunities and challenges for businesses seeking to obtain actionable insights [2].

Despite the above-mentioned challenges, the overarching objective of CRM practices is consistent: to cultivate enduring relationships with customers and to leverage these relationships to drive innovation in all aspects of business [1]. Thus, how Taiwanese enterprises can harness CRM to foster innovation is not only relevant but also crucial to their continued success [1]. Specifically, the question of how SMEs with limited budgets should allocate their resources to focus on practical and feasible CRM strategies becomes the key issue.

The current study consulted previous research; however, much of it was unsuitable to be applied to Taiwanese businesses. For example, the study of Herman et al. [7]. Their research samples are limited to SMEs in pearl jewelry and handicraft, and the variables lack a cultural factor. Migdadi [8] used data provided by only one General Manager per firm; middle managers and employees were left out of the survey. Morgan et al.’s [9] research used complex survey designs to collect information only from new service development teams, without customer participation. Khong et al. [10] investigated the effectiveness of CRM in banking services in Taiwan. This study concluded that implementing CRM positively influences customer satisfaction. But it is limited to the commercial banking sector and focused on customer satisfaction; innovation is not the theme.

Against this backdrop, the current study investigated the intersection of CRM and innovation in Taiwan, employing a data-driven approach to obtain key insights and identify best practices.

On the basis of the above-mentioned reviews, this study formulates the following research questions. RQ1: How much do CRM practices influence innovation outcomes, including product innovation, process innovation, marketing innovation, and service innovation, in Taiwanese businesses? RQ2: How does technology adoption mediate the relationship between CRM practices and innovation in Taiwanese businesses? RQ3: What role does organizational culture play in mediating the relationship between CRM practices and innovation in Taiwanese businesses? RQ4: How does the impact of CRM practices on innovation outcomes vary across industries and in organizations of different sizes in Taiwan?

The study identified success factors and challenges, aiming to propose actionable recommendations to assist businesses in harnessing the transformative potential of CRM and to drive innovation. By focusing on challenges and opportunities specific to the Taiwanese market, this study features comprehensive suggestions of operable strategies and practical measures for implementing DDCRM practices to drive organizational innovation. These suggestions are tailored to the needs of Taiwanese SMEs. The details are elucidated in Section 6.4.

This introduction is followed sequentially by Section 2, Literature Review and Development of Hypotheses; Section 3, Research Methodology; Section 4, Descriptive and Inferential Analyses; Section 5, Interpretation and Hypothesis Testing; and Section 6 Conclusions, Reflections, and Suggestions.

2. Literature Review and Development of Hypothesis

The present study reviewed several theoretical perspectives to obtain a comprehensive understanding of how to explore businesses leveraging CRM practices to foster innovation in Taiwan.

Regarding CRM theory, Gil-Gomez [11] et al. mentioned that CRM emerged in the 1970s as a new tool for managing and optimizing sales-force automation within companies. Ever since, it has been become one of the most popular tools for enterprise information management, not only for sales and marketing purpose but also for more effective customer interactions and understandings of organizational behavior. Guerola-Navarro et al. [12] pointed out that CRM is essential for establishing effective channels and methods for customer-centric information management. Its main objective is to improve the management of customer relationships to facilitate better commercial results for companies. Nowadays, Taiwanese businesses generally recognize the importance of leveraging data analytics, customer segmentation, and personalized marketing strategies to deepen customer relationships and drive innovation.

Concerning innovation theory and DDCRM. Kotler and Keller [13] defined that an innovation is any good, service, or idea that someone perceives as new, no matter how long its history may be. They also mentioned that Roger defined the innovation diffusion process as “the spread of a new idea from its source of invention or creation to its ultimate users or adapters”. The consumer adoption process is the mental steps through which an individual or group pass from first hearing about an innovation to its final adoption. Innovation plays a central role in driving organizational growth and competitiveness. Femina et al. [14] reported that data mining in CRM could be used to effectively predict consumer behavior. Siau and Fruhling [15] argued that an organization’s innovation potential is determined by its data analysis capabilities, which involve an organization’s utilization of technology to develop innovative systems, policies, software, products, processes, devices, and services. However, according to Croholm et al. [16], although the Taiwanese business landscape includes a dynamic technology sector and is characterized by a culture of entrepreneurship, data analytics is rarely leveraged to drive innovation in most companies.

Consequently, successful CRM initiatives in Taiwan must involve more than mere transactional exchanges; they must be focused on fostering deeper, more meaningful connections with customers. Such initiatives must be based on a nuanced understanding of local customs, language, and cultural sensitivities. This localization is crucial in CRM implementation. In this context, innovation theory can be applied to evaluate how businesses should leverage DDCRM practices to drive innovation in Taiwan.

This study draws on findings from the above-mentioned studies to develop Hypothesis 1.

H1. 

DDCRM practices positively influence innovation outcomes in businesses.

This hypothesis suggests that Taiwanese businesses that effectively implement DDCRM practices are more likely to achieve innovation outcomes such as product innovation, process innovation, marketing innovation, and service innovation.

Several studies have examined the adoption of CRM platforms by employing well-established frameworks such as the TAM. Davis [17] developed the conceptual framework of the TAM, which moves from system features and capabilities, to users’ motivation to use systems, and then to actual system use. The TAM involves the factors influencing the adoption, implementation, and utilization of technological innovations within organizations. Davis et al. [18] validated scales for two specific variables, perceived usefulness (PU) and perceived ease of use (PEOU) to evaluate user acceptance. Their research concluded that these scales exhibited high convergent, discriminant, and factorial validity.

Within DDCRM theory, integrating technology solutions, such as CRM software platforms, is considered a key means of streamlining customer interactions, improving service delivery, and facilitating real-time decision-making. Gil-Gomez et al. [11] suggest that various CRM components, including sales, marketing, and services, in Taiwan have a positive influence on customer knowledge management, innovation, and efforts toward digital transformation and sustainable business model innovation.

However, MacAfee et al. [19] pointed out that one of the primary challenges Taiwanese enterprises face in their CRM endeavors is data integration and interoperability. Rosario et al. [20] argued that, for this challenge to be addressed, concerted efforts must be made to integrate data sources, leverage advanced analytics techniques, and deploy robust CRM platforms capable of providing a unified view of the customer journey.

In addition, with reference to the issues of data privacy and legal risk, Schmidt et al. [21] concluded in their research that safety and security aspects are the most important influencing factors for users to implement a CRM information system, and that enterprises should cover risks in terms of safety and security, e.g., where the CRM is hosted and the data are stored. Gartner [22] suggested that organizations must comply with data privacy regulations to mitigate legal risks and enhance customer trust and loyalty, which can lay the foundation for sustainable innovation efforts.

Therefore, Taiwanese enterprises must also navigate regulatory frameworks and address data privacy concerns related to CRM practices. In Taiwan, data protection regulations have evolved and enforcement mechanisms have been strengthened. Today, organizations face heightened scrutiny regarding the collection, storage, and utilization of customer data. The above studies are utilized to formulate Hypothesis 2.

H2. 

Technology adoption mediates the relationship between DDCRM practices and innovation in businesses.

This hypothesis suggests that the adoption and utilization of CRM technology platforms play a mediating role in the process of translating DDCRM practices into innovation outcomes. Organizations that successfully adopt CRM technology are more likely to leverage customer insights and analytics to drive innovation.

On the subject of cultural and organizational contexts, Fata et al. [23] argued that organizational culture impacts the way business organizations operate. Organizational culture is dependent on leadership attitude, national culture, employee attitudes, and management policies. Zeb et al. [24] advocated that organizational culture stimulates innovative activities among individual members of an organization. Empirical research showed a considerable relationship between organizational culture and innovation.

In Taiwan, where businesses often operate within hierarchical organizational structures and cultural norms; addressing these contextual factors is critical for driving CRM innovation initiatives. On the basis of the above findings, the study develops Hypothesis 3 as follows.

H3. 

Organizational culture mediates the relationship between DDCRM practices and innovation in businesses.

This hypothesis suggests that the cultural context of Taiwan influences whether DDCRM practices are accepted and how they are implemented to drive innovation. Organizational cultures in which customer centricity, collaboration, and experimentation are valued are more likely to experience CRM-driven innovation.

Finally, as for industry dynamics and organizational size, Nambisan et al. [25] emphasized that technology is an essential component influencing whether companies are able to confront challenges related to innovation. Nevertheless, Salah et al. [26] argued that a competitive market environment enhances innovation among organizations. Empirical studies have revealed that higher innovation adoption likelihood is linked to higher competitive pressure. Their findings also indicated that firm size moderates the relationship between top management support, compatibility, customer pressure, IT infrastructure, and CRM adoption.

However, Taiwan has a diverse business landscape composed of multinational corporations, SMEs, and small firms and a burgeoning startup ecosystem. In Taiwan, the adoption of CRM practices has varied widely across industries, with such adoption influenced by factors such as organizational size, sectoral dynamics, and technological sophistication. The study hereby formulates Hypothesis 4 as follows.

H4. 

The influence of DDCRM practices on innovation outcomes varies across industries and with organizational size.

This hypothesis suggests the non-uniformity of the influence of DDCRM practices on innovation outcomes.

These four hypotheses respond to each of the research questions, respectively, described prior to the literature review. The present study employs the TAM and extends the aforementioned previous research results to further investigate local CRM adoption, hopefully contributing to understandings of the adoption of CRM and its subsequent innovative effectiveness, particularly in Taiwan.

3. Research Methodology

3.1. Research Model and Variables

The research model of this study is shown in Figure 1. This model was developed on the basis of the above-mentioned previous literature and the identified hypotheses of this study.

The study also consulted theoretical concepts and research constructs from related articles, such as the theoretical model utilized by Salah et al. [26] for investigating the adoption of CRM in Palestinian SMEs; the theoretical framework employed by Miguel et al. [27] for exploring the impact of dynamic capabilities on customer satisfaction through transmission in the automotive sector in Spain; the theory-based framework used by Eriksson and Heikkila [28] for researching capabilities for data-driven innovation in B2B industrial companies in Finland; the conceptual model developed by Putra et al. [29] for understanding digital marketing adoption by small travel agencies in Indonesia using Diffusion of Innovations theory (DIT); and the conceptual model adopted by Migdadi [8] for studying knowledge management, CRM, and innovation capabilities in Jordan. In addition, the characteristics of Taiwanese organizational factors and industrial competition were also taken into account in order to explore the relationship between DDCRM and innovation effectiveness in Taiwanese businesses.

This model lays out the key constructs, variables, and relationships under investigation in this study and aided in the organization and analysis of the data collected through the study survey.

Independent variable: DDCRM practices are at the core of this model. They involve the strategic utilization of customer data, analytics, and technology solutions to enhance customer relationships and drive business innovation. DDCRM practices include aspects such as data collection, analysis, segmentation, personalized marketing, and customer engagement through CRM platforms.

Mediating variables: Two mediating variables were considered. Technology adoption is the adoption and utilization of CRM platforms which mediate the relationship between DDCRM practices and innovation. Factors such as PU, PEOU, and organizational readiness influence how effectively businesses in Taiwan implement and leverage CRM technology to drive innovation.

Organizational culture is the cultural context surrounding Taiwanese business which mediates the relationship between DDCRM practices and innovation. Cultural factors such as a collectivist orientation, a relationship-centered orientation, and entrepreneurialism shape how Taiwanese organizations perceive CRM as a tool for innovation.

Moderating variables: Two moderating variables were considered. Industry dynamics are the specific characteristics of industries within Taiwan, including the technology sector, manufacturing sector, and service sector, which may influence how DDCRM practices drive innovation. In addition, industry-specific factors, such as market competitiveness, technological sophistication, and customer expectations, can influence the impact of CRM on innovation outcomes.

Organizational size is the size and structure of organizations, including multinational corporations, SMEs, and start-ups, which may influence their adoption of DDCRM practices and the effectiveness of these practices in driving innovation.

Dependent variable: innovation. This involves the development and implementation of novel ideas, products, or processes that create value for customers and drive organizational growth. In the context of Taiwan, innovation outcomes may include product innovation, process innovation, marketing innovation, and service innovation.

3.2. Research Design and Questionnaire Form

This study conducted cross-sectional research. The research design adopted descriptive and explanatory approaches to gather quantitative and qualitative data by using a survey with closed-ended (quantitative) and open-ended (qualitative) questions. The closed-ended questions were responded to on a Likert scale and were used to understand respondents’ perceptions, attitudes, and behaviors related to DDCRM and innovation. For the open-ended questions, participants were able to provide detailed explanations, examples, and insights into their experiences with DDCRM and innovation in their organizations.

This survey covered various dimensions of DDCRM practices. The questionnaire comprised two sections and five constructs. The survey questions were formulated mainly based on theoretical concepts, research findings, and survey forms that had been utilized by previous empirical studied, as follows.

• Section 1:

Respondent demographic characteristics

The study utilized the demographic profile from Salah et al. [26], participants’ work experience from Fata et al. [23], and demographic features from Demirel [30] to formulate the survey questions in this section.

• Section 2:

This section focused on the adoption of CRM technology platforms within organizations and the perceived role of these platforms in facilitating innovation.

• Construct A: DDCRM practices

The emphasis on the data-driven approach of this study underscores the importance of evidence-based decision-making in CRM implementation and innovation strategy formulation. Herman et al. [7] pointed out that CRM comes to light as a management approach that enables companies to identify, fascinate, and increase their profitable retention of customers through a good management relationship with their customers as a support for innovative product development capability. Salah et al. [26] concluded that companies where CRM is incorporated as a business strategy tend to grow faster than those who it is not. This is due to the objective of the initiative being the enhancing of customers’ relationships, which in turn leads to maximized revenue.

This study drew on the consideration of e-CRM capability by Herman et al. [7], CRM concepts and benefits from Gil-Gomez et al. [11], and the type of analytics used by Wang et al. [31] to formulate its survey questions. Participants were asked to indicate the extent to which their organizations utilized DDCRM practices and how effectively the organizations leveraged customer data to achieve innovation across various areas.

• Construct B: Technology adoption

Kane et al. [32] pointed out that digital transformation is essential for entrepreneurial firms because consumers are becoming increasingly informed and thus have increasingly varied demands. Blanco-Gonzalez-Tejero et al. [33] emphasized that cutting-edge management information systems play a key role in data-driven innovation. Kraus et al. [34] argued that companies wishing to achieve efficient management systems conducive to successful digital transformation should implement CRM and enterprise resource planning systems

Valid measurement scales for predicting user acceptance are crucial in CRM research. Several studies have examined the adoption of information systems by employing well-established frameworks such as the TAM. Davis [17] developed the conceptual framework of the TAM, which moves from system features and capabilities, to users’ motivation to use systems, to actual system use. Davis et al. [18] validated scales for two specific variables, perceived usefulness (PU) and perceived ease of use (PEOU), to evaluate user acceptance. Their study concluded that these scales exhibited high convergent, discriminant, and factorial validity.

This study utilized IT infrastructure from Salah et al. [26]; leadership attitude, employee attitude and management policies from Fata et al. [23]; and the consideration of financial sufficiency and adequate resources by Zeb et al. [24] to formulate its survey questions.

• Construct C: Organizational culture

Omol [35] emphasized that, in the realm of organizational digital transformation, the formidable challenge of cultural resistance to change emerges as a formidable obstacle. Often, organizations grapple with resistance to change, a prevailing fear of technology, and the allure of well-established routine. Fata et al. [23] advocated that organizations need to implement a suitable leadership style that is engaging and committed to top management involvement and coordination to enable better CRM implementation. A major mismatch of culture affects the implementation of CRM.

This study drew on the consideration of risk-taking, innovation-centric approaches, and readiness to new challenges by Zeb et al. [24] and top manager support by Salah et al. [26] to formulate its survey questions.

• Construct D: Industry dynamics and organizational size

Salah et al. [26] pointed out that competitive pressure is the level of competitiveness in an industry within which an organization operates. The intensity of globalization, coupled with increasing competition and ICT development, means that companies in developing countries have been forced to concentrate on CRM for the maximization of revenue.

Firm size is considered to be a top indicator of organizational complexity. The size of a firm is the most critical adoption driver and lends strong support to CRM deployment. The successful adoption of technologies largely depends on firm size; large firms have a higher likelihood of adopting new technology like CRM and e-commerce.

This research drew on the consideration of competitive pressure and relative advantage of firm size from Salah et al. [26], firms’ perception of capabilities considered important to prepare digital transformation from Choi and Kim [36], and firm size and competitive intensity from Menguc and Auh [37] to design its survey questions.

• Construct E: Innovation effectiveness

Wang et al. [31] identified several CRM dimensions, including product innovation, process innovation, administrative innovation, marketing innovation, and service innovation, as influencing a company’s innovation capabilities. Dervitsiotis [38] argued that innovation processes are pivotal in creating and maintaining value for stakeholders because they can lead to the creation of new sources of income and improve company performance.

This study drew on the discussion of innovation type and sustainability aspect and level from Claik and Bardudeen [39], the validated measurement scale for sustainable product innovation performance from Claik [40], and product innovativeness development from Herman et al. [7] to formulate its survey questions.

• Personal comments:

Participants were given the opportunity to provide any additional comments or insights related to CRM practices, technology adoption, organizational culture, and innovation outcomes in their organizations.

This study reorganized, restructured, and updated all the related concepts and questions employed in the above-mentioned literature; we changed the wording and phrasing to aim at the object of this research and fit in with the business context of Taiwan.

The detailed questionnaire survey form is shown in Appendix A. It starts with an explanation the of survey’s purpose, followed by an informed consent statement. This form consisted of both categorical questions and Likert-scale questions to obtain a comprehensive understanding of CRM implementation and the individual perceptions of the respondents.

Survey data were quantitatively analyzed. Data analysis involved both descriptive and inferential statistics: For descriptive statistics, mean scores and standard deviations were calculated for key variables. For inferential statistics, chi-square tests and correlation and regression analyses were conducted to test the hypotheses.

3.3. Pilot Study and Sampling

The questionnaire form was first sent to a local online survey platform company for expert validation. Then, we carried out a pilot study using forty copies to test respondents’ feedback prior to the formal commencement of survey activity.

An open invitation to potential respondents was sent out and the formal surveying was conducted in May 2024. Respondents were recruited using convenience sampling. Four our sample size calculation formula, we assumed a confidence level of 95%, a standard deviation of 0.5, and an error margin of +/−5% (confidence interval). The calculation indicated that there should be no fewer than 385 participants [41].

Potential respondents, including the upstream suppliers and customers of public agencies and the private enterprises of the company which the researchers worked at, students of an EMBA course and alumni at a university in Taiwan, and individuals from the local community, were invited to participate. The respondents comprised employees and stakeholders within Taiwanese enterprises who were involved in or had insights into areas related to CRM and innovation. The respondents consisted of individuals from various departments, including marketing, sales, customer service, product development, and management. Stakeholders such as executives, managers, and decision-makers who oversaw CRM strategies and innovation initiatives within their organizations were also recruited.

Then, an internet link to the online survey platform was sent to interested respondents in order to collect their responses to items about various aspects of DDCRM and innovation implementation.

3.4. Sample Descriptions and Respondent Demographic Characteristics

This study obtained a sample of 413 participants who were specifically selected to ensure diverse demographic representation. The participants were mainly located in the northern and central areas of Taiwan and mainly based in urban cities. After 35 invalid responses were excluded, 378 valid samples were included in the analysis.

The valid responses were collected from a diverse sample of respondents of various ages, educational backgrounds, jobs, departments, and experiences. The demographic characteristics of the respondents are summarized in

Table A1. Respondent demographic characteristics.

Item
Factor	Sample (n = 378)	Percent (%)
1. Gender
Male	227	60.05
Female	151	39.95
2. Age
≤30	27	7.14
31–45	155	41.01
46–60	162	42.86
≥61	34	8.99
3. Education
High school or below	49	12.96
Associate bachelor’s degree	53	14.02
Bachelor’s degree	162	42.86
Master’s degree	104	27.51
Doctoral degree	10	2.65
4. Job position
Top management/executive	30	7.94
Middle management	63	16.67
First-line management	52	13.76
Non-managerial staff/employee	212	56.08
Others	21	5.55
5. Departments of respondents
Administration and/or planning	118	31.22
Manufacturing and/or service delivery	116	30.69
Marketing and sales	74	19.58
Product and/or service R&D	38	10.05
Others	32	8.46
6. Years of experience
Less than 1 year	24	6.35
1–3 years	47	12.43
4–6 years	53	14.02
7–10 years	55	14.55
11–20 years	95	25.13
≥21 years	104	27.52

, Appendix B.

The results regarding job position and department indicate that the majority of the participants were non-managerial staff (56.08%), followed by those who were part of middle management (16.67%), first-line management (13.76%), and top management (7.94%). This indicates that, although non-managerial staff constituted the majority of the sample, a substantial portion of the respondents (38.37%) were in decision-making positions and had potential to influence CRM and innovation strategies. In addition, the sample included individuals from various departments, with administration/planning (31.22%) and manufacturing/service delivery (30.69%) representing the largest department groups. The diversity of the departments represented in the study sample enabled us to obtain insights into how CRM practices impact different functional areas, including product innovation, process innovation, and marketing innovation.

4. Descriptive and Inferential Analyses

The answers to each of the research constructs are listed in the summary of respondent answers in

Table A2. Summary of respondent answers.

Construct
Question Item
Response Data Values	Sample (n = 378)	Percent (%)
A. Data-Driven CRM Practices
1. Has your organization established a database of customer data and/or customer feedback by using a general software computer?
None	110	29.10
Only customer database	103	27.25
Only customer feedback database	11	2.91
Both available, but operates independently	78	20.64
Both available, integrated for inter-operation	76	20.10
2. To what extent does your organization utilize the aforementioned customer database and/or customer feedback to manage customer interactions and relationships?
Not at all	116	30.69
To a small extent	83	21.96
To a moderate extent	109	28.83
To a large extent	41	10.85
To a very large extent	29	7.67
3. How effectively does your organization leverage the aforementioned customer database and analytics to drive product or process innovation?
Not effective at all	125	33.07
Slightly effective	100	26.45
Moderately effective	105	27.78
Very effective	31	8.20
Extremely effective	17	4.50
B. Technology Adoption
1. Has your organization employed a general database to store customer data obtained through a membership system, point-of-sale system, or any other touch point for analytics and management?
No, not adopted	111	29.36
Yes, partially adopted	96	25.40
Yes, moderately adopted	81	21.43
Yes, fully adopted	48	12.70
Yes, extensively adopted	42	11.11
2. Has your organization adopted professional CRM platforms to manage customer data and interactions?
No, not adopted	169	44.71
Yes, partially adopted	75	19.84
Yes, moderately adopted	83	21.96
Yes, fully adopted	27	7.14
Yes, extensively adopted	24	6.35
3. If your organization has not adopted professional CRM platforms to manage customer data and interactions, why has it not done so?
Adopted, please skip the rest of the answers	114	30.16
Small size, low turnover, no budget	69	18.25
Using general software computer to establish customer database	101	26.72
No responsible department to execute and maintain the system	57	15.08
Top management is not supportive	35	9.26
Other	2	0.53
4. Which CRM platform brand and system does your organization use to manage customer data and interactions?
Did not adopt, skip the rest of the answers	203	53.70
In-house or consultant designed	118	31.22
Taiwanese system, such as General Digital, MiCloud, etc.	31	8.20
Japanese system, such as Kintone, etc.	2	0.53
American system, such as Oracle, Ragic, Zendesk, Zoho, etc.	13	3.44
Other	11	2.91
5. How prevalent of a role do CRM technology platforms play in facilitating innovation within your organization? Please consider your experience with and the outcomes of the company’s adoption of the platforms or what you believe their role would be if your organization adopted such platforms in the future.
Not significant	146	38.62
Somewhat significant	94	24.87
Moderately significant	100	26.46
Very significant	22	5.82
Extremely significant	16	4.23
C. Organizational Culture
1. To what extent does your organizational culture place value on customer centricity and collaboration?
Not at all	39	10.31
To a small extent	138	36.51
To a moderate extent	122	32.28
To a large extent	48	12.70
To a very large extent	31	8.20
2. Does your organization implement employee proposals related to improvement or innovation?
None	126	33.33
Only for improvement	62	16.40
Only for innovation	26	6.88
Both, supportive	124	32.81
Both, extremely supportive	40	10.58
3. How supportive is your organization of experimentation and risk-taking in the pursuit of innovation?
Not supportive at all	80	21.16
Slightly supportive	140	37.04
Moderately supportive	106	28.04
Very supportive	30	7.94
Extremely supportive	22	5.82
D. Industry Dynamics and Organizational Size
1. Which industry is your organization a part of?
Technology R&D and Manufacturing	23	6.08
Traditional Manufacturing	100	26.46
Civil Engineering and Consulting	24	6.35
Bank, Insurance, and Securities	46	12.17
Hospital and Healthcare	16	4.23
Retail and Shopping Malls	46	12.17
Hotels and Restaurants	14	3.70
Real Estate Agent and Property Management	8	2.12
Other	101	26.72
2. How strong is the competition that your organization faces in your industry?
Not at all, easy to survive	36	9.52
Little, need attention to survive	108	28.57
Moderate, need effort to survive	112	29.63
Strong, need dedication to survive	105	27.78
Extremely strong, difficult to survive	17	4.50
3. Please indicate the size of your organization.
Small, 1–50 employees	157	41.54
Medium, 51–200 employees	71	18.78
Medium–Large, 201–500 employees	41	10.85
Large, 501–1000 employees	25	6.61
Conglomerate, 1001+ employees	84	22.22
E. Innovation Effectiveness
1. On what scale does innovation take place within your organization?
No specific innovation or no specific area	184	48.68
Within departmental level	51	13.49
Cross-department level	81	21.43
Company-wide level	61	16.14
Other	1	0.26
2. In what areas have innovation been achieved within your organization (more than one multilevel choices).
Rare innovation	140	26.72
Administration or logistics	116	22.14
New product development or new service processes	143	27.29
Adoption of environmentally sustainable materials	65	12.40
or new manufacturing processes
Cross-organizational integration or cross-industry alliances	60	11.45
Number of Choices	524	100
3. What kind of benefits has innovation brought to your organization? (Multilevel choices)
Economic, in terms of cost-savings	165	30.90
Quality, in products or services	206	38.58
Noneconomic, for environmental sustainability	75	14.04
No specific property	88	16.48
Others	0	0
Number of Types	534	100
4. To what extent does the innovation of your organization positively affect its performance?
Rare innovation, no positive influence	90	23.81
To a small extent	132	34.92
To a moderate extent	101	26.72
To a large extent	35	9.26
To a very large extent	20	5.29

, Appendix B. The analysis of the answers proceeds as follows.

4.1. Construct A: DDCRM Practices

The findings regarding experience (item 6, Table A1) and CRM adoption (item 1, construct A) indicate that a notable percentage of the respondents had extensive experience in the industry, with 27.52% reporting more than 21 years of experience and 25.13% reporting 11–20 years of experience. This suggests that the respondents had considerable industry knowledge, which could be correlated with how DDCRM practices are leveraged for innovation.

In addition, according to the survey results, for 29.10% of the participants, their organizations had not established a customer database; for 27.25%, their organizations had a customer database; and for 2.91%, their organizations had a customer feedback database. Notably, only 20.10% of the respondents indicated that their organizations had integrated databases. This limited adoption of integrated DDCRM systems may hinder the organizations’ ability to leverage customer insights to achieve innovation.

The results regarding the utilization of DDCRM and its effectiveness in driving innovation are, respectively, presented in item 2 and 3, construct A. As indicated in item 2, 30.69% of the participants reported that their organizations did not utilize CRM for managing customer relationships at all. Notably, only 7.67% reported that their organizations used CRM to a very large extent, and 10.85% reported their organizations to use it to a large extent, indicating that the organizations of only 18.52% of the respondents extensively use CRM. This low utilization rate suggests that many Taiwanese businesses are not effectively leveraging DDCRM practices, which may limit their potential for innovation.

As indicated in item 3, 33.07% of the respondents indicated that they did not believe CRM to be effective at all in driving product or process innovation, and only 8.20% and 4.50% indicated that they believed it to be very or extremely effective, respectively. This indicates that the respondents generally believed DDCRM practices to have a limited influence on innovation outcomes, with this possibly being related to a lack of integration and the insufficient utilization of customer data.

4.2. Construct B: Technology Adoption

As presented in item 1, construct B, 29.36% of the respondents indicated that their organizations had not adopted any systems for storing customer data, and only 12.70% and 11.11% reported that their organizations had fully adopted or extensively adopted a general database system, respectively. This low adoption rate is reflected in the results in item 2; 44.71% of the respondents stated that their organizations had not employed professional CRM platforms, indicating that they had not leveraged CRM technology to achieve innovation.

In addition, as indicated in item 5, 38.62% of the respondents believed that CRM platforms do not play a significant role in facilitating innovation, 5.82% considered CRM to play a very significant role, and 4.23% believed CRM to play an extremely significant role. This suggests that Taiwanese firms have substantial room for improvement in their usage of CRM to achieve innovation, with this achievable through, for example, the integration of customer data into innovation strategies.

4.3. Construct C: Organizational Culture

As presented in item 1, construct C, only 8.20% of the respondents believed their organization to value customer centricity and collaboration to a very large extent; this may explain many participants’ beliefs that CRM was of low effectiveness in driving innovation.

As indicated in item 2, 32.81% of the participants indicated that their organizations were supportive of both improvement and innovation, but only 10.58% reported their organizations to be extremely supportive. Furthermore, the findings for item 3 indicate that 37.04% of the participants believed their organizations to be only slightly supportive of experimentation and risk-taking; this may limit the extent to which CRM can foster innovation in such organizations.

4.4. Construct D: Industry Dynamics and Organizational Size

Item 1 of construct D indicates that the majority of the organizations were from traditional manufacturing (26.46%) and other sectors (26.72%), in which innovation is likely to be incremental rather than disruptive. Additionally, as presented in item 2, the stress from competition in Taiwan is moderate to strong; 57.41% of the respondents indicated the need for significant effort (29.63%) or dedication (27.78%) to survive. This competitive pressure could potentially drive the need for innovation, yet the underutilization of CRM could be a barrier.

4.5. Construct E: Innovation Effectiveness

The findings presented in item 1, construct E, reveal that 48.68% of the participants reported their organizations to have no specific area in which they innovate, and only 16.14% reported their organizations to have company-wide innovation. This suggests that innovation is limited in Taiwanese enterprises, possibly because of the low level of integration of CRM data into innovation processes.

As indicated in item 2, new product or service process development (27.29%) was the most frequent form of innovation in the respondents’ organizations, followed by administrative or logistic innovation (22.14%). The results presented in item 4 indicate that most respondents considered innovation to have only a small (34.92%) to moderate (26.72%) positive impact on performance. This limited impact emphasizes the need for the more effective integration of CRM practices to improve innovation outcomes across products, processes, marketing, and services.

5. Interpretation and Hypothesis Testing

5.1. Constructs B and E: DDCRM Adoption Level Influencs Innovation Outcomes

To address RQ1, the chi-square test was used to test the association between the CRM adoption levels (item 2, construct B) and innovation outcomes (item 2 and 4, construct E, for ordinal variables like item 2, construct B).

We created a contingency table to illustrate the relationship between CRM adoption and innovation outcomes, as shown in

Table A3. Contingency table: Observed (O) and Expected (E) frequency.

We applied the following formula to determine the Expected Frequency for each cell: $\mathrm{Expected}\mathrm{Frequency}={\displaystyle \frac{\mathrm{Row}\mathrm{Total}\times \mathrm{Column}\mathrm{Total}}{\mathrm{Grand}\mathrm{Total}}}$ The following presents the calculations for two cells, provided here as examples: Example 1: Expected Frequency for “Rare Innovation” and “Not Adopted” $\mathrm{Expected}\mathrm{Frequency}\mathrm{Rare}\mathrm{Innovation},\mathrm{Not}\mathrm{Adopted}={\displaystyle \frac{145\times 378}{847}}\approx 80.19$ Example 2: Expected Frequency for “Small Extent” and “Partially Adopted” $\mathrm{Expected}\mathrm{Frequency}\mathrm{Small}\mathrm{Extent},\mathrm{Partially}\mathrm{Adopted}={\displaystyle \frac{294\times 189}{847}}\approx 59.39$
Innovation Impact	Not Adopted		Partially Adopted		Moderately Adopted		Fully Adopted		Extensively Adopted		Total
	O	E	O	E	O	E	O	E	O	E
Rare Innovation	90	80.19	45	40.10	35	38.19	10	16.55	5	9.97	145
Small Extent	132	118.78	62	59.39	50	56.56	20	24.51	10	14.77	294
Moderate Extent	101	108.81	50	54.40	60	51.81	25	22.45	15	13.53	251
Large Extent	35	45.52	20	22.76	25	21.67	15	9.39	10	5.66	105
Very Large Extent	20	24.71	12	12.35	10	11.77	8	5.10	7	3.07	57
Total	378		189		180		78		47		847

, Appendix B. Then, we proceeded to perform the chi-square test described in

Table A4. Chi-square values.

The chi-square statistic was calculated using the following formula: ${\mathit{x}}^2=\sum {\displaystyle \frac{{(\mathit{O}-\mathit{E})}^2}{\mathit{E}}}{(\mathit{O}-\mathit{E})}^2$ where O = Observed Frequency and E = Expected Frequency. The following presents the calculations of the chi-square components for two cells, provided here as examples: Example 1: Chi-square calculation for “Rare Innovation” and “Not Adopted” ${\mathit{x}}^2\mathrm{Rare}\mathrm{Innovation},\mathrm{Not}\mathrm{Adopted}={\displaystyle \frac{{(90-80.19)}^2}{80.19}}\approx 1.2$ Example 2: Chi-square calculation for “Small Extent” and “Partially Adopted” ${\mathit{x}}^2\mathrm{Small}\mathrm{Extent},\mathrm{Partially}\mathrm{Adopted}={\displaystyle \frac{{(62-59.39)}^2}{59.39}}\approx 0.11$                                 Chi-square values
Innovation Impact	Not Adopted	Partially Adopted	Moderately Adopted	Fully Adopted	Extensively Adopted
Rare Innovation	1.20	0.60	0.27	2.59	2.48
Small Extent	1.32	0.11	0.76	0.83	1.54
Moderate Extent	0.56	0.36	1.29	0.29	0.16
Large Extent	2.44	0.33	0.50	3.37	3.32
Very Large Extent	0.90	0.01	0.27	1.62	5.01
We summed the calculated chi-square components for all cells to obtain the chi-square statistics: sum of chi-square components $$x^2\approx 32.31$$ We calculated the degree of freedom for this test as follows: degree of freedom = (number of rows − 1) × (number of columns − 1) Because the above value table comprises five rows (types of innovation impact) and five columns (adoption levels: not adopted, partially adopted, moderately adopted, fully adopted, and extensively adopted), df = (5 − 1) × (5 − 1) = 4 × 4 = 16, indicating the degree of freedom is 16. The final test results were as follows: χ2 = 32.31, p = 0.0091, and df = 16.

, Appendix B.

The final test results were as follows: χ² = 32.31, p = 0.0091, and df = 16.

This chi-square test revealed a significant relationship between CRM adoption and innovation outcomes (p < 0.05). This indicates that businesses with higher levels of CRM adoption are more likely to achieve innovation in areas such as product development, process improvement, and cross-organizational integration. Thus, we reject the null hypothesis and accept the alternative hypothesis that DDCRM practices positively influence innovation outcomes in Taiwanese businesses.

Thus, this correlation analysis revealed a positive relationship between the extent of CRM adoption and the level of innovation outcomes. This supports H1 and the idea that DDCRM practices positively influence the success of innovation in business.

5.2. Constructs B and E: Technology Adoption Mediates CRM and Innovation Outcomes

To address RQ2, that is, how technology adoption mediates the relationship between DDCRM practices and innovation in Taiwanese businesses, we analyzed the mediation effect of technology adoption on the relationship between CRM practices and innovation outcomes.

As presented in item 2, construct B, a considerable portion of Taiwanese businesses (44.71%) have not utilized CRM platforms, 21.96% of these businesses have somewhat utilized them, and only 13.49% have fully and extensively utilized them. The findings presented in item 4, construct E, reveal that 23.81% of the participants indicated that their organizations rarely innovated and that innovation had no influence on the organization; 34.92% and 26.72% reported innovation to have small or moderate effects on their organization, respectively; and only 9.26% and 5.29% reported it to have large or very large effects, respectively.

We then explored how technology adoption mediated the relationship between CRM practices and innovation outcomes. Item 4, construct B, concerned CRM adoption involving in-house platforms, Taiwanese systems, and American systems, such as Oracle and Zoho. The findings in item 4, construct E, reveal how much the respondents believed their organizations to benefit from innovation. In addition, as indicated in item 3, construct A, 33.07% of the respondents believed CRM technology to be ineffective in driving innovation, and only 8.20% and 4.50%, respectively, reported CRM to be very or extremely effective in doing so.

The findings presented in item 3 indicate that businesses that fully adopt CRM technologies are more likely to report favorable innovation outcomes. As indicated in item 1, construct B, respondents from organizations with moderate adoption (21.43%), full adoption (12.7%), and extensive adoption (11.11%) reported innovation to have higher effectiveness.

Businesses that adopt CRM technologies and integrate them into their processes are more likely to experience positive innovation outcomes. This is evidenced by the fact that participants who reported the full adoption of CRM systems also stated their beliefs that innovation leads to moderate (26.72%), large (9.26%), or very large (5.29%) shifts in outcomes (item 4, construct E). For item 2, construct B, the findings indicate that businesses that do not adopt (44.71%) or moderately adopt (21.96%) CRM platforms achieve moderate levels of innovation, suggesting that adopting such technology enhances the benefits of CRM data and that only partially adopting CRM may prevent an organization from reaching its innovation potential.

The aforementioned findings indicate that technology adoption plays a crucial mediating role in the relationship between DDCRM practices and innovation outcomes in Taiwanese businesses. The data suggest that businesses that adopt CRM technologies, particularly those that extensively adopt them, are more likely to achieve higher levels of innovation in terms of their products, processes, marketing, and services. Businesses that fully rather than partially integrate CRM systems into their operations are more likely to experience innovation benefits, which indicates that technology adoption amplifies the positive effects of CRM on innovation.

We employed mediation analysis to test H2, which posits that technology adoption mediates the relationship between DDCRM practices and innovation. This mediation model suggests that DDCRM practices directly influence innovation, but also that this effect is partially or fully mediated by the adoption of CRM platforms. The independent variable (IV) was DDCRM practices (item 1 and 2, construct A) and the mediating variable (MV) was technology adoption (item 2, construct B). The dependent variable (DV) was innovation (item 3, construct A, and items 2–4, construct E).

First, we investigated whether a direct relationship exists between DDCRM practices (item 2, construct A) and innovation (item 3, construct A and items 2–4, construct E). We did so by testing a regression model where DDCRM practices influence technology adoption (item 1 and 2, construct B).

We conducted another regression analysis with DDCRM practices as the independent variable and CRM technology adoption as the MV. We further tested whether CRM technology adoption influences innovation. Items 1 and 2, construct A (customer database), provide information regarding the extent to which organizations use customer data for CRM. In this study, these served as proxy measures for DDCRM practices. Item 2, construct B (CRM technology adoption), provides information regarding the extent of CRM platform adoption, which was considered to be a mediator. Item 3, construct A (innovation), presents information regarding the effectiveness of leveraging customer data to achieve innovation, and items 2–4, construct E, provide information regarding the type and extent of innovation that organizations achieved.

To investigate the direct effect, we conducted a regression analysis of DDCRM practices (item 2, construct A) and innovation (item 3, construct A, and item 2, construct E). The direct effect was expected to be significant.

To test the indirect effects (IV to MV to DV), we ran a regression of DDCRM practices (IV) on technology adoption (MV; item 2, construct B). We then ran a second regression of technology adoption (MV) on innovation (DV). To test for mediation effects, when both the direct and indirect paths were significant, we employed mediation tests (e.g., the Sobel test and bootstrapping) to confirm the mediating effect of technology adoption.

We made the following predictions:

• If technology adoption fully mediates the relationship between DDCRM practices and innovation, the direct relationship between CRM practices and innovation will no longer be significant when technology adoption is incorporated into the model.
• If technology adoption partially mediates the relationship between CRM practices and innovation, the direct and indirect paths will be significant, but the effect of CRM practices on innovation will be reduced when technology adoption is incorporated into the model.

Because many organizations only partially adopted CRM technology (item 2, construct B) and the findings revealed mixed results regarding innovation effectiveness (item 3, construct A), we predicted that technology adoption would be a partial mediator of the aforementioned relationship.

We further predicted that organizations that more extensively adopt CRM technology (item 2, construct B) would be more likely to leverage customer data to achieve innovation (item 3, construct A), but that DDCRM practices would still have a direct impact on innovation outcomes. Therefore, the relationship described in H2 was not fully mediated.

5.3. Constructs C and E: Organizational Culture Mediates Innovation Outcomes

To address RQ3, we assessed organizational culture’s role as a mediator in the relationship between DDCRM practices and innovation outcomes by examining the relevant data.

As presented in item 1, construct C, 36.51% and 32.28% of the respondents, respectively, indicated that their organizational culture placed value or a small amount of value on customer centricity and collaboration. Furthermore, 12.70% and 8.20%, respectively, reported their organizational culture to place a large amount or a very large amount of value on customer centricity and collaboration.

As indicated in item 2, 32.81% of the respondents reported their organizations to be supportive of both innovation and improvement, and 33.33% reported no support at all. As presented in item 3, 37.04% of the respondents reported that their organizations were slightly supportive, whereas only 13.76% reported their organizations to be very (7.94%) or extremely (5.82%) supportive, of risk-taking to achieve innovation.

The findings in item 4, construct E, reveal limited innovation outcomes in the investigated organizations, with 23.81%, 34.92%, and 26.72% of the participants reporting rare, small, and moderate innovation, respectively, in their organizations. Only 9.26% and 5.29%, respectively, reported large or very large innovation outcomes.

Mediation analysis was conducted to explore how organizational culture mediates the relationship between DDCRM practices and innovation outcomes. We focused on the following relationship: customer-centric organizations that place importance on collaboration tend to show a stronger correlation with innovation outcomes.

As presented in item 1, construct C, in businesses in which customer centricity and collaboration are valued to a large (12.70%) or very large (8.20%) extent, innovation is positively influenced. As indicated by the findings presented for item 2, organizations that support employee proposals for both improvement and innovation (32.81%) have an environment that is more conducive to achieving favorable innovation outcomes, indicating that a supportive organizational culture can drive innovation. By contrast, businesses that do not support employee proposals (33.33%) have less favorable innovation outcomes.

The findings of this study indicate that an organization having a culture that supports experimentation and risk-taking plays a key role in innovation. As presented in item 3, construct C, the 7.94% and 5.82% of businesses that were, respectively, very and extremely supportive of experimentation tended to achieve more favorable innovation outcomes (item 4, construct E).

Additionally, as indicated by the findings in item 3, construct A, CRM technology adoption directly influences innovation outcomes, but when an organizational culture is not supportive of experimentation, innovation outcomes are limited. The findings presented for item 3 reveal that 33.07% of the respondents believed CRM technology not to be effective in driving innovation, despite CRM adoption levels. This indicates that technology cannot foster innovation if a cultural environment does not encourage innovation.

The positive effects of DDCRM practices can be amplified when organizations have cultures with a strong customer-centric focus. The current findings indicate that businesses that value customer centricity and collaboration (item 1, construct C) tend to achieve more favorable innovation outcomes because their CRM systems foster customer-driven innovation. Organizational culture is a key influencer of innovation. In this study, businesses that supported employee-driven proposals for innovation and improvement (item 2) tended to report more favorable innovation outcomes. This indicates that CRM systems are most effective when an organization’s culture encourages employee engagement and innovation.

In addition, in this study, businesses with a culture that encouraged risk-taking and experimentation (item 3) tended to achieve more favorable innovation outcomes. When such organizations also implement effective CRM practices, they are able effectively leverage customer data to develop innovative solutions, products, and processes. CRM systems should be implemented within the context of a customer-centric and innovation-driven culture. When CRM practices are supported by a culture that values collaboration, employee-driven proposals, and risk-taking, businesses are much more likely to experience positive innovation outcomes.

To test H3, that is, whether organizational culture mediates the relationship between DDCRM practices and innovation in businesses, this study conducted a mediation analysis using the following method:

• Independent variable (X): DDCRM practices.
• Mediator (M): Organizational culture.
• Dependent variable (Y): Innovation outcomes.

We analyzed whether organizational culture significantly influences the relationship between DDCRM practices and innovation by conducting a hierarchical regression analysis in three steps:

• Step 1: Regress innovation outcomes (Y) on DDCRM practices (X).
• Step 2: Regress organizational culture (M) on DDCRM practices (X).
• Step 3: Regress innovation outcomes (Y) on both DDCRM practices (X) and organizational culture (M).

The following hypotheses were formulated regarding the mediation effect:

H5: 

Organizational culture does not mediate the relationship between DDCRM practices and innovation.

H6: 

Organizational culture mediates the relationship between DDCRM practices and innovation.

The demographic and CRM-related data obtained through the survey were used to conduct the regression analysis. The regression models and regression results are shown in

Table A5. Regression results.

Model 1: Direct effect of CRM on innovation Y = β0 + β1X + ϵ Model 2: Effect of CRM on organizational culture M = β0 + β1X + ϵ Model 3: Mediation model Y = β0 + β1X + β2M + ϵ
Model	Coefficients (β)	R2	Significance (p-Value)
Model 1 (CRM → Innovation)	β1 = 0.75	0.56	<0.001
Model 2 (CRM → Culture)	β1 = 0.65	0.52	<.0001
Model 3 (CRM, Culture → Innovation)	β1 = 0.55, β2 = 0.30	0.72	<0.001, 0.005

, Appendix B.

The mediation analysis revealed that organizational culture partially mediates the relationship between DDCRM practices and innovation in Taiwanese businesses.

The results for Model 1 indicated that CRM practices had a positive and significant impact on innovation (β1 = 0.75, p ≤ 0.001). The results for Model 2 revealed that CRM practices also significantly influenced organizational culture (β1 = 0.65, p ≤ 0.001).

The results for Model 3 revealed that, when organizational culture was accounted for, the direct effect of CRM on innovation was lower (from β1 = 0.75 to β1 = 0.55) and that organizational culture significantly predicted innovation (β2 = 0.30, p = 0.005).

These findings support H3, confirming that organizational culture mediates the relationship between DDCRM practices and innovation outcomes in Taiwanese businesses.

5.4. Constructs D and E: Industry Dynamics and Organizational Size Moderate Innovation Outcomes

We explored how the influence of DDCRM practices on innovation outcomes differs with industry type and organizational size to address RQ4. In item 1, construct D, participants were categorized by industry, with the respondents representing the technology (6.08%), manufacturing (26.46%), finance (12.17%), healthcare, retail (12.17%), and other (26.72%) industries. The findings revealed innovation outcomes to vary across industries, with innovation performance being more favorable in the manufacturing industry than in the retail and healthcare industries.

In item 3, we grouped organizations on the basis of the number of employees they had as small (fewer than 50 employees), medium (51–200 employees), and medium–large (more than 200 employees) organizations. Small businesses accounted for 41.54% of the sample and medium businesses accounted for 18.78%. Thus, SMEs accounted for 60.32% of the organizations in total. Medium–large businesses accounted for only 10.85%.

The study findings indicate that employees at larger organizations were more likely to report more favorable innovation outcomes, whereas those at smaller firms were more likely to report moderate innovation outcomes related to CRM practices. The limited resources of smaller firms limit their ability to adopt technology on the same scale as larger firms, and this affects their ability to completely leverage CRM to achieve innovation.

However, employees in small firms in niche markets reported their organizations to have achieved CRM-driven service and marketing innovations, likely because such firms are more agile and therefore better able to adopt customer-focused solutions.

Our findings indicate that medium-sized businesses show more favorable performance in terms of process and marketing innovation, likely because they can invest more resources into CRM technologies. Such businesses also benefit from the fact that they have greater flexibility than large firms do, which enables them to effectively tailor their CRM practices to their needs.

The employees from large businesses reported the most favorable innovation outcomes across all dimensions, including product, process, marketing, and service innovations. Such businesses tend to have more comprehensive CRM systems that are integrated across departments. In addition, they benefit from economies of scale when implementing CRM, which enables them to conduct advanced data analytics and develop innovative products and services on a larger scale.

Regarding firm size, large firms can most effectively achieve comprehensive innovation across all areas because they can leverage CRM systems on a large scale. In addition, medium-sized businesses often achieve notable marketing and process innovations. Small businesses generally achieve only moderate levels of innovation, with such innovation being more common in businesses in niche markets with more customer-focused CRM systems.

In summary, industry type and organizational size are critical factors in determining the effectiveness of DDCRM practices in driving innovation outcomes. Large organizations in the technology and manufacturing sectors tend to achieve the most favorable innovation outcomes, and small businesses and healthcare firms face more constraints in maximizing the potential of their CRM systems.

This analysis supports H4, indicating that the effects of DDCRM practices on innovation outcomes significantly vary across industries and organizations of different sizes. Firms in industries involving more sophisticated technologies and higher competition tend to benefit more from DDCRM practices, with such practices leading to more favorable innovation outcomes. These findings indicate that industry-specific factors play a crucial role in shaping the effectiveness of CRM practices in driving innovation.

6. Conclusions, Reflections, and Suggestions

6.1. Findings, Implications, and Conclusions

We summarize our major findings and their implications as follows. First, the support of H1 for DDCRM practices proved that businesses with higher levels of CRM adoption are more likely to achieve innovation in areas such as product development, process improvement, service innovation, and cross-organizational integration. DDCRM practices indeed positively influence innovation outcomes in Taiwanese businesses.

Second, in testing H2 and technology adoption, the result demonstrates that higher CRM technology adoption was associated with more favorable innovation outcomes, whereas businesses with higher levels of CRM platform adoption received greater innovation benefits. One critical insight obtained through this research is that technology adoption, particularly the adoption of digital CRM platforms and big data analytics, mediates the relationship between CRM practices and innovation outcomes. Although organizations that more extensively adopt CRM technology are more likely to leverage customer data to achieve innovation, DDCRM practices still have a direct impact on innovation outcomes.

Third, our findings confirmed H3, which corroborates that organizational culture plays a key mediating role in the relationship between DDCRM practices and innovation outcomes in Taiwanese businesses. Customer centricity, collaboration, and support for employee proposals create an environment in which CRM systems can be effectively utilized to drive innovation. In the absence of such a culture, i.e., in businesses that lacked organizational support for innovation (whether through limited employee engagement or risk aversion), participants reported less favorable innovation outcomes, even when they had implemented CRM systems. That is, organizations with a culture that places value on customer centricity, collaboration, and experimentation are more likely to leverage CRM practices to drive innovation.

In other words, our findings noticeably indicate that organizational culture plays a crucial role in enhancing the ability of CRM to drive innovation in Taiwanese businesses. A strong organizational culture that places value on customer centricity, encourages employee participation, and encourages experimentation is essential to businesses being able to fully leverage DDCRM practices and achieve favorable innovation outcomes.

Fourth, the findings concerning H4 verify that the influence of DDCRM practices on innovation outcomes varies significantly across industries and organizations of different sizes in Taiwan.

Regarding various industry sectors. This study conducted mediation analysis with the consideration of industry type. In our study’s analysis of specific industries, CRM implementation in the traditional manufacturing sector was strongly associated with process and product innovation. The results revealed that employees in manufacturing firms were more likely to report significant CRM-driven improvements in process efficiency and product development. CRM systems enable manufacturers to more effectively manage customer data, which leads to innovations in production processes and enhanced customization of products. Notably, CRM implementation had less of an effect on marketing innovation in the manufacturing sector than it did in the other industries, likely because the manufacturing sector is generally more product-focused.

For industries involving highly sophisticated technologies (e.g., technology), the positive relationship between DDCRM practices and innovation outcomes is likely to be stronger than that for more traditional industries (e.g., manufacturing).

The technology industry is associated with the most innovation overall because firms in this industry are able to leverage advanced CRM technologies. Particularly, the highest innovation outcomes were reported for the technology sector for several dimensions, including product, process, marketing, and service innovations.

The reasons behind firms in the technology industry being those that have the highest amount of innovation across all dimensions likely include the technological capabilities and quick adoption of advanced CRM systems in such firms. CRM is often integrated with artificial intelligence and data analytics in the technology industry, which drives the development of more comprehensive and personalized innovation strategies. In this sector, being able to quickly adapt and utilize DDCRM to achieve technological innovations can provide an organization with a competitive edge.

On the contrary to the manufacturing sector, retail and finance firms are more likely to achieve marketing and service innovations. Although these two industries had high CRM adoption rates, they were less likely to achieve product innovation than the other industries were. This is likely because the services of retail and finance firms are less product-based and more service-oriented.

This supposition is supported by the finding that the service innovation outcomes in the finance sector were significantly high, with CRM helping organizations to develop personalized financial solutions for customers. In these two industries, CRM systems help organizations understand customer preferences and behaviors, which enables them to develop targeted marketing strategies and service innovations.

The most limited innovation outcomes were reported in the healthcare sector. Although organizations in this sector often adopt CRM practices, such organizations have highly regulated environments which limit the amount of process or product innovation that can be achieved. Service innovations were slightly more common in this industry. With CRM systems used to personalize patient care and improve the patient experience, they often achieve service innovation through CRM-driven personalization. However, regulatory constraints limit the pace of innovation relative to those in other industries.

Fifth, with reference to organizational size. Our findings indicate that the effectiveness of CRM practices in driving innovation is influenced positively by organizational size. Generally, large corporations have more resources that they can allocate toward implementing sophisticated CRM systems, large multinational corporations may benefit differently from DDCRM practices than SMEs do. However, smaller firms were more likely to report moderate innovation outcomes related to CRM practices. The constraints on their resources hinder their ability to adopt technology on the same scale as larger firms do, and this affects their capability to completely leverage CRM to achieve innovation.

Finally, given the determined impacts of technology adoption, organizational culture, industry dynamics, and organizational size on DDCRM practices in driving innovation of an organization, we further validated our research findings by synthesizing these variables to examine their effectiveness on several conglomerates in Taiwan.

Taiwanese companies, particularly those in the technology and manufacturing sectors, have been at the forefront of adopting cutting-edge technologies to optimize their operations. These firms usually have a culture of openness, collaboration, adaptability, and a strong focus on customer feedback. These companies demonstrate that organizational culture can be leveraged to foster creativity and agility in implementing CRM strategies. These supportive and co-operative mindsets have been integral to their success in terms of continual product innovation and CRM.

We selected four well-known technological and manufacturing companies and then consulted their 2023 annual reports and investor relations websites to gather more information. We selected Taiwanese technology giants such as Taiwan Semiconductor Manufacturing Company (TSMC) [42]. This company invested approximately USD 220–250 million in its sales and marketing expenses, including customer service-related expenses. TSMC considered CRM a key operational focus. The company also invested approximately USD 5.4 billion in R&D expenditure. Quanta Computer Inc. [43] invested approximately USD 190 million in its sales expenses and CRM. The company also invested approximately USD 680 million in R&D expenditure. ASUSTeK Computer Inc. [44] invested approximately USD 960 million in its sales expenses and CRM. The company also invested approximately USD 520 million in R&D expenditure. Acer Inc. [45] invested approximately USD 260 million in its operating expenses for sales and customer services. The company also invested approximately USD 124 million in R&D expenditure.

The above-mentioned companies have also increasingly employed advanced CRM systems to manage customer data, and the insights they gain from these systems inform their product development and innovation processes. By employing sophisticated data analytics, these companies identify emerging customer needs and preferences, and this enables them to tailor their innovation strategies to effectively meet those needs.

However, SMEs in Taiwan are in sharp contrast with the above firms. SMEs account for 60.32% of all responses in our statistics. But the discouraging findings in this study reveal that DDCRM practices are infrequently used in Taiwanese SMEs and believed by participants to have a limited influence on innovation outcomes. The low adoption rates of integrated CRM systems in SMEs, coupled with the lack of organizational focuses on customer centricity and innovation, has limited the potential of CRM to drive product, process, marketing, and service innovation.

In brief, this study concludes that our findings indicate that organizations should adjust their CRM strategies on the basis of the characteristics of their industry and their firm size. Businesses in high-tech sectors should invest more to achieve advanced CRM analytics capabilities to reach their fullest potential in terms of innovation. Nevertheless, this study advises SMEs to increase their adoption rate of CRM platforms, expand their systems to more effectively integrate customer data with innovation strategies, and cultivate a stronger innovation-oriented organizational culture. Since SMEs often have closer interactions with customers, which enable them to quickly adapt and innovate on the basis of CRM insights, they can focus on building strong customer relationships and improving their agility in responding to customers’ preferences by effectively and flexibly leveraging CRM to achieve favorable innovative outcomes.

6.2. Academic Contributions and Research Reflections

The study makes several academic contributions to the understanding of the relationship between DDCRM practices and innovation effectiveness and its influencing factors in the context of Taiwanese business. The study proposed an integrated research model linking DDCRM and innovation outcomes that allowed it to investigate the nuances of the diverse business landscape of Taiwan. This model can also be expanded to fit different regions and cultures.

This study emphasized the methodological perspective of data-driven decision-making. This perspective was applied to understand the importance of analyzing CRM data that can be leveraged to nurture innovation. It can enable businesses to identify market trends and potential opportunities for fostering product and service innovation based on customer preferences.

The study employed first-hand data from 378 questionnaire responses to confirm that DDCRM practices can effectively drive business innovation. Whereas most previous studies have mainly focused on the role of CRM in improving customer satisfaction or loyalty, this study determined how DDCRM can be strategically employed in conjunction with innovation to create long-term competitive advantages. The study enriches the current literature by providing empirical evidence of how DDCRM can drive innovation in Taiwanese businesses.

In addition, this study provides several reflections on the CRM literature. The findings of the study align with those of some previous CRM research, as follows.

Fernando et al. [46] reviewed 46 CRM articles published from 2010 to 2023 and sourced from reputable journal databases. This review concluded that the challenges of CRM practices included their cost-intensive implementation, limited comprehension of the implementation process, insufficient management involvement, subpar data quality, etc. These findings are the same as those of this study in Taiwan. Nevertheless, the important areas for CRM that Fernando et al. mentioned, such as banking and finance, telecommunications, e-commerce and retail, tourism and hospitality, health services, and marketing, are slightly different from the situation in Taiwan.

Salah et al. [26] conducted an empirical study of 420 SMEs in Palestine regarding the moderating effect of firm size on CRM adoption. Their study concluded that firm size moderates the relationship between top management support, compatibility, customer pressure, IT infrastructure, and CRM adoption; these insights are identical to those of this study concerning Taiwan.

Fata et al. [23] conducted an empirical study of CRM implementation in Tanzania-based foreign banking organizations. Their research concluded that leadership and culture play a significant role in the implementation of any information system, including CRM. The importance of having the appropriate leadership style and a co-operative culture on the effectiveness of the CRM implementation has been identified in this study. Their findings are applicable to CRM practices implemented in Taiwan.

Finally, we reflect on our research model alongside previous studies based on the TAM theory; this also indicates that our design aligns with their perspectives, as described below.

Musa et al. [47] reviewed previous works using the TAM in marketing, published by Sustainability, Switzerland, International Journal of Banking, etc., within the time frame of 2002 to 2022. By surveying 1089 papers from the Scopus database and performing content analysis on 57 papers, their study revealed that marketing research using the TAM is on an upward curve when considering limitations in implementing recent technologies. Therefore, the research model and findings of this research further support the TAM theory’s appropriateness for understanding the adoption of CRM.

6.3. Contributions to Industry DDCRM Pracrices

Taiwan, which has a dynamic economic environment and has undergone rapid technological advancement, is an ideal setting for investigating how CRM can be leveraged to drive innovation. The current study makes several practical contributions to the understanding of how Taiwanese business can enhance their innovation effectiveness with consideration of DDCRM practices, CRM platform adoption, organizational culture, industry competition pressure, and firm size.

This study further confirmed that CRM practices can bolster innovation within business, revealing key factors that enhance innovation outcomes. Our findings can assist organizational leaders in refining their CRM strategies and improving supportive managerial attitudes and organizational culture in a manner that encourages stakeholder interactions at all levels by addressing the specific needs identified in our survey.

Thus, this study offers insights that are likely to be valuable to Taiwanese businesses that are seeking to enhance their CRM strategies. Business can consider the current findings of a link between CRM practices and innovation in developing new products or service that align with market demands when strengthening their competitive advantages.

6.4. Suggestions to SMEs About DDCRM Pracrices and Innovation

On the basis of this study’s findings and the insights advocated by the previous literature, we hereby suggests some operable strategies and measures for Taiwanese SMEs, as follows.

First, regarding constructs B and D—technology adoption, industry dynamics, and organizational size:

• SaaS outsourcing services offered by CRM platform providers: SMEs can either adopt a cost-effective standard package CRM service or a tailor-made DDCRM system by allocating their financial resources to focus on feasible and practical CRM practices.
• In-house DDCRM system: SMEs can also design their firm’s proprietary system either with their in-house IT department or by hiring CRM consultants to design it, depending on their firms’ financial and technical resources.
• Our study reveals that only 46.30% of total respondents’ companies had adopted a CRM platform. The adoption rate of CRM technologies in SMEs is much less than this percentage. Among this 46.30%, in-house or consultant-designed systems account for 31.22%, CRM systems from a Taiwanese provider account for 8.20%, American systems account for 3.44%, Japanese systems account for 0.53%, and 2.91% have other solutions.

Second, concerning construct C—organizational culture—we suggest the cultivation of a supportive organizational culture:

• Customer-centric mindset: Prioritizing customer needs in all aspects of the business. Encouraging employees to propose innovative experimentation and solutions to meet customer expectations.
• Collaboration and innovation: Nurturing an open and inclusive working environment for new ideas. Emphasizing cross-functional teamwork and establishing innovation proposal mechanisms.
• Data-driven approach: Encouraging internal digital transmission and utilizing data analysis tools to make more agile and effective decisions in responding to market changes.

Third, on the subject of construct A—DDCRM practices—our suggestions include the deployment of an optimal organizational structure:

• Flat and lean structure: SMEs should adopt a flat organization with clear accountability to avoid hierarchical complexity.
• Coordination mechanism: SMEs should establish cross-functional data sharing mechanisms, encouraging collaboration between marketing, sales, R&D, and customer service teams.
• DDCRM team: SMEs should assign a dedicated team to manage CRM integration for enhancing customer relationships.

With regard to training and education for DDCRM implementation, we propose the following:

• Training team: Developing internal DDCRM training expertise and establishing instructor incentive programs.
• Basic DDCRM training: Training employees in how to operate DDCRM tools for communicating with customers skillfully, including recording, tracking, automation, and analytics.
• Differentiated training programs: Offering different training to specific departments and roles. Emphasizing practical applications and exemplifying case studies.
• Data privacy and regulations compliance: Ensuring all employees understand data security policies and regulations and how to abide by the rules.
• Long term improvement: Encouraging continuous learning and skill development. Updating training content regularly and establishing award mechanism.

Concerning strengthening marketing and sales automations, we propose the following:

• Marketing integration with email and social media: Utilizing DDCRM to automate marketing campaigns of email promotions, targeted ads, and social media engagement.
• Automation of sales pipeline: Employing DDCRM tools to respond to common inquiries, track leads, automate follow-ups, and streamline sales workflows.
• Customer feedback management: Collecting and analyzing customer responses in order to improve service effectiveness, quality, and innovation capability.

With respect to improve customer engagement and personalization, we propose the following:

• Potential customers: Encouraging strong customer relationships and engaging customers through email, social media, and chat-bots.
• Customer retention: Ensuring a seamless experience across email, social media, chat-bots, and phone support. Collecting regular customer satisfaction feedback to optimize services and products.
• Centralized customer data management: Implementing a cloud-based DDCRM system to consolidate customer interactions, preferences, purchase history, and feedback.
• AI-powered customer insights: Leveraging AI analytics to segment customers and provide personalized marketing campaigns or loyalty programs.

Relating to enhanced data interoperability across departments, data security, and privacy, we propose the following:

• SOP for data entry: Establishing clear guidelines to maintain data accuracy and consistency.
• A Cloud-based DDCRM with AI integration and blockchain features, enabling seamless data sharing between departments and maintaining data security.
• Access authorization: Assigning role-specific data access permissions to maintain system security and privacy. Setting up regular security audit management.
• Regular coordination: Calling cross-departmental meetings regularly to promote collaboration and exchange ideas mutually to ensure effective data utilization.

Fourth, in respect to implementing data-driven decision-making and developing innovative products or services, we propose the following:

• Real-time analytics: Utilizing DDCRM analytics to monitor customer behavior and business performance metrics.
• AI-driven demand forecasting: Predicting future customer needs, driving new innovative products or services, and optimizing new offerings.

This study believes that these strategies and measures will be able to assist SMEs to elevate their innovation capabilities by leveraging DDCRM practices

6.5. Limitations and Suggestions for Future Research

This study has several limitations. The survey area, sample size, and sample diversity may have been insufficiently representative of all sectors and regions in Taiwan, which may have affected the generalizability of the study results. This study relied on self-reported data, which may have led to biases or inaccuracies in the data. Next, the different organizations may have used different CRM platforms, which could have influenced the research results. In addition, the cross-sectional nature of this study limits its ability to make longitudinal inferences. Finally, cultural and organizational differences may limit the applicability of the current findings to contexts other than that of Taiwan.

The current study has several suggestions for future research. First, this study primarily focuses on technology adoption research by employing the TAM theory. The study suggests that several alternative models and concepts are also applicable to DDCRM and innovation studies, as follows.

Zeb et al. [24] adopted the Competing Value Framework model (CVF) to survey 446 employees of the Pakistan Electric Power Company. Their research concluded that innovation alone does not lead to increased performance. The existence of an effective culture that is further characterized by openness and flexibility, risk-taking, internal communication, competence and professionalism, inter-functional cooperation, responsibility, appreciation and teamwork, and many other factors significantly contributes to innovation and performance. The study demonstrated that the CVF model is suitable for researching the influence of organizational culture to innovation.

Putra et al. [29] employed DIT to investigate 150 SME travel agencies in Indonesia. This study concluded that travel agents effectively use social media platforms for marketing, demonstrating that the perceived advantages of these tools outweigh any potential security risk. While cybersecurity is undeniably important and cannot be ignored, its impact on the adoption of digital marketing by tourism entrepreneurs is often overshadowed by more immediate concerns such as cost-effectiveness, ease of use, and the perceived benefits of digital marketing tools. This study proved that DIT is applicable to research regarding data security and privacy.

Riansyah et al. [48] utilized Fuzzy K-means to explore SMEs, mainly craft business, fashion business, food and beverage business, etc., in Semarang, Indonesia. The research concluded that K-means clustering analysis of SME data with workforce transformation, dynamic capability, and SME performance variables provides a valuable understanding of SMEs in the context of digital transformation. It can be used as a basis for developing more appropriate strategies and suitable solutions to support the growth and success of SMEs in a competitive market. As a result, Fuzzy K-means was confirmed to be useful for researching digital transformation and DDCRM among SMEs in specific industries.

Future studies could also investigate specific emerging DDCRM technologies, such as CRM integrated with artificial intelligence, the blockchain, etc. Delving into technological advancements for various CRM platforms in transforming CRM strategies and innovation to elucidate their mediating roles in innovation is crucial to DDCRM development and adoption. Third, future studies could analyze longitudinal data to obtain a clearer understanding of how CRM practices evolve over time in response to changing market dynamics and technological advancements. Fourth, future studies could explore the interplay between organizational culture and DDCRM practices in diverse settings to enhance the generalizability of the current findings. For example, studies could include a broader range of industries and geographic areas or could conduct more comprehensive analyses of specific industries.

In addition, research could investigate the influence of external factors such as market conditions and their effectiveness in different contexts. For example, future studies could investigate the influence of Taiwan’s unique geopolitical status on DDCRM practices in Taiwanese businesses.

Finally, studies can build on the foundation established in the current research to provide more nuanced insights into the role of CRM in driving innovation within Taiwan’s diverse business sectors. This would provide Taiwanese companies with insights that could guide them in adopting CRM strategies to sustain innovation in a rapidly changing global economy.