Research on Enterprise Digital Marketing Strategy Based on Big Data and Business Performance: Mediating Role of Customer Relationship Management
Yueqi Li1, Chen Yuan 2*
1College of Business,Quzhou University,Quzhou，Zhejiang,324000,China; 40083@qzc.edu.cn
2School of Finance,Nankai University,Tianjin,300350,China; 1120211042@mail.nankai.edu.cn
Statements and Declarations
Consent to participate:
Informed consent to participate in the research was obtained from all participants in written form.
Consent for publication:
Not applicable. This study does not include any data from individual persons, such as images or videos, requiring consent for publication.
Declaration of conflicting interest:
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Data availability:
The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. Data sharing is subject to ethical and legal restrictions to protect participant confidentiality.
Author Note:
Correspondence concerning this article should be addressed to
Chen Yuan 2*
Email: 1120211042@mail.nankai.edu.cn
Abstract
In the rapidly growing and competitive global market landscape, only those businesses can survive which adapt the latest technological (e.g., big data) tools. Today’s world is going through the digital marketing transformation. Enterprise digital marketing and big data work closely to reach their targeted market segments. Aligning the business strategy with big data digital marketing is the key of today’s successful enterprises in China. Keeping this view, the current study explored the importance of enterprise digital marketing strategy for business performance based on big data. The study sample was represented by 390 experts from the various Chinese enterprises. Study design was cross-sectional and the instruments were adopted from the existing research, and standardized questionnaire was formed to collect the survey data. To empirically validate the model, statistical data was analyzed by using Smart PLS and SPSS. Enterprise digital marketing strategy and big data were studied with business performance through mediation of customer relationship management (CRM). Results showed that enterprise digital marketing strategy, big data and CRM have significantly positive impact on business performance. Similarly, customer relationship management mediates the relationship between enterprise digital marketing and business performance. Contrary to this, customer relationship management has no impact between the relationship of big data analytics competency (BDAC) and business performance. The research findings proved that enterprise digital marketing based on the big data is the need of the time and essential for the survival of the enterprises in China. Furthermore, the findings are aligned with the previous researches that has revealed the fact that higher big data analytics competencies result in higher firm performance.
Keywords: Enterprise Digital Marketing Strategy; Big Data; Customer Relationship Management; Business Performance.
Introduction
Nowadays due to rapid advances in technology and an ever-expanding range of digital applications, China has entered the big data era. In light of these recent developments, the marketing strategy employed by businesses across all sectors has experienced major changes. The enterprises are one of China’s fastest growing traditional industries (Jun, Yoo, & Choi, 2018). The impact of the digital marketing approach on the traditional enterprises has been un-precedent. Traditional marketing ideas have gradually lost their competitive advantage. If we do not modify the ideas that we use for marketing and continue to implement old marketing techniques, then the local business will not be able to maintain its competitive advantage in the market competition. Thus, in order to establish the basic capabilities necessary for core competitiveness, it is vital to examine the specific marketing of businesses (Parkin, 2016).
So, the rise of digital marketing has changed the way of the world’s economy works and given consumers more power. Competition among businesses has become more competitive as a result of the expansion of digital technology (Dastane, 2020). The use of digital technologies (e.g., big data) has resulted in significant changes to the communication between corporations, audiences, and a variety of other organizations (Suleiman, Muhammad, Yahaya, Adamu, & Sabo, 2020). The term “digital technology” refers to the utilization of new technology for the purpose of achieving marketing goals (Ghobakhloo,2020). In a similar manner, the use of digital technology requires an entirely new range of knowledge and skill. When working in an atmosphere that is dominated by digital technology, it is difficult for marketers to successfully apply traditional marketing strategies (Biletska, Paladieva, Avchinnikova, & Kazak, 2021). The rapid growth of digital marketing is occurring in tandem with the general expansion of digital technology, such as smartphones, intelligent products, the internet of things, and artificial intelligence (AI) (Jianjun et al., 2021; Berg, Burg, Gombovi, & Puri, 2020). This has a significant impact on the performance of businesses, and it is also helping to reshape the future marketing strategy (Buttle & Maklan, 2019). Smartphones offer additional benefits to users in the form of instant access to services, in comparison to the traditional services offered by the organization (Algharabat, Rana, Alalwan, Baabdullah, & Gupta, 2020). The expansion of digital technology has made it easier for customers to gain access to services whenever and wherever they want by logging in to the website of the company, which features a background filled with information about the services offered and graphs (Su, Lin, & Wang, 2022).
The expansion of digital technology has made it possible for businesses to cut their spending on traditional forms of marketing through the integration of social media, which presents a possibility for cost savings. However, it can be difficult for marketers to select the optimal combination of digital marketing strategies to increase brand recognition and successfully achieve the appropriate level of business performance through the collection of 296 different opinions from customers continuously (Tamrakar, Pyo, & Gruca, 2018). Furthermore, digital marketing can encourage a diverse variety of clients to establish an effective relationship with the company via digital media in order to direct them in the procurement process (Kurdi, Alshurideh, Akour,Alzoubi, Obeidat, & Alhamad,2022). Sharing ideas, perspectives, and experiences of customers through digital marketing helps to establish the value of the brand in an effective manner, which ultimately results in improved business performance and a better relationship with customer’s enterprises are becoming extremely advanced and efficient, which has led to the development of customer relationship management (CRM) (Wai, Dastane, Johari, & Ismail, 2019).
CRM was developed to help businesses raise their sales by better serving their customers and adapting to their needs in a timely manner (Naim, Muniasamy, & Alqahtani, 2022). The conventional method of customer relationship management (CRM) is currently being blended into modern technology through the use of digital marketing in areas such as communications, advertising, and customer support in order to win the loyalty and happiness of customers (Bist, Agarwal, Aini, & Khofifah, (2022). The use of CRM provides more opportunities for customers to mine data, have a better knowledge of strategic marketing information, and access to that information, all of which lead to a decrease in costs caused by improper marketing methods (Wong et al., 2019).
Big data analytics has been emerged as an advanced IT capability and businesses are yet moving towards adoption of its practices; this area therefore, offers many unexplored domains. Taking the organization’ resources and dimensions into consideration, researchers and practitioners are highly interested in analyzing the role of various elements in development of big data analytics competency (Yu,Tao, Hanan, Ong, T. Latif, & Ali, 2022). The current research has attempted to address this problem to some level by incorporating the numbers of possible data-specific resource dimensions in BDAC to analyze its impact on firm performance. Furthermore, past research has revealed the fact that higher big data analytics competency results in higher firm performance (Tongtong, & Xinhang,2022). This makes up for the problem that decision making performance either improved or deteriorate, poses influence on a relationship between BDAC and firm performance. In addition, as BDA is driver of innovation, so along with CRM and digital marketing also offers a major part in making an impression upon business performance (van Biljon, 2022). However, owing to mentioned facts, there exists a need to investigate the mediating relationship of CRM and digital marketing in relationship between BDA competency and firm performance which is still uncharted. Assuredly, there are other factors too which can pose a strong impact on this relationship; however, current study as of now is interested in exploring the mediating role of CRM (Verma, & Kumar, 2022).
The potential of the enterprises has opened the door for digital marketing tactics to contribute to the enhancement of business performance and to the formation of strong relationships with both current and potential consumers. As a result, the primary purpose of this research is to investigate the effect that enterprise digital marketing strategy has on the performance of businesses by focusing on the function that customer relationship management plays as a mediator (Nuseir, & Refae, 2022). In addition, the current study offers a full summary of enterprise digital marketing strategy as it relates to the effects it has on the business performance of organizations that are inspired by CRM in the enterprises. In addition, the concept of customer relationship management (CRM) acting as a mediator between BDA competency; digital marketing and business performance (Alwan, & Alshurideh, 2022). However, very few studies have been conducted on the impact that digital marketing and CRM had on the intention to buy. Although this study primarily focuses on the business enterprises in China to evaluate the mediating effect of CRM between BDA competency, digital marketing capabilities and the development of business performance in order to fill a research gap. According to the researcher’s knowledge, there has not yet been a study that used the same research model paradigm. In addition, regulators and policymakers will find this study valuable in protecting the interests of customers and fostering growth in the enterprises from a business viewpoint in this increasingly digital environment (Ferrari, 2022).
Literature Review and Hypotheses Development
The present study investigates the area of big data analytics competency, its underlying resource dimensions and its contribution in firm performance with mediating role of customer relationship management. Extensive literature has been reviewed in the selected domain to analyze the already present studies and identify the gap. In addition, this section provides the conceptual understanding of research framework with hypothesis generation for the current study (Jaber, & Abbad,2021). Organizations develop their competitive advantage through their effort of integration and deployment of firm resources thus building their capabilities. Since the last two decades, big data analytics competency has been distinguished as one such IT competency of firm which makes a strong impact on its performance (Jaber, & Abbad,2021).
Business performance has remained a focused factor of investigation in business research since forever by researchers and practitioners in different context and settings (Miller et al., 2013). It is acknowledged as a multidimensional concept of comparison among organizations on which one performs higher than its competitor (Christie,2022). Furthermore, Business is referred as an organization with value maximization as its core objective through exploitation of its resources. Value maximization however, is derived by all such strategic decisions which can soar the market value of a firm in longer run (Wood, Williams, Nagarajan, & Sacks, 2021). In addition, Rappaport (2016) claimed value maximization as a strategic challenge in business environment and to compete successfully, an organization must have to be ahead of its competitors at seizing opportunities and developing potential competitive advantage which will enable it to create value and enhance firm performance (Petit, & Teece, 2021). In today’s hyper competitive business world, firms are in severe competition with each other for their success and survival (Kamkankaew, Phattarowas, Khumwongpin, Limpiaongkhanan, & Sribenjachot, 2022).
Performance measurement system being a key part of development of organization strategy allows firms to evaluate their achievement level in terms of their defined organizational objectives (Hristov, Appolloni, Chirico, & Cheng, 2021). Business performance is mostly determined based upon its financial performance by evaluating factors like efficiency or return on investment (ROI), return on equity (ROE) and profit scales such as return on sales (ROS), net profit margin, turnover etc (Owolabi, 2022). However, financial measure only are not enough to assess firm performance owing to its limitation, as a claim has been made that merely accounting driven financial measurements are sometimes not sufficient to evaluate firm performance (Thuy, Khuong, Canh, & Liem, 2021).
The current study aims at investing the effect of big data analytics competency on business performance and the underlying theory of the study is Resource Based Theory (RBT). RBT is one of the mostly used theories in organizational settings which describes, elaborates and anticipates organizational relationships and believes that competition among organizations develop based on resources which are rare in nature, hard to imitate, valuable when exploit and are properly organized (Oesterreich, Anton, Teuteberg, & Dwivedi, 2022).
In addition, RBT not only determines the strategic value of firm resources but also highlights the clear dependency of firm performance on its resources. (Garcia-Buendia, Moyano-Fuentes, Maqueira, & Avella, 2022). However, over time, the said paradox resolved owing to several research studies conducted to realize that along with IT investment, numbers of other resources are needed to be focused upon in order to drive the true value through IT investment. It was that time when IT was considered as a winning weapon, while in the present digital world big data analytics has emerged as one of the competitive weapons which can earn competitive advantage to firms (Schäfer, Pesch, Manik, Gollenstede, Lin, Beck, & Timme, 2022). Big data analytics competency incorporates a number of multiple other competencies (resource domains) in it including human resources, managerial resources and IT resources, which require proper investment to produce its true value in form of improved Business performance. In light of the growing significance of big data analytics, a growing number of academics and industry practitioners have investigated the question of whether and under what circumstances big data analytics may boost the value of an organization by means of a competitive advantage (Oesterreich, Anton, Teuteberg, & Dwivedi,2022).
According to the research conducted by Côrte-Real, Ruivo, and Oliveira 2020, just 27% of the companies that used big data analytics were successful in achieving the desired outcomes. In addition, a significant number of companies are still in the early stages of learning and comprehending how to generate value through BDA and what kinds of resources and talents are necessary to derive the greatest possible benefits from this strategy (Jha, Agi, & Ngai, 2020). As a result of this and taking into mind the facts that have already been presented as well as the growing significance of BDA and its relationship with Business performance.
CRM emphasis on awareness, learning, and market transformation, digital marketing functions as a kind of inbound cycle. This is accomplished by analyzing the wants and actions of customers through individualized feedback and evaluations (Gupta, 2019). In addition, customer support may be provided more quickly, and related conversations can take place more frequently, on digital marketing and CRM personalization (Anshari, Almunawar, Lim, & Al-Mudimigh,2019). Tracking digital marketing, obtaining consumer feedback, and customizing marketing plans are three ways to foster stronger ties with existing and potential customers (Ramesh & Vidhya, 2019).
The advent of digital technology has fundamentally changed the norms of marketing, and as a result, many traditional marketing strategies are now considered to be ineffective (Kingsnorth, 2019). According to Pieiro-Otero and Martnez-Rolán (2016), the advent of the internet precipitated the rise of digital marketing as an essential component for many businesses that aspire to be successful in the competitive business sector. According to Wibisurya (2018), digital marketing has a beneficial influence on purchase intention, with a considerable impact on the attractiveness of content, personalization, and customization for the customer. Therefore, based on the findings of the investigations, the working hypothesis is that Cost-driven and revenue-based measurements are related to business performance (Casalino, Żuchowski, Labrinos, Munoz Nieto, & Martín, 2019). The majority of managers and investors believed that profit growth was crucial, despite the fact that it was rarely utilized as a measurement of successful marketing (Mintz, Gilbride, Lenk, & Currim, 2021).
Customer relationship management has the potential to considerably contribute to the enhancement of business performance. When a client is satisfied with the service they receive, they are more likely to come back for additional purchases, which ultimately helps the company’s bottom line (Chatterjee, Ghosh, & Chaudhuri, 2020). Wibisurya (2018) has shown that there is a deficiency in the amount of advertising that is done for customized items in order to attract the attention of customers and encourage them to buy new products. The newer customers an organization can bring in by catering to the specific needs of those customers, the more opportunities there will be for the business to succeed.
CRM is a factor that develops better CRM skills in a company and is dependent on the quality of the IT system outcomes produced by such an organization. CRM is an acronym for customer relationship management. Therefore, as the analytical quality of the system gets better, the CRM capabilities of the organization will continue to expand. The success of customer relationship management (CRM) depends on the method through which a system gathers the daily data of market-related actions. It also relates to the manner in which these data are processed to provide information on new customers and individualized information on specific consumers in order to deliver BCS to them and keep them as members of the organization (Pekkanen, 2022). Therefore, the present research suggests that CRM plays a mediating function between the relationships of BDA and BP in organizations. On the basis of the previous studies, the following hypotheses are constructed (Cao, Nie, Sun, & Taghizadeh-Hesary, 2021).
Digital marketing has a huge impact on the success of businesses and has needed a never-ending revolution in marketing methods in order to develop solid relationships with clients and customers (McGruer,2020). In addition, Ahmed and Zahid (2014) observed a considerable impact after demonstrating the mediating role that CRM plays between digital marketing and the purchase intention of customers. This assessment of Ahmed and Zahid was also corroborated by (Yunus, Saputra, & Muhammad, 2022). On the other hand, Karjaluoto and Ulkuniemi (2015) concluded that digital marketing does not have a substantial impact on CRM. They reasoned that this was because excessive digital platforms give customers the impression that they are being underserved, which in turn influences whether they intend to make a purchase. However, there are not enough studies that have been done to examine the connection between CRM, business performance, and digital marketing (Mehralian, & Khazaee, 2022). This mediating impact has not yet been investigated regarding the capabilities of enterprise digital marketing strategy and the enhancement of business performance. Considering the available literature, the following hypothesis can be formulated:
Research Hypotheses
H1 BDAC has a positive impact on business performance
H2 BDAC has a positive impact on Digital Marketing Strategy
H3 BDAC has a positive impact on Customer Relationship Management
H4 Digital Marketing Strategy has a positive impact on Customer Relationship Management
H5 Digital Marketing Strategy has a positive impact on business performance
H6 Customer Relationship Management has a positive impact on business performance
H7 Customer Relationship Management mediates the relationship between BDAC and business performance.
H8 Customer Relationship Management mediates the relationship between Digital Marketing Strategy and business performance.
Fig 1. Theoretical framework
Research Methodology
Research methodology is an important part of any study that defines and explains the research’s “why” and “how” aspects. Current research is based on a quantitative study that adheres to the collection of data through questionnaires and used a cross sectional study design because of the epidemic or COVID-19 situation which badly harms the world. Sampling techniques help in selecting the right respondents for the research. In this study the population was the 390 experts of various enterprises in China. Primary data has been collected through questionnaire-based survey method. The purpose of using primary data is that new variables are used and their scales are adapted from different researchers. In this study,
Table1: Measurement of different variables and items.
| Variables | Items | Sources |
| Digital Marketing | 6 | (Dastane, 2020) |
| Customer Relationship Management | 5 | (Dastane, 2020) |
| business performance | 6 | Ravichandran, Lertwongsatien, & Lertwongsatien, (2005). |
| Big Data Analytics Competency | 6 | Gupta, & George, (2016) |
The 6 items scale developed by (Gupta, & George, 2016) was used to determine big data analytics competency. The 6 and 5 items scale developed by (Dastane, 2020) was used to determine digital marketing and Customer Relationship Management respectively. The 6 items scale developed by (Ravichandran, Lertwongsatien, & Lertwongsatien, (2005) was used in order to determine CRM. The 7-point Likert scale was used to rate all variables that ranges from1- strongly disagree to 5- strongly disagree.
Results
The data was analyzed using smart PLS- 3 and several tests were conducted. The structural model results, measurement model results, alpha, reliabilities, AVE, and composite reliability. Results of hypothesis testing direct effect, and mediating effect tests, to find the supportive and non-supportive evidence for the hypothesis.
Measurement Model
The measurement model was accepted to confirm the model reliability and validity in the model evaluation.
Figure 2: Measurement model
Construct reliability and validity
Outer Loading
The item loadings serve as an indication of the degree to which an item is related with the latent variable that the item is supposed to calculate. In this way, the item loadings demonstrate the level of reliability associated with the item. (Ramayah, Cheah, Chuah, Ting, & Memon, 2016; Hair Jr et al., 2016). Loading results show that the outer loadings of items were within an acceptable range of 0.40 to 0.70. Table 2.
Table 2: Outer Loadings
|
Cronbach’s Alpha, CR, and AVE
A most commonly used measure to determine the convergent reliability is Average Variance Extracted (AVE). The estimation of AVE has performed by calculating the mean of squared factor loadings for all indicators linked with the concerned construct. The analysis shows that the average variance extracted (AVE) of variables are: BDAC= 0.700, BP =0.818, CRM =0.805, DM = 0.697. All values of different variables AVE are above 0.50.
In reliability, the values of composite reliability should be checked and their values should be more than 0.70. Moreover, different variables loadings are shown in Table 3 BDAC=0.923; BP=0.956; CRM=0.941; DM=0.930. Results of CR and AVE demonstrate that the CR and AVE were in acceptable range
Table 3: Average Variance Extracted (AVE)
| Variables | Cronbach’s alpha | Composite reliability (rho_a) | Average variance extracted (AVE) |
| BDAC | 0.914 | 0.923 | 0.700 |
| BP | 0.955 | 0.956 | 0.818 |
| CRM | 0.939 | 0.941 | 0.805 |
| DM | 0.912 | 0.930 | 0.697 |
Discriminant validity
Two criteria were used to find out discriminant validity. First is Fornell & Larker and second is Hetrotrait-Monotrait Ratio Criterion.
HTMT
HTMT is an estimate for the factor correlation. For the establishment of discriminant validity, the HTMT is used and value of HTMT below than 1 is satisfactory (Henseler et al., 2015). Similarly, Table 4 shows the Hetrotrait-Monotrait Ratio (HTMT) discriminant validity.
Table 4: Hetrotrait-monotrait (HTMT) ratio of correlations
| BDAC | BP | CRM | DM | |
| BDAC | ||||
| BP | 0.444 | |||
| CRM | 0.319 | 0.905 | ||
| DM | 0.508 | 0.693 | 0.606 |
Fornell-Larcker criterion
Fornell-Larcker criterion is another successive method for the estimation of discriminant validity as this measure helps in making the comparison between the square root of AVE values and correlation of latent variables (Fornell and Larcker, 1981).
Table 5: Correlation matrix Fornell-Larcker criterion
| BDAC | BP | CRM | DM | |
| BDAC | 0.837 | |||
| BP | 0.421 | 0.904 | ||
| CRM | 0.303 | 0.868 | 0.897 |
Structural model was assessed after the acceptability of measurement model.
Assessing the Structural Model (Inner Model) for Hypotheses Testing
The structure model in PLS provides the relationship between the study’s constructs. Using t-values and path coefficients to display construct relationships.
Direct Relationship
Table 6 expresses the results of hypothesis testing. According to proposed hypothesis, the results show the value of T-statics is greater than 2 which is 8.856 and p value is 0.00 so it has found that BDAC has insignificant relationship with BP. The BDAC effect on CRM and DM was accepted and the results show that T- statistics is 6.081 and 10.116, p value is 0.00 and. The DM impact on BP and CRM was supported. The value shows that T-statistics is 13.496 and 10.568, p-value is 0.00. Similarly, the impact of CRM on BP also was supported. Their values show that T-statistics is 26.670, p- value is 0.00. Hence, this study fulfilled state of a strong connection between all variables. So, all direct Hypothesis are accepted.
Table 6: Direct Relationship
| Original sample (O) | Sample mean (M) | Standard deviation ) | T statistics | P values | |
| BDAC -> BP | 0.421 | 0.422 | 0.048 | 8.856 | 0.000 |
| BDAC -> CRM | 0.303 | 0.305 | 0.050 | 6.081 | 0.000 |
| BDAC -> DM | 0.490 | 0.493 | 0.048 | 10.116 | 0.000 |
| CRM -> BP | 0.725 | 0.723 | 0.027 | 26.670 | 0.000 |
| DM -> BP | 0.598 | 0.598 | 0.044 | 13.496 | 0.000 |
| DM -> CRM | 0.556 | 0.557 | 0.053 | 10.568 | 0.000 |
Mediation analysis
In mediation Table 7, BDAC relationship was tested with BP and DM with mediation of CRM. To determining confidence intervals and t-values, the bootstrapping approach was applied to the mediation analysis with 5000 subsamples. In mediation the results show that H8 is rejected because their t-value is less than 2 which is 0.682 while H7 is accepted because their values are greater than 2 which are 11.797 and 3.06. So, the table 4.7 justifies the hypothesis which tells that in the presence of CRM the relationship between DM and BP will be strong.
| Original sample (O) | Sample mean (M) | Standard deviation (STDEV) | T statistics (|O/STDEV|) | P values | |
| DM -> CRM -> BP | 0.403 | 0.402 | 0.034 | 11.797 | 0.000 |
| BDAC -> CRM -> BP | 0.022 | 0.022 | 0.033 | 0.682 | 0.495 |
The expansion of digital technologies has resulted in the creation of a new opportunity to cut expenses associated with marketing in comparison to traditional marketing through the integration of social media. However, marketers are experiencing new hurdles in determining the optimum mix of digital marketing to establish brand recognition and successfully generate the needed business performance through consistent input from customers. The smart PLS-SEM analysis was utilized in order to conduct an empirical test of the proposed study model. In addition, the reliability, validity, and predictive power of the model were evaluated with the help of the PLS-SEM algorithm, the bootstrapping method. According to the findings of the research, the results show that research model greatly satisfies the necessary requirements of reliability and validity as well as predictive power.
In addition, the findings of the study support the premise that big data and enterprise digital marketing strategy (i.e., the utilization of social media, online advertisement, and content marketing) has a major influence on the improvement of business performance as well as the management of customer relationships. In addition, customer relationship management serves as a crucial mediator between big data; digital marketing and the improvement of business performance. The emphasis that CRM places on the awareness, learning, and transformation of the consumer is reinforced by digital marketing, which is a type of the inbound marketing cycle. Digital marketing does this by analyzing customer behavior and requirements with personalized feedback and reviews.
Tracking digital marketing, obtaining consumer feedback and reviews, and customizing marketing techniques are three ways to foster stronger ties with existing and potential customers. It is postulated that effective management of customer relationships can considerably contribute to improvements in business performance, which is related to both cost-driven measurements and revenue-based measures. When a client is happy with the service they receive, they are more likely to come back for additional purchases, which ultimately helps the company’s bottom line. The potential customers an organization can bring in by catering to the specific needs of those customers, the more opportunities there will be for the business to succeed. As a result, the findings of this study lead us to the conclusion that digital marketing has a sizeable effect on the performance of businesses and necessitates the continuous revision of marketing strategies in order to develop a client base that is capable of competing successfully. It has been suggested that by practicing BDA in businesses, enterprises can effectively counter the challenges of improve their CRM capabilities. In addition, in order to improve the performance, it is recommended to the managers that they expand the capabilities of CRM and make certain that BDA provides specific information regarding upgrading and retaining existing customers, creating new customers, and winning back lost ones.
Theoretical and Practical Implications
Integration of organizational resources which has been explored in this study to examine the big data analytics competency of a firm is not used in this combination before. Additionally, it is observed through previous studies that there is lack of literature available in domain of big data analytics and enterprise digital marketing strategy regarding Chinese organizational setting. The present study thus, has made its part in bridging this gap in literature. The current research is of immense significance as the already existed literature on big data analytics and enterprise digital marketing strategy in context of Chinese enterprises is excessively rare. China is a country where investment in IT related competencies is at initial stage. The current research is carried out with a target to analyze big data analytics competency precisely in contextual setting of China.
The current study offers a significant contribution in practical implementation of big data analytics as it firstly provides an integrated IT and managerial view of BDAC related facets which will help to focus on every possible aspect to improve this competency of firms. Secondly, the present study is of practical significance to improve firm performance as it focuses not on big data analytics competency and enterprise digital marketing strategy solely but also on CRM which provides extensive contribution in escalation of any firm performance. BDAC alone won’t boost company performance. Thus, academics and practitioners must focus on other factors to attain the ultimate goal of big data analytics, improved firm performance.
Research Limitations and Suggestions for Future Research
This study has several limitations, just like any other research. For starters, this report is solely applicable to Chinese businesses. Because of factors like as size or organizational culture, the results for other sectors in China or the same sector in other nations throughout the world may differ. Secondly, the sample size was limited to 390 respondents, and the study was limited to cross-sectional data analysis. Next, the current study has included only a few of the available factors to investigate an organization’s big data analytics; additional dimensions may have a significant impact as well. Furthermore, one mediator in the relationship between big data analytics ability and company performance was identified in this study. While there may be other elements that can moderate the given relationship, it was not possible to consider all of them. Finally, the current study did not investigate the effect of any moderator. It is just intended to depict the role of a mediator. While researchers can investigate the impact of BDAC on individual departments or areas such as supply chain management, etc.
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