Abstract
Introduction
Customer orientation refers to the extent to which organizations and their employees are committed to meeting customer needs and improving customer well-being (Kiffin-Petersen & Soutar, 2020; Zablah et al., 2012). Since the first conceptualization by Saxe and Weitz (1982), customer orientation has become a widespread topic in the fields of marketing, organizational behavior, and psychology, and been regarded as a key determinant of business success (Domi et al., 2020; Grissemann et al., 2013; Sa et al., 2020; Slater & Narver, 2000; Yang & Tsai, 2019; Zang et al., 2020). In practice, organizations have constantly improved their customer-centric beliefs of front-line employees, encouraged them to provide customer-oriented services, and made unremitting efforts to become a customer-oriented organization.
From a research point of view, a large volume of studies has devoted to understanding the performance implications of customer orientation. For instance, the positive effects of customer orientation on individual, team, and business performance are largely undisputed with empirically evidence supporting such effects (Grizzle et al., 2009; Herhausen et al., 2018; Miao & Wang, 2016; Pousa et al., 2018; Sa et al., 2020; Yang & Tsai, 2019; Zablah et al., 2012; Zang et al., 2020). Meanwhile, another stream of studies investigates how customer orientation has potential significant influences on employee attitudes and behaviors, such as employee job satisfaction (Menguc et al., 2016), commitment (Donavan et al., 2004), selling behavior (Shu et al., 2019), organizational citizenship behavior (Donavan et al., 2004), intention to leave (Babakus & Yavas, 2012), and exhaustion (Babakus et al., 2011). As customer is at the core of an organization’s customer orientation, studies have explored its impact on customer outcomes, such as customers’ attitude to product and salesperson (Homburg et al., 2011b), customer satisfaction and loyalty (Gerlach et al., 2016), customer repurchase intention (Kim & Ok, 2010), and customer perceived service quality (Gazzoli et al., 2013).
Although existing review papers or meta-analysis studies have summarized customer orientation or its correlated terminology, market orientation (see Franke & Park, 2006; Kirca et al., 2005; Zablah et al., 2012), these articles neither cover the multifaceted findings of customer orientation, nor include papers published in the past decade. Further, the emergence of many new customer-oriented research questions makes accurate positioning of customer-oriented future research directions as an important topic for scholars in this field to promote customer-oriented academic research. For instance, the global economic situation has been unpredictable, and business competition and the living environment have undergone subversive changes. Consequently, consumers’ consumption patterns have changed, which force organizations to adjust or change their business models. In this case, many new customer-oriented problems have been triggered. For example, to better adapt to new competitive environments and improve corporate performance, more and more companies organize their business activities in a team mode (Cummings, 2007). Compared with the individual work model, the customer-oriented activities in the team model are more complicated. For example, in a multi-member team, achieving a high-level customer-oriented team climate with highly consistent employees within the team is a very tough question. For now, very limited research has focused on the customer orientation research in the team context, such as the research of Menguc et al. (2016) and Herhausen et al. (2017). While, these team customer-oriented findings are far from satisfying the practical needs of more than 75% of companies currently switching from the traditional individual work model to the team model. The mechanism of team-level customer orientation is still unclear (Herhausen et al., 2017). For another example, in the era of rapid development of e-commerce and self-media platforms, efficiently achieving customer-oriented activities under the virtual network environment arouses increasing research questions. For another example, in the post-pandemic era, how companies can effectively implement customer-oriented strategies remains to be studied. Thus, the massive amount and complexity of the existing customer orientation literature along with the ever-evolving business and market environments has led to an increasing need for a thorough literature review.
Echoing this need, this paper presents a systematic review of customer orientation research via content analysis as well as bibliometric analysis, which is a critical tool of conducting longitudinal reviews of given research fields such as hospitality (e.g., Li et al., 2017), intangible cultural heritage (Su et al., 2019), and leadership (e.g., Zhu et al., 2019). This analysis allows researchers to quantitatively analyze the existing knowledge and thereby demonstrate the development of the research field (Denyer & Tranfield, 2006). Despite the popularity of the method, to the best of our knowledge, there is no bibliometric reviews in relation to customer orientation. The study claims originality on the following grounds: this study (1) provides a relatively comprehensive review of the customer orientation field, by analyzing studies published in the past two decades and sourced in Web-of-Science (WOS); and (2) visualizes the landscape and evolution of customer orientation research to identify its dynamics and frontiers.
The remainder of the paper introduces the data sources for the bibliometric analysis, and details the co-citation analysis, co-occurrence analysis, burst detection, and cluster analysis. This is followed by the presentation of key results of the abovementioned analyses, core research themes, content analysis results and future research directions.
Methodology
Data Sources
The searching, selection, and refinement of literature is a critical part of bibliometric analysis (Arora & Chakraborty, 2021). In accordance with other bibliometric review papers in organizational and marketing areas (e.g., Batistič et al., 2017; Rey-Martí et al., 2016; Zhu et al., 2019), we collected data from Web of Science (WOS) Core Collection: Citation Indexes. This database includes publications indexed by SCI-Expanded, SSCI, CPCI-S, and CPCI-SSH, and their bibliographic information which is needed in the subsequent analysis, such as information of authors, journals, and citation.
As the criteria used in collecting the data potentially influence the results, we made several efforts to reduce the contamination in order to precisely reflect the customer orientation literature. We previewed the customer orientation literature in order to check all relevant terms that have been used in the customer orientation area. Most of the prior research used customer orientation as the key term, or used its abbreviation “CO,” while others used customer-oriented behavior or customer-oriented attitude as key terms. Based on the above analysis, we limited our research to customer orientation and customer-oriented as the key searching items. The final bibliometric search method can be described as the following: title = (“customer orientation” or “customer-oriented”), or key words = (“customer orientation” or “customer-oriented”), or key words plus = (“customer orientation” or “customer-oriented”), language = English, document type = (“article” or “review”), and time span = (“2001–2020”). A time window of 20 years as this time window is chose which is an accepted timeframe in similar bibliometric analysis (Arora & Chakraborty, 2021), and allows to examine how citations and co-citations are built over time (Rey-Martí et al., 2016).
Our initial sample included 477 records with the abovementioned terms in their titles, 665 records the abovementioned terms in their keywords, 953 records with keywords plus as the field tag. About 1,709 documents were selected after reducing the duplications through CiteSpace. Figure 1 presents the numbers of annual customer orientation publications from 2001 to 2020. As shown in Figure 1, the number of studies on customer orientation has been steadily growing from 2001 to 2008, and there is a sharp increase from 2008 to 2011, and then fluctuates from time to time. The mean growth rate is 0.16 per year, with the highest growth rate of 0.55 between Year 2004 and Year 2005 and the lowest of −28.57% from Year 2016 to Year 2017. Nevertheless, the general upward trend and the overall increasing annual number of publications indicates the growing attention from scholars.
The annual number of publications from 2001 to 2020. The data for this article were downloaded on December 12, 2020.
Analytical Method and Tool
Bibliometric analysis and CiteSpace
Bibliometric analysis allows researchers to draw a clear picture of a specific research field in terms of its origin, current status and development, which helps researchers to better formulate revenues for future research (Chen et al., 2016; Cui et al., 2018). We used CiteSpace (5.7.R2), a powerful and popular bibliometric analysis tool (Chen, 2006), in this study. This Java-based visualization tool can carry out various visual knowledge analyses, such as co-citation analysis, co-occurrence analysis, burst detection, and timezone visualization (Chen, 2006; Zhu et al., 2019).
CiteSpace produces three key structural metrics: betweenness centrality, modularity, and silhouette (Chen, 2006). The betweenness centrality of a node ranges from 0 to 1. The node with high betweenness centrality indicates that the nodes have a strong ability to connect with other nodes. Therefore, this node acts as a landmark for the research field (Chen et al., 2010, 2014). The value of modularity and silhouette are indicators of cluster analysis. Modularity Q, often ranging between 0.3 and 0.7 (Newman & Girvan, 2004), represents the extent to which a network can be divided into different relative independent clusters with clear boundaries (Chen et al., 2014). If the value of Modularity Q is more than 0.7, it indicates a better clustering result. Silhouette (S) reflect the quality of the contour of a cluster (Rousseeuw, 1987). Generally, a value higher than 0.5 indicates reasonable clustering.
Co-citation analysis
Co-citation analysis is one of the main bibliometric analysis techniques, which is commonly used to reflect the frequency with which two specific items of prior research are cited in subsequent articles (Yang et al., 2019). For instance, when author A and author B are cited in the same publication, they have a co-citation link. The higher the co-citation frequency of any two authors is, the closer their academic relationship is. The frontier research problems in any field can be reflected by the articles actively cited by scholars. So, Chen (2006) suggested using co-citation analysis to monitor the landscape of a research field. Based on the co-citation relationships between primary documents, we can identify the intellectual structure of a research area over time. In this study, we used author co-citation analysis, journal co-citation analysis, and document co-citation analysis to identify the influential authors and journals, and key references in the customer orientation literature.
Co-occurrence analysis
Co-occurrence analysis is a type of content analysis method, which can detect the co-occurrence of information in various articles. This analysis allows researchers to identify research themes and the number of citations that each keyword receives (Foroudi et al., 2021). Information extracted from the title, abstract, author, and keywords of the articles can be used in the co-occurrence analysis. In this study, we did keywords co-occurrence analysis to show the relationships among different keywords that appear in the same article, which explicated the hot topics in customer orientation and the evolution of this research area over time.
Burst detection
Burst detection can be used to identify research frontiers using a burst detection algorithm proposed by Kleinberg (2002). This methodology can capture key issues (e.g., key terms) that are characterized by a sharp increase in the usage frequency. CiteSpace can support several types of burst analysis. One of the popular burst detections is to detect documents with strong co-citation bursts, which is normally conducted simultaneously with co-citation analysis. Another one is to detect keywords with a surge in usage, which can be conducted via the keyword co-occurrence analysis. In this study, we conducted both the keyword and document burst detection. Based on burstiness, we can detect the most active references and identify frontier work in the customer orientation literature.
Cluster analysis and timeline and timezone view
CiteSpace can also generate cluster analysis, which is regarded as the most commonly used technique in bibliometric analysis (Hair et al., 1998). Cluster analysis is normally used to analyze the similarity between relevant papers and emergent topics (van Eck & Waltman, 2017). For instance, a document co-citation cluster displays the nature of the clusters for cited documents and the interrelationship between these clusters. The names of clusters are automatically extracted from the information input in CiteSpace, such as the title and keywords (Zhu & Hua, 2017). This study adopted the log-likelihood rate (LLR) as it allows the generation of best results in terms of coverage and uniqueness (Chen, 2006).
We also performed a timeline analysis with CiteSpace to identify the origin, development and current status of customer orientation research. In the timeline view, the clusters are arranged on a horizontal timeline which clearly demonstrate the evolution of the clusters over time. This can help scholars to identify the emerging trends. The time zone view is used to manifest the temporal patterns of the results, which contain a series of vertical strips that are arranged chronologically from left to right (Chen, 2006).
Data Analysis
We first pre-ran our data in CiteSpace. As there were more than 500 nodes when 1-year slices were selected for analysis, betweenness centrality was 0. Therefore, we divided the timeframe into reasonable intervals to better analyze the results in this study (Pestana et al., 2019). In line with Zhu et al. (2019) and Pestana et al. (2019), we adopted 4-year time slots and divided the time span between 2001 and 2020 to five time slices: 2001–2004, 2005–2008, 2009–2012, 2013–2016, and 2017–2020. The pruning was set as “pathfinder” and “pruning the merged network.” Terms sources were title, abstract, author keywords (DE), and keywords plus (ID). Node type was set in terms of the analysis, such as author, keyword, cited journal, and etc. We selected the top 50 levels of most cited or occurred items in each time slice to analyze their intellectual structure and development. Table 1 presents CiteSpace metrics for the analysis of the network of customer orientation research, which will be discussed in the following sections.
CiteSpace Metrics by Node Type.
Results
Distribution of Journals and Cited Journal Analysis
The journal co-citation analysis can provide the distribution statistics of important knowledge sources in a certain field and help readers to answer the questions such as “which journals are cited in this research field” and “what is the relationship between these journals.” Table 2 list the most cited journals in customer orientation research, as well as the top 10 journals ranked by the number of publications. As shown in Table 2, five of the most cited journals in customer orientation research include both top-tier marketing journals (i.e.,
Top 10 Journals in Customer Orientation Research.
Figure 2 shows the networks of the co-cited journals. As a bigger circle indicates a higher citation frequency, the results show that articles published in the
Journal co-citation analysis.
Author and Author Co-Citation Analysis
The author co-citation analysis not only shows the distribution of highly cited authors and identify influential scholars in the customer orientation field, but also helps understand the distribution of similar authors’ research topics and discipline fields. The author co-citation network is shown in Figure 3. The results indicate that Fornell C., Podsakoff P.M., Narver J.C., Kohli A.K., and Bagozzi R.P. are the top five most influential authors in the network. It should be noted that Podsakoff’s work mainly focuses on the common method biases in behavioral research, which is usually discussed in the customer orientation literature. In addition, the top 10 authors with highest number of publications are Homburg C., Menguc B., Agnihotri R., Feng T.W., Chang C.S., Naude P., Rapp A., Brown T.J., and Ngo L.V. Their topics cover the impact of individual-level customer orientation on individual performance (e.g., Brown et al., 2002), customer outcomes (e.g., Homburg et al., 2011a,b), and organizational performance (e.g., Rapp et al., 2010); team customer orientation (e.g., Menguc & Boichuk, 2012; Menguc et al., 2016); service employee customer orientation (e.g., Donavan et al., 2004); customer orientation in B2B market (e.g., Da Silva et al., 2002); market orientation (e.g., Ngo & O’Cass, 2012); and the effectiveness of market orientation in new product development (e.g., Feng et al., 2012).
Author co-citation analysis.
Top Institutes in Customer Orientation Research
There are 267 institutions contributing to the customer orientation research. Table 3 list the top 10 institutions in the customer orientation research field. As shown in Table 3, Oklahoma State University ranked the top with 27 publications, followed by University of Alabama (25) and Hong Kong Polytech University (24). The top three institutions with high centrality are University Mannheim, Hong Kong Baptist University, and University of Texas, which show the importance and influence of these three universities in customer orientation research.
Contributing Institutions by Frequency and Centrality.
Popular Topics
The network of keywords (Table 1) clearly presents the interrelationships between representative keywords. The large number of linkages among the nodes in each time slices indicates the interconnected relationships among the keywords. As shown in Table 1, the Modularity Q (larger than 0.5) and mean Silhouette (larger than 0.5) scores for each time slices show good clustering profiles. The density is highest in the 2001 to 2004 time period, where there are a small group of central keywords. In the following three time periods, as the number of the keywords increases, the density of the network is decreases.
The keyword co-occurrence analysis results as presented in Figure 4. Keywords with high co-occurrence frequencies in each time slice represent major customer orientation research topics during that time period. The most popular research topics in customer orientation literature include performance, market orientation, impact, orientation, antecedent, satisfaction, model, and customer satisfaction. The size as well as the font size of each nodes (Figure 4) are good indicators of co-occurrence frequencies. In order to clearly demonstrate the popular topics, the connections and evolutions among the topics, we list the most popular topics from 2001 to 2020 in Table 4. It can be observed that there are key terms with low frequencies, but high betweenness centrality, in each time slice. The betweenness centrality can help identify the keywords that are strongly interconnected with other keywords (Pestana et al., 2019). If the betweenness centrality of a keyword is greater than 0.10, that key term can be deemed a heated topic (Chen, 2006). Thus, we list such key terms as popular topics in Table 4. From the betweenness centrality, articles published from 2001 to 2004 have the most amount of 27 keywords which have strong connection with other keywords. The keywords with high frequencies and centralities in the five time slices suggest that topics such as the performance implications of customer orientation, satisfaction and antecedents of customer orientation have been heated topics for a long duration. In the meantime, the impact of customer orientation on innovation and the moderators that influence the performance implications of customer orientation have become heated emergent topics.
Key Words Co-occurrence Analysis Results by Slices.
Keywords co-occurrence networks for customer orientation research from 2001 to 2020.
Additionally, we also detected 59 burst terms out of 555 keywords as indicators of emerging topics or trends (Chen et al., 2014). Figure 5 demonstrates the distribution of these major burst terms in different time periods. There are several important burst terms in the 2001 to 2004 time period, such as model, market orientation, and firm, followed by a few major bursts in the second time period, such as driven. Moderating role and mediating roles are the two very important emergent topics in the time period of 2009 to 2012. Work engagement, engagement, and co-creation are the main emergent topics from 2013 to 2016, whereas the current trending topics are corporate social responsibility and social media.
The distribution of the burst terms in different time periods.
Document Analysis in Customer Orientation
The networks of document co-citation are shown in Figure 6. The detailed information, summarized in Table 1, illustrates that the documents are centralized around a couple of top articles in the five time periods. The density decreases as years roll on, which is similar with the trend of density for keywords. The top five co-cited documents with high frequencies are Zablah et al. (2012) (frequencies: 104), Kirca et al. (2005) (frequencies: 68), Hair et al. (2010) (frequencies: 60), Podsakoff et al. (2012) (frequencies: 60), and Homburg et al. (2011) (frequencies: 59). Two of these papers provide a meta-analysis of customer orientation, whereas the other two focus on the research methodology, multiple regression and sources of method bias, which are usually used or discussed in customer orientation articles. The last one is about salesperson customer orientation in sales encounters. The top five influential co-cited documents with high centrality are Payne et al. (2008) (centrality: 0.29; topic: value co-creation), Cervera et al. (2001) (centrality: 0.26; topic: antecedents and consequence of market orientation), Brown et al. (2002) (centrality: 0.25; topic: customer orientation of service workers), Stock and Hoyer (2005) (centrality: 0.25; topic: an attitude-behavior model of salespeople customer orientation), and Im and Workman (2004) (centrality: 0.24; topic: market orientation and new product performance).
The network of document co-citation of customer orientation research.
We further clustered the networks by using Log-likelihood ratio (LLR) clustering algorithm to enable nuanced interpretation. The cluster names were extracted by choosing “T” (title) as the labeling source. Thus, the above co-citation networks were divided into 24 knowledge clusters. The Modularity Q (0.9024) and mean Silhouette (0.9667) scores indicate good cluster analysis results. For instance, cluster #0 is labeled as market orientation. This cluster contains 46 studies. The value of silhouette is 0.955, indicating a high consistency among these references. This cluster mainly focuses on market orientation at the organizational level, cultural versus operational market orientation, objective versus subjective performance, business performance, and channel relationship. Cluster #1 is labeled as organizational performance, which mainly focuses on customer orientation, organizational performance, customer-linking capabilities, CRM technology, and complementary role. Details information about the top 10 clusters are shown in Table 5.
Summary of the Largest 10 Clusters.
We also used burst detection in keyword analysis to check research frontiers and trends (Ye et al., 2020). As the citation burst indicates a surge of citation (Chen et al., 2014), it is viewed as a measure of how one publication attracts significant attention from other scholars in the same research field in a given period (Liu, Liu et al., 2019). The citation burst is also an indicator of heated topics and research trends over time (Ye et al., 2020; Zhu et al., 2019). In our sample data, the top item by citation burst is Kirca et al.’s (2005) meta-analysis paper with the burst value of 22.53, which focuses on the antecedents and impact of market orientation. It attracts huge attention since 2006. The next item by citation burst is Podsakoff et al.’s (2003) study on common method biases in behavioral research. The following two items are studies about service worker’s customer orientation by Brown et al. (2002) and Donavan et al. (2004), which manifest a very hot topic by scholars since 2006. Table 6 summarizes 16 references with high citation burst.
The Documents With Strong Citation Burst in Customer Orientation.
These references in customer orientations represents the bellwether and the good indicators of this research field. By doing a content analysis of all these documents, we summarize several key research areas of customer orientation.
(1) The antecedents of frontline employee customer orientation. The document burst detection and the content analysis indicates its importance in customer orientation research field. Half of the reference with strong citation burst discuss the mechanism of employee customer orientation (#2, #3, #6, #7, #9, #13, #14, #15 in Table 6). The employees include both salespersons (reference #7, #9, #14, #15) and service staff (reference #2, #3). For instance, the impacts of personal characteristics, for example, gender, personal traits, working experience, and adaptive selling behavior (reference #2, #7), on employee customer orientation.
(2) The various job-related and customer-related outcomes of frontline employee customer orientation. As mentioned in the above research area, most key documents in the customer orientation field focus on its outcomes. The detailed content analysis also indicates that it is a large branch in the customer orientation field. For example, the key documents explored the impacts of frontline employee customer orientation on employee job outcomes, such as job satisfaction (reference #2, #3, #6, #7), and customer outcomes, such as customer satisfaction, retention, and perceived service quality (reference #9, #13, #14, #15).
(3) The implication of customer orientation on performance (reference #1, #2, #8, #11, #12). Since its introduction, the implication of customer orientation on performance received a huge research attention. These studies investigate customer orientation’ impact on many performance indicators, such as organizational performance, individual employee performance, manager performance. Therefore, we propose that performance as one of the main knowledge structure of customer orientation.
(4) The discussion around market orientation strategies and climate (reference #1, #5, #8, #11, #12, #14). Treating customer orientation as one of the three dimensions of market orientation, some studies focus on organizational level customer orientation and their antecedents and consequences. It worth noting that the relationship between market orientation and new product performance is a very important stream of this part.
Discussion and Conclusion
Customer orientation has been widely explored in marketing, organizational behavior, psychology, and other research fields for nearly 40 years. Despite the popularity of the topic, there is no quantitative or visualized research to explicate how the customer orientation research landscape has evolved. To fill in this gap, our study used CiteSpace to present an overview and evolution of the research areas. Keywords co-occurrence analysis, burst detection, cluster analysis, time-zone visualization graphic, and content analysis were used to demonstrate the evolution, popular topics, and the new trends of customer orientation research. Our study finds that most customer orientation articles are published in top marketing journals such as Journal of Marketing and Journal of the Academy of Marketing Science. The most influential authors include Homburg C., Menguc B., Narver J. C., Brown T. J. Over 200 institutions contribute to this research field, among which Oklahoma State University has made the most significant contribution on the basis of the number of publications.
In addition, we identify four research avenues for future research. Table 7 shows the key research direction and exemplar research questions.
Directions for Future Research.
The first one is to investigate the new mediating and moderating factors on the linkage between customer orientation and performance when considering the new social and market environment. Although scholars have continually revisited the impact of customer orientation on various performance indicators, it is still a valuable research direction. This is because performance as a keyword emerged with a high frequency from beginning to end in our analysis. In the time slice of 2017 to 2020, sale performance has a relative higher centrality, which indicates a strong ability to connect with other keywords. Furthermore, performance is a main research purpose of the documents with strong citation burst. Further research should also focus on exploring the mediating and moderating impact of customer orientation on various performance indicators. Especially, the internal and external marketing environment has undergone a subversive and constant change (Iheanachor et al., 2021). Many new situational factors disturb or accelerate the implementation of customer orientation as well as its performance implications. Investigating the moderating factors between the linkage of customer orientation and performance is a valuable research direction.
The second avenue is to discuss the mechanism of customer orientation in the social media context. Our keyword burst detection results show that social media is the most important burst in the current time slice (2017–2020). Social media platforms offer a marketing-sales-service interface for organization (Enyinda et al., 2021). In the platforms, customers have a great chance to contribute to marketing activities. Although, several studies have discussed this topic (e.g., Mpandare & Li, 2020), there are still many new research questions. For instance, how can companies leverage customer orientation and social media platforms to build long-lasting customer relationships, how to effectively implement customer orientation strategy in live streaming commerce, and how customer orientation contributes to e-loyalty.
The third one is to discuss how corporate social responsibility influences the implementation of customer orientation. Corporate social responsibility is a new keyword burst in our analysis. Reviewing the customer orientation literature reveals that only few studies have discussed the relationship between corporate social responsibility and customer orientation (e.g., Hu et al., 2020). Corporate social responsibility is viewed as an organizational discretionary practice which is beneficial to social wellbeing (McWilliams & Siegel, 2001). However, previous studies have kept silence on the role of corporate social responsibility in shaping employee customer orientation (Hu et al., 2020), leading to a research gap to be filled in. Further research could unravel the relationship between corporate social responsibility and employee customer orientation.
Finally, research on the mechanism of customer orientation at the team level will be one of the most important stream of customer orientation literature. Although the team-level customer-oriented research is not highlighted in the visual literature analysis, the detailed content analysis shows that with more and more enterprises organizing marketing activities in the team mode, the research on team-level customer orientation will become an important research branch in this field. Meanwhile, the existing research results show the complexity of team-level customer orientation research. Therefore, it is an important content of future customer orientation research to deeply explore the mechanism of team customer orientation from the perspective of team-level customer orientation climate, team-level customer orientation consistency, and employees (mis)fit in customer orientation within a team.
This study provides a systematic understanding of the landscapes and evolution of customer orientation research. However, there are still some limitations that should be noted. First, we only used WOS for data collection, which inevitably omitted articles that are sourced in other databases. We recommend future research including other databases (e.g., Scopus) in article search and comparing their results to ours. Second, although a time window of 20 years is deemed a reasonable time frame for literature review (Rey-Martí et al., 2016), we recommend future research including articles published before 2001 to enrich the understanding of the research area. Third, despite the popularity of CiteSpace and the abovementioned bibliometric analysis methods, other types of bibliometric analysis and other visualization software should also be considered by future research at an attempt to provide a more comprehensive understanding of the customer orientation research domain.
Footnotes
Declaration of Conflicting Interests
Funding
ORCID iD
