Customer Orientation: A Literature Review Based on Bibliometric Analysis

Abstract

Rooted in the concept of market orientation, the literature has long affirmed the importance of customer orientation to business success. While a number of literature reviews on customer orientation exist, there is still a need for a thorough investigation into the current status of customer orientation research. Using the bibliometric method and CiteSpace visual analysis tool, this study analyzes and summarizes the customer orientation literature from 2001 to 2020. By using the co-occurrence analysis, co-citation analysis, burst detection, and time zone visualization, this paper provides an all-around intellectual structure of the customer orientation literature, for example, an overview of relevant publications, the distribution of journals, the authors’ and institutions’ contribution, the heated issues, core research theories, and future avenues of customer orientation research.

Keywords

customer orientation performance bibliometric analysis literature review CiteSpace

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.

Figure 1.

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.

Table 1.

CiteSpace Metrics by Node Type.

Network	Node type	Modularity Q	Mean Silhouette	Nodes	Link	Density	Numbers of clusters
Network	By year	Modularity Q	Mean Silhouette	Nodes	Link	Density	Numbers of clusters
Journal co-citation	Journal	0.8234	0.9584	219	567	0.0077
	2001–2004			156	312
	2005–2008			117	234
	2009–2012			100	200
	2013–2016			100	200
	2017–2020			102	204
Author co-citation	Author	0.8588	0.9701	207	725	0.0090
	2001–2004			110	220
	2005–2008			108	216
	2009–2012			102	204
	2013–2016			104	208
	2017–2020			100	200
Institution co-citation	Institution	0.8538	0.9477	267	411	0.0066
	2001–2004			43	22
	2005–2008			51	24
	2009–2012			85	78
	2013–2016			103	131
	2017–2020			111	161
Keywords co-occurrence	Keywords	0.8538	0.9477	555	2277	0.0052	21
	2001–2004	0.7031	1.0000	111	222	0.0364	9
	2005–2008	0.6574	1.0000	149	298	0.0270	11
	2009–2012	0.6887	1.0000	265	530	0.0152	11
	2013–2016	0.6708	1.0000	318	636	0.0126	13
	2017–2020	0.6645	1.0000	369	738	0.0109	13
Document co-citation	Documents	0.9024	0.9667	729	1756	0.0029	24
	2001–2004	0.6024	0.9976	103	206	0.0392	3
	2005–2008	0.5268	0.9976	123	246	0.0328	8
	2009–2012	0.7394	0.9980	207	414	0.0194	8
	2013–2016	0.7423	0.9980	213	426	0.0189	10
	2017–2020	0.7049	0.9959	238	476	0.0169	9

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., Journal of Marketing, Journal of Marketing Research, Journal of The Academy of Marketing Science) and management journals with a broader focus (i.e., Journal of Business Research and Academy of Management Journal). In terms of the number of publications, the top five productive publications include Industrial Marketing Management, Journal of Business Research, Journal of The Academy of Marketing Science, European Journal of Marketing, and Journal of Marketing. The total publications of the top 10 journals account for 35.4% of the total publications.

Table 2.

Top 10 Journals in Customer Orientation Research.

Top 10 journals by citation	Total citation	Centrality	Top 10 journals by publications	Total publications
Journal of Marketing	1,457	0.46	Industrial Marketing Management	132
Journal of Marketing Research	1,296	0.42	Journal of Business Research	90
Journal of The Academy of Marketing Science	1,240	0.23	Journal of Business Industrial Marketing	82
Journal of Business Research	1,171	0.26	Journal of The Academy of Marketing Science	62
Academy of Management Journal	864	0.30	European Journal of Marketing	54
Journal of Applied Psychology	855	0.16	International Journal of Hospitality Management	48
Academy of Manage Review	843	0.00	Journal of Marketing	38
Industrial Marketing Management	801	0.24	Service Industries Journal	38
Journal of Management	771	0.00	Journal of Services Marketing	33
European Journal of Marketing	762	0.05	International Journal of Contemporary Hospitality Management	29

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 of Marketing have the highest citation frequency (the total citation of 1,457). The total citation is 33,231, while the top 10 influential journals account for 30.3% of the total citation. The citation frequency ranking is generally consistent with the overall journal ranking by Association of Business Schools (ABS).

Figure 2.

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).

Figure 3.

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.

Table 3.

Contributing Institutions by Frequency and Centrality.

Institutions	Frequency	Institutions	Centrality
Oklahoma State University	27	University of Mannheim	0.43
University of Alabama	25	Hong Kong Baptist University	0.31
Hong Kong Polytech University	24	University of Texas	0.31
Michigan State University	19	University of Melbourne	0.29
University of New South Wales	19	University of Manchester	0.27
Georgia State University	19	University of Nottingham	0.27
Kyung Hee University	18	University of North Carolina	0.25
Aalto University	17	Shanghai University of Finance and Economics	0.25
University of Melbourne	16	Baylor University	0.24
National Cheng Kung University	16	Cornell University	0.22
Griffith University	16	Manchester Metropolitan University	0.22
		Queen Mary University London	0.22

Time Slice	Keywords	Count	Keywords	Centrality	Time Slice	Keywords	Count	Keywords	Centrality
2001–2020	customer orientation	588	competitor orientation	0.22	2001–2004	Performance	51	Learning orientation	0.37
performance	556	role conflict	0.18	Market orientation	50	Strategy	0.29
market orientation	516	job performance	0.16	Customer orientation	50	Customer satisfaction	0.26
impact	385	sales force	0.15	Antecedent	40	Relationship quality	0.24
orientation	329	CRM	0.15	Impact	38	Antecedent	0.23
antecedent	321	justice	0.14	Orientation	37	Construct	0.23
satisfaction	246	attribution	0.14	Model	34	Competition	0.23
model	245	role ambiguity	0.14	Satisfaction	34	Salespeople	0.22
customer satisfaction	228	business	0.13	Customer satisfaction	31	Sales force	0.22
innovation	207	fit	0.13	Behavior	31	SOCO scale	0.22
		individual difference	0.13
2005–2008	Market orientation	52	Relationship quality	0.15	2009–2012	Customer orientation	136	Consumer satisfaction	0.12
Customer orientation	51	Implementation	0.14	Performance	124	Service innovation	0.11
Performance	40	Communication	0.14	Market orientation	118	Commitment	0.10
Model	29	Fit	0.14	Antecedent	82	Conceptualization	0.09
Antecedent	25	Consequence	0.13	Impact	71	Expectation	0.09
Satisfaction	23	Environment	0.13	Orientation	71	Retention	0.09
Firm	20	Customer loyalty	0.13	Satisfaction	69	Satisfaction	0.08
Impact	20	Retention	0.13	Model	64	Product development	0.08
Behavior	19	Economy	0.12	Customer satisfaction	47	Marketing capability	0.08
Orientation	19	Attitude	0.11	Management	42	Self	0.08
Knowledge	19	Competitor orientation	0.11
2013–2016	performance	162	market orientation	0.20	2017–2020	customer orientation	219	job satisfaction	0.13
market orientation	161	dynamic capability	0.14	performance	202	absorptive capacity	0.12
customer orientation	156	industrial	0.13	impact	156	hotel	0.10
impact	133	customer orientation	0.12	market orientation	154	customer orientation	0.09
orientation	104	business-to-business	0.09	orientation	125	market orientation	0.09
antecedent	91	optimization	0.08	antecedent	110	employee satisfaction	0.09
customer satisfaction	82	hotel	0.07	innovation	89	sales performance	0.08
satisfaction	72	strategic management	0.07	customer	79	corporate reputation	0.08
model	68			satisfaction	77	emerging economy	0.08
management	65			moderating role	74

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).

Figure 6.

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.

Table 5.

Summary of the Largest 10 Clusters.

Cluster	Size	Silhouette	Top 10 terms (Log-likelihood rate, p-level)
# 0	46	0.955	market orientation (62.55, 1.0E-4); subjective performance (56.95, 1.0E-4); cultural v (56.95, 1.0E-4); objective v (56.95, 1.0E-4); operational market orientation (56.95, 1.0E-4); channel relationship (51.73, 1.0E-4); supplier perception (46.52, 1.0E-4); manufacturers market orientation (46.52, 1.0E-4); UK companies (41.31, 1.0E-4); subjective empirical evidence (41.31, 1.0E-4)
# 1	46	0.921	organizational performance (89.27, 1.0E-4); complementary role (52.2, 1.0E-4); CRM technology (52.2, 1.0E-4); customer-linking capabilities (52.2, 1.0E-4); process-oriented perspective (46.94, 1.0E-4); empirical investigation (46.94, 1.0E-4); marketing capability (41.69, 1.0E-4); firms resource (36.44, 1.0E-4); enterprise CRM (36.44, 1.0E-4); case study (36.44, 1.0E-4)
# 2	36	0.979	service profit chain (108.67, 1.0E-4); missing link (62.28, 1.0E-4); service-oriented high-performance work system (58.1, 1.0E-4); comprehensive understanding (58.1, 1.0E-4); prosocial motive (53.92, 1.0E-4); emotional labor (53.92, 1.0E-4); employee-customer triad perspective (45.57, 1.0E-4); throughput orientation (45.57, 1.0E-4); Vietnamese banking firm (41.4, 1.0E-4); internal marketing (41.4, 1.0E-4)
# 3	34	0.974	environmental profit (47.54, 1.0E-4); supply chain resilience (47.54, 1.0E-4); green supplier integration (47.54, 1.0E-4); knowledge combination (47.54, 1.0E-4); market-relating capabilities (42.22, 1.0E-4); relational performance metrics (42.22, 1.0E-4); collaborative communication (42.22, 1.0E-4); knowledge type (36.91, 1.0E-4); high-technology firm (36.91, 1.0E-4); new product creativity (36.91, 1.0E-4)
# 4	34	0.99	firm innovativeness (76.68, 1.0E-4); joint moderating effect (39.13, 1.0E-4); hotel industry (34.25, 1.0E-4); market turbulence (33.23, 1.0E-4); enhancing business performance (31.71, 1.0E-4); environmental networking (26.82, 1.0E-4); structural contingencies (26.82, 1.0E-4); enabling innovation (24.36, 1.0E-4); strategic synergies (24.36, 1.0E-4); business performance (23.09, 1.0E-4)
# 5	31	0.999	cross-industry exploration (15.15, 1.0E-4); consumer relationship (15.15, 1.0E-4); customer orientation (0.13, 1.0); market orientation (0.07, 1.0); service innovation (0.06, 1.0); business performance (0.05, 1.0); mediating role (0.05, 1.0); strategic orientation (0.04, 1.0); firm performance (0.04, 1.0); service performance (0.04, 1.0)
# 6	30	0.97	luxury-hotel restaurant (56.26, 1.0E-4); customer-oriented constructive deviance (56.26, 1.0E-4); integrated approach (55.57, 1.0E-4); big social issue (55.57, 1.0E-4); civic engagement (55.57, 1.0E-4); organizational pride (52.38, 1.0E-4); dynamic relationship (52.38, 1.0E-4); mutual information sharing (51.57, 1.0E-4); social penetration theory perspective (51.57, 1.0E-4); relationship empathy (51.57, 1.0E-4)
# 7	30	0.97	radical product innovation (51.02, 1.0E-4); strategic orientation (45.66, 1.0E-4); research agenda (45.41, 1.0E-4); radical new product innovation (45.41, 1.0E-4); successful customer integration (45.41, 1.0E-4); customer relationship (34.29, 1.0E-4); acquisitive learning (34.29, 1.0E-4); firm performance (31.82, 1.0E-4); international market performance (28.8, 1.0E-4); internet marketing capabilities (28.8, 1.0E-4)
# 8	29	0.981	salesperson trait (38.54, 1.0E-4); sensemaking perspective (38.54, 1.0E-4); intuitive judgment (38.54, 1.0E-4); adaptive selling (33.78, 1.0E-4); to-business sales success (32.16, 1.0E-4); d-marketing cooperation (28.8, 1.0E-4); social media use (23.75, 1.0E-4); big brother (22.33, 1.0E-4); innovation success (20.6, 1.0E-4); intensive era (15.99, 1.0E-4)
# 9	26	0.946	service innovation (75.97, 1.0E-4); key service innovation driver (29.71, 1.0E-4); tourism sector (29.71, 1.0E-4); human-related factor (22.24, 1.0E-4); firm performance (21.26, 1.0E-4); empirical evidence (16.54, 1.0E-4); organizational culture (8.22, 0.005); new shore (7.38, 0.01); product innovation (7.38, 0.01); hybrid innovation (7.38, 0.01)

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.

Table 6.

The Documents With Strong Citation Burst in Customer Orientation.

Number	Author	Title	Source	Burst	Begin	End	Co-citation	Centrality
#1	Kirca et al. (2005)	Market orientation: a meta-analytic review and assessment of its antecedents and impact on performance	Journal of Marketing	22.53	2006	2021	68	0.03
#2	Brown et al. (2002)	The customer orientation of service workers: personality trait effects on self and supervisor performance ratings	Journal of Marketing Research	21.8	2006	2021	44	0.25
#3	Donavan et al. (2004)	Internal benefits of service-worker customer orientation: job satisfaction, commitment, and organizational citizenship behaviors	Journal of Marketing	18.96	2006	2021	53	0.00
#4	Vargo and Lusch (2004)	Evolving to a new dominant logic for marketing	Journal of Marketing	18.67	2006	2021	50	0.15
#5	Narver et al. (2004)	Responsive and proactive market orientation and new-product success	Journal of Product Innovation Management	14.74	2006	2021	39	0.16
#6	Zablah et al. (2012)	How and when does customer orientation influence frontline employee job outcomes? a meta-analytic evaluation	Journal of Marketing	14.35	2012	2021	104	0.15
#7	Franke and Park (2006)	Salesperson Adaptive Selling Behavior and Customer Orientation: A Meta-Analysis	Journal of Marketing Research	13.8	2006	2021	42	0.19
#8	Atuahene-Gima et al. (2005)	The Contingent Value of Responsive and Proactive Market Orientations for New Product Program Performance	Journal of Product Innovation Management	12.41	2006	2021	32	0.23
#9	Homburg et al. (2011a)	When Should the Customer Really Be King? On the Optimum Level of Salesperson Customer Orientation in Sales Encounters	Journal of Marketing	11.43	2011	2021	59	0.12
#10	Zhou et al. (2005)	The effects of strategic orientations on technology- and market-based breakthrough innovations	Journal of Marketing	10.86	2006	2021	33	0.10
#11	Noble et al. (2002)	Market Orientation and Alternative Strategic Orientations: A Longitudinal Assessment of Performance Implications	Journal of Marketing	10.85	2006	2021	22	0.00
#12	Im and Workman (2004)	Market orientation, creativity and new product performance in high-technology firms	Journal of Marketing	10.18	2006	2021	27	0.24
#13	Brady and Cronin (2001)	Customer orientation: effects on customer service perceptions and outcome behaviors	Journal of Service Research	10.07	2006	2021	18	0.00
#14	Jones et al. (2003)	Firm market orientation and salesperson customer orientation: interpersonal and intrapersonal influences on customer service and retention in business-to-business buyer–seller relationships	Journal of Business Research	9.98	2006	2021	24	0.09
#15	Stock and Hoyer (2005)	An attitude-behavior model of salespeople’s customer orientation	Journal of the Academy of Marketing Science	9.42	2006	2021	25	0.25
#16	De Luca and Atuahene-Gima (2007)	Market Knowledge Dimensions and Cross-Functional Collaboration: Examining the Different Routes to Product Innovation Performance	Journal of Marketing	8.66	2007	2021	25	0.00

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.

Table 7.

Directions for Future Research.

Eras	Sample research questions
The new mediating and moderating factors on the linkage between customer orientation and performance	What kinds of leadership would be better in cultivating employees customer orientation to achieve high performance in the post-pandemic era?What new mediators and/or moderators on the relationship between customer orientation and various performance in the new dynamic marketing environment?
The mechanism of customer orientation in the social media context.	How to transform customer orientation into digital customer orientation in the platform ecosystem?What are the implications of we media platform on customer orientation activities?What are the differences between the mechanism of traditional customer orientation and the mechanism of customer orientation in the social media context?How to effectively improve digital customer orientation? What are the key determinants of digital customer orientation?What are the mediators on the linkage between digital customer orientation and performance?
The linkage between corporate social responsibility and customer orientation	What kinds of corporate social responsibility can arouse high customer-oriented behaviors?Does engaging corporate social responsibility practices improve employee customer-oriented attitude?What are the mediators and moderators on the linkages between corporate social responsibility and customer orientation?
The mechanism of customer orientation at the team level	How to achieve a high consistency customer-oriented team? What are the drivers of high team customer orientation climate?Does high team customer orientation climate lead to a high team customer orientation strength?What are the customer, team, and organizational outcomes of team customer orientation climate?

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

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

Funding

The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Young Foundation of Ministry of Education in China (No. 19YJC630227), National Natural Science Foundation of China (No.71972033), the Fundamental Research Funds for the Central Universities (No. N2023035).

ORCID iD

Xin Zhao

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Customer Orientation: A Literature Review Based on Bibliometric Analysis

Abstract

Keywords

Introduction

Methodology

Data Sources

Analytical Method and Tool

Bibliometric analysis and CiteSpace

Co-citation analysis

Co-occurrence analysis

Burst detection

Cluster analysis and timeline and timezone view

Data Analysis

Results

Distribution of Journals and Cited Journal Analysis

Author and Author Co-Citation Analysis

Top Institutes in Customer Orientation Research

Popular Topics

Document Analysis in Customer Orientation

Discussion and Conclusion

Footnotes

Declaration of Conflicting Interests

Funding

ORCID iD

References