Toss a Peach and Get Back a Plum: Impact of Customized Services on Firm Performance

Abstract

The digital economy has led to the diversification of user needs and continuous innovation by firms, and firms are increasingly providing customized services to respond to changing user needs and expect to improve their competitiveness and performance. However, there are few in-depth studies on the mechanisms of the impact of customized services on firm performance and the differences among industries. This study attempts to fill this gap by constructing an econometric model and conducting an empirical study. The results of the analysis of data from firms on Taobao show that: customized services provision has a positive effect on firm performance; differences exist in these positive effects, with the effects ranging from high to low in the raw materials, consumer goods, and industrial goods industries; and customer satisfaction plays a mediating role between customized services and firm performance. This study provides insights for firms to modify, improve, and transform their business strategies in the context of the digital economy.

Keywords

customized services firm performance consumer satisfaction heterogeneity Taobao

Introduction

Intensified market competition puts firms under constant pressure to innovate (Theilen, 2012; Vives, 2008). In the digital economy, providing customized services in the face of unprecedentedly diverse user needs is an attempt at innovation made by many firms, such as Identity Club, Haier, and Qingdao Red Collar) (Feng et al., 2015). The essence of customized services is to understand the individual needs of each customer in advance and meet them in the service process (Kumar, 2007; Tseng et al., 2010). The concept of customized services was first introduced by Davis (1989), and a series of similar concepts have been proposed, such as a “one-to-one relationship,”“personalized customization,”“adaptation,”“individuation,” and “consumer-centric” (Anshari et al., 2019; Peppers & Rogers, 1993; Riecken, 2000; Xin & Sun, 2010). These studies investigated the connotation and extension of customized services (Fan & Poole, 2003; Jiao et al., 2007) and explored the antecedents of their popularity among consumers (Sundar & Marathe, 2010). In addition, the key role of customized services in customer relationships and network strategies has been studied (Fan & Poole, 2003). This study considers a firm to be using customized services if it offers services with differentiated products for different consumers.

The development of the digital economy has promoted the development of customized services, and a series of typical customized service applications have emerged in various industries. For example, in the home appliance industry, the flexible mass customization of one production line that can support more than 500 models has been realized in Haier’s Shenyang refrigerator factory. The production beat of this factory has been shortened to 10 seconds a unit, which is the factory with the fastest production beat and the widest range of models undertaken in the global refrigerator industry. In the furniture industry, the representative enterprise Markor Home Furnishings realizes the intelligent integration of the manufacturing system at the manufacturing end through modular product design, intelligent manufacturing technology, intelligent logistics technology, automation technology, and IT technology application, thus supporting the realization of the mass customization business model. In the apparel industry, the Adidas Swift factory selects ingredients and designs according to customer needs and completes customization with the joint collaboration of robots and human assistance. Robots, 3D printers, and knitting machines in the factory are directly controlled by computer design programs, reducing the changeover time required to produce different products. Another typical apparel company offering customization services is the Red Collar Group, which has built a platform digital operation system containing more than 20 subsystems. With its big data processing system, it has more than 1,000 trillion design combinations and over 100 trillion style combinations.

Numerous studies have shown that a firm’s performance reflects its competitive performance in the marketplace (Van, 1992), thus the impact of customized services on firm performance improvement is an issue of concern. Xu and Lu (2021) asserted that improving firm performance is the core objective of firms providing customized services. Customers and firms are the two subjects involved in customized services, and consumers’ individually unique needs are greatly enhanced in the digital economy context (Belk, 2013). With the promotion of internet technology and online trading platforms, firms are free from geographic constraints in providing products and services, making it possible to provide customized services to consumers on a large scale, satisfying users’ personalized needs and improving service performance (Li et al., 2020). However, in marketing, Kaniewska-Sejba and Pilarczyk (2014) found that excessive personalization of customized services may be perceived as a risk, when customized services do not lead to improved firm performance. We argue that these conflicting results arise from the lack of in-depth investigation on the boundary conditions and mechanisms of the impact of customized services on firm performance improvement. Customized services as a strategy have been adopted in a variety of industries, yet there are widespread differences and even barriers among industries, and the findings that customized services work in one industry are not necessarily generalizable to other industries. At the same time, we propose the intermediary role of customer satisfaction in the relationship between customized services and corporate performance in terms of matching the individualized needs of customers with the customized services offered by the firm. Motivated by the above concerns, we address the following three research questions.

RQ1: Is there a positive effect of customized services on firm performance?

RQ2: Does the impact of customized services on firm performance vary by industry?

RQ3: Is the impact of customized services on firm performance mediated by customer satisfaction?

To answer these questions, we developed three research hypotheses based on the literature. We used a sample of firms on Taobao to obtain objective data on firm customized services and consumer satisfaction; we wrote a web crawler code to empirically examine the effects of customized services on firm performance, industry differences, and the mediating effect of customer satisfaction and constructed an econometric model. Compared with past studies, the theoretical contribution of the current study is threefold. First, although prior literature logically anticipated a positive relationship between customized services and firm performance, the current study, to the best of our knowledge, is one of the first to provide empirical evidence that customized services contribute to firm performance using objective data.

Second, we point out the impact of industry on the effect of customized services and broaden boundary conditions for customized services to enhance firm performance, while past studies targeted a single industry or did not differentiate between industries.

Third, this study reveals and validates the mediating role of customer satisfaction between customized services and firm performance. It also provides practical insights into firms’ transformation, improvement, and business strategy modification in the context of the digital economy, where customized services have proven to be a viable option. Additionally, our results remind firms to focus on their own industry and emphasize the importance of customer satisfaction.

The remainder of the paper is organized as follows: Section “Literature Review” reviews the relevant literature, and Section “Theoretical Model and Research Hypothesis” describes the process of developing the research hypotheses. Section “Empirical Tests” presents the data and model; the empirical results are given in Section “Mechanism of Action Test and Heterogeneity Analysis.” We present the conclusion and research implications in the final section.

Literature Review

Customized Services and Their Impact

The boom of the digital economy and e-commerce has prompted firms to broaden their sales channels through the internet, and the competition among firms is gradually shifting from price to service (S. M. Ali et al., 2018). Firms have increasingly adopted customized services to improve their service in response to progressively more diverse customer needs (Liu et al., 2021; G. Wang et al., 2010). Previous literature on customized services has focused on the customization of advertising services. For example, Imhoff et al. (2001) consider customized services as a firm’s ability to identify and treat its consumers through personal information, targeted banner advertisements, bill specials, or other personal transactions. Roberts and Zahay (2012) consider customized services the process of preparing personalized service for a specific person based on stated or implied preferences. Customized services strengthen the interactions and deepen the relationships between firms and consumers (O’Hern & Rindfleisch, 2010; Simonson, 2005). Customized services in early marketing was a limiting factor in achieving further benefits of mass customized production (Kannan, 2017; Wind & Rangaswamy, 2001). However, customized services in terms of promotion, communication, product, service, price, or delivery have become a reality driven by information technology (Montgomery & Smith, 2009; Verhagen et al., 2014).

Customized services have an impact on stakeholders—both firms and customers—as they involve the interaction and joint participation of both parties. In terms of the impact on firms, Fitzsimmons and Fitzsimmons (2008) argue that the joint value creation of firms and consumers is enhanced by customized services offered by firms. Firm performance is higher, and the product price is higher, more profitable and competitive in the presence of customized services (Ansari & Mela, 2003; Arora et al., 2008; Postma & Brokke, 2002). Customized services can create benefits for consumers and marketers. For consumers, they can provide better preference matching, products, services, communication, and experience (Dillman, 1978; Fiore et al., 2004; Pitta et al., 2004; Polito & Watson, 2004; Prahalad & Ramaswamy, 2004). However, in the marketing industry, it has also been proven that highly personalized services can have a negative impact, as they can be perceived by consumers as presenting a risk to personal information, which can be detrimental to the sale of products (Kaniewska-Sejba & Pilarczyk, 2014). In terms of the impact on customers, Mittal and Lassar (1996) reveal a positive relationship between customized services and customer satisfaction. In terms of the mechanisms by which customized services enhance customer satisfaction, Liang et al. (2009) propose a dual core theory that considers the combined effect of economic factors through perceived transaction costs and emotional factors of perceived care.

The impact of customized services on consumer satisfaction is also influenced by other factors. Studies based on choice task complexity theory and loss aversion theory have found that consumers’ perceived utility of product customization is influenced by their perceived complexity of the customization construct in addition to the utility of the customized product (Dellaert & Stremersch, 2005). Customization by attributes can increase consumer satisfaction and purchase intention for customization compared to customization by solution (Valenzuela et al., 2009). A customization approach that offers an initial solution works better than customization by attribute because the former approach reduces customization complexity (Hildebrand et al., 2014).

Factors Influencing Firm Performance

Academic research on the factors affecting firm performance has a long history, and the identified factors are distributed among the macro, meso, and micro levels. Macrolevel factors include inflation, exports, and taxes (M. Ali & Ibrahim, 2018; Tuhin & Swanepoel, 2017), while mesolevel studies have examined the impact of factors such as industrial policies on firm performance (Criscuolo et al., 2007). The customized services referenced in this study are microlevel factors, which are often studied in the context of firms, and the effects of factors such as firm capital structure and equity structure on firm performance have been noted in the past literature (Saeedi & Mahmoodi, 2011; Saleh et al., 2017; Salim & Yadav, 2012). Fosu (2013) concludes that financial leverage significantly improves firm performance by analyzing 257 firms in South Africa over the period 1998 to 2009. Yu (2013) demonstrates that firms with state-owned equity structures perform better than firms with state-owned equity structures because of their access to more government support. In addition, factors such as corporate investment, executive characteristics, board size, and corporate strategy have been found to have an impact on firm performance (Adams et al., 2005; Arasa & Gathinji, 2014; Elsayed, 2011; Grazzi et al., 2016).

There is a lack of studies that directly establish the association between customized services and firm performance. The individually unique needs of consumers are greatly enhanced in the digital economy context (Yang & Song, 2019). Meanwhile, it has become possible for companies to provide mass customized services to consumers with the help of internet technology and network platforms (Bardakci & Whitelock, 2004; Li et al., 2020; Rajapathirana & Hui, 2018; Vesanen, 2007). Another chain of inference of validity exists with respect to the relationship between customized services and firm performance. Since customized services are also essentially a form of collaboration between firms and customers (Franke et al., 2010), there are differences in the impact of different types of collaboration on firm performance (Belderbos et al., 2004). In response to these research gaps, this study attempts to take a step further through empirical research to provide a clearer understanding of whether and how customized services affect firm performance.

Theoretical Model and Research Hypothesis

Impact of Customized Services

Customized services were originally designed with the core objective of improving firm performance (Xu & Lu, 2021), and although some studies such as Kaniewska-Sejba and Pilarczyk (2014) fail to reveal an association between customized services and firm performance, we assert that, overall, a positive association between them is to be expected. Past research has hinted at different ways in which customized services may impact firm performance. First, firms improve performance by providing differentiated products and services through customized services (Arora et al., 2008). Customized services are essentially an innovation, and firms’ adoption of them helps them make their products more competitive. When the cost is the same, firms provide higher quality and service, which helps increase market share and thus improve performance. Second, customized services enhance corporate performance by cocreating value with consumers. They can attract more consumers (J. B. Wang & Fan, 2020), and in the process of consumer involvement in product design, the integration of knowledge between firms and consumers can play a key role in value creation and value coproduction (Vargo & Lusch, 2004). The value of cocreation and value-adding is achieved by integrating consumer resources and optimizing product design to better meet consumer needs (Fitzsimmons & Fitzsimmons, 2008). Based on the above analysis, we argue that customized services contribute to firm performance, and we propose the first research hypothesis.

Hypothesis 1 Customized services make a positive contribution to firm performance.

Industry Heterogeneity

Customized services are a way for companies to process their products and thus meet the individual needs of consumers. The impact of customized services on firm performance may vary by industry, as there are significant differences between products among industries (Tran & Santarelli, 2017). Analyzed from a product perspective, the degree of personalized processing of products varies from industry to industry, as does the degree of product standardization. Among the three industries of raw materials, consumer goods, and industrial products, industrial products require the highest degree of standardization, while raw materials products and consumer goods are relatively less standardized and have more room for processing and design (Kasiri et al., 2017). Analyzed from the perspective of consumer demand, there are differences in the diversity of consumer demand faced by products in different industries. Consumer demand diversity is richer for products of raw materials (e.g., textiles) and consumer goods (e.g., apparel), while it is weaker for industrial products (Lewis & Winkler, 2015). It should be added that the three industries analyzed in this paper, raw materials, consumer goods, and industrial goods industries, are divided differently from the traditional national economic industry classification. In particular, according to Taobao’s division, the raw materials industry refers to some industries that have a rich diversity of products with a low degree of standardization (such as the agricultural products industry), but are used for further processing of some industries with more space. Thus, the contribution of customized services to firm performance is confirmed for all three industries but is greater in industries with low product standardization and highly diverse consumer demand. The contribution of customized services to firm performance is greater in the consumer goods and raw materials industries than in the industrial goods industry. At the same time, it is greater in raw materials than in consumer goods.

Hypothesis 2 In the raw material, consumer goods and industrial goods industries, the contribution of customized services to firm performance is in the order of raw material, consumer goods, and industrial goods.

Mechanism of Customer Satisfaction

In the provision of products to consumers, the involvement of consumers in the product design process can be enhanced by customized services, and products that meet consumer needs are produced more often, thus increasing consumer satisfaction. Increased consumer satisfaction helps improve buyerã seller relationships, enrich two-way communication, improve service quality, improve products, and promote consumer value creation. With high consumer satisfaction, consumer recognition of the product can be enhanced and lead to more active consumer participation in the value creation process of the company. As a result of value cocreation between companies and consumers, consumers’ attitudes toward products and services are improved, leading to increased purchase intentions and a word-of-mouth effect (Humphreys, 2009). At the same time, consumers are able to incorporate their own preferences into the creation of new products, so that the products are tailored to their preferences and thus better meet their individual needs. The size of the firm’s sales will increase as a result of increased consumer satisfaction, which in turn will improve the firm’s performance. All else being equal, customized services will increase consumer satisfaction and lead to higher consumer purchase intentions, which in turn will lead to higher performance. Based on the above analysis, this paper proposes the third research hypothesis.

Hypothesis 3 Customized services will raise the level of firm performance by increasing consumer satisfaction.

Empirical Tests

Data Sources

This study empirically tests the above proposed hypotheses by collecting objective data and constructing an econometric model. The data used for analysis come from the firm data on Taobao.com, which contains basic firm information and business status. The basic information includes the company’s products or services, industry, registered capital, establishment time, enterprise type, legal representative, and social credit code and whether it provides customized services. The business status contains indicators such as turnover, cumulative number of transactions, buyer evaluation score, repeat purchase rate, satisfaction rate, refund rate, exports, and R&D.

Taobao, the website chosen for this study, is very representative. It is the largest online retail business community in the Asia-Pacific region and was founded by Alibaba Group in May 2003. Taobao is a popular online retail platform in China, with nearly 500 million registered users and over 60 million regular visitors per day, while the number of products online has exceeded 800 million per day, with an average of 48,000 products sold every minute. Additionally, the data sample chosen for this study is representative. We crawled to obtain Taobao.com store data through Python software in July 2019, and the total number of initial samples was 310,239. After obtaining the samples, the sample data were processed, some important missing information and obviously invalid data were eliminated, and the final number of valid samples was 213,545. The data are not from Taobao.com’s “Double Eleven” campaign, which ensures their validity.

Variables

Empirical model building involves determining the explanatory variables, core explanatory variables, and mediating variables. Firm performance is the explanatory variable, which is measured in this study by using the firm turnover ratio over the number of employees in the firm. Restricted by the availability of data, there are very few indicators that can be used to explain firm performance in the data of this study. This paper selects the ratio of firm turnover to the number of employees as a proxy variable because while it is less than perfect, firm performance is the most common solution that we could find. Customized services is the core explanatory variable, which is measured by using the availability of customized services in this study. The mediating variable is Satisfaction. Satisfaction is the ratio of consumers who are satisfied throughout the purchase process to all consumers, and the variable Satisfaction takes values in the range of 0% to 100%. Also, in the process of robustness testing, this paper uses Repeat Purchase Rate (Recon) and User Evaluation (Evaluation). Recon is the ratio of consumers with multiple purchases in the store to all consumers, and the range of the variable Recon is 0% to 100%. Evaluation refers to the level of consumers’ comprehensive evaluation of the store’s product quality and service and other factors, which laterally reflects the level of consumers’ satisfaction with the product. The range of values of the variable Evaluation is 0 to 5. In addition, this study controls for important control variables, including firm age, price index, whether or not the firm has a website, exports, and R&D.

Industry Segmentation

In the Taobao firm data, industries are divided into the four categories of raw materials, consumer goods, industrial goods, and business services. Among them, the raw material industry includes textiles, packaging, agriculture, construction and building materials, medical maintenance, metallurgy, rubber and plastic, and other subsectors. The consumer goods industry includes clothing, underwear, bags, shoes, household items, food and beverages, digital, toys, and other subsectors. Industrial goods include segments such as machinery, electricians, equipment, security, electronics, hardware tools, instruments, and lighting. In conducting the industry heterogeneity analysis, we adopt Taobao’s industry classification guidelines. Because the business services industry itself has highly customized service characteristics, it is not comparable with the other three industries. Therefore, this study classifies industries into three major categories—raw materials, consumer goods, and industrial goods—after considering data and industry factors to support the next step of industry heterogeneity assessment.

Model Building

In this study, we analyze the mechanism of the effect of customized services on firm performance in theoretical development. To empirically test the proposed research hypotheses, we construct econometric regression models (Wen et al., 2004), as in equations (1) to (3).

Performanc e_{i} = β_{0} + γ_{0} Service s_{i} + δ_{0} X_{i} + ε_{i}

(1)

Satisfactio n_{i} = β_{1} + γ_{1} Service s_{i} + δ_{1} X_{i} + ε_{i}

(2)

\begin{matrix} Performanc e_{i} = β_{2} + γ_{2} Service s_{i} + λ_{2} Satisfactio n_{i} \\ + δ_{2} X_{i} + ε_{i} \end{matrix}

(3)

where $Performanc e_{i}$ indicates firm performance, $Service s_{i}$ indicates whether the firm provide customized services, and $Satisfactio n_{i}$ indicates the level of user satisfaction. $X_{i}$ is a series of control variables, and $ε_{i}$ is a random disturbance term.

Mechanism of Action Test and Heterogeneity Analysis

Data Samples

In this study, a web crawler program written in Python was used to crawl Taobao online firm data in July 2019, and a total of 310,239 initial samples were obtained. After acquiring the data, we cleaned the sample data and eliminated some data with important information missing or clearly invalid. Since enterprise turnover, number of employees, export size, and R&D investment are interval types in the obtained data, we processed the interval data and used the median value of the interval as its proxy value. The descriptive statistics of the variables used in the econometric model after processing are shown in Table 1.

Table 1.

Descriptive Statistics of the Sample.

Variables	Variable explanation	Maximum	Minimum	Average	Standard deviation
Performance	Firm performance	333	0	32.853	45.254
Services	Whether to provide customized services	1	0	0.845	0.362
Age	Firm age	40	1	6.075	4.281
Price	Price index	14	0.0001	0.641	1.650
Web	Whether to provide website	1	0	0.538	0.499
Export	Export level	14	0	4.423	3.542
RD	R&D intensity	9	0	1.734	1.165
Recon	Whether repeat consumption	1	0	0.238	0.171
Satisfaction	Consumer satisfaction	100%	0%	97.6%	0.107
Evaluation	Consumer reviews	5	0	4.886	0.525

Baseline Regression Results

According to the equation of model (1), a benchmark regression is conducted to analyze the impact of customized services on firm performance. Table 2 shows the results of the baseline regression. Where only the core explanatory variables are considered in column (1), control variables such as firm age and price index are added and controlled for industry in column (2), control variables are considered and controlled for city in column (3), and both industry and city are controlled for in column (4) to attenuate the effect of omitted control variables on the regression results.

Table 2.

Baseline Regression Results.

Variables	(1)	(2)	(3)	(4)
Variables	Performance	Performance	Performance	Performance
Services	8.209*** (0.256)	1.720*** (0.231)	1.791*** (0.234)	1.803*** (0.234)
Age		1.177*** (0.026)	1.062*** (0.027)	1.048*** (0.027)
Price		2.075*** (0.075)	1.859*** (0.076)	1.816*** (0.076)
Web		−0.108 (0.179)	0.143 (0.178)	0.098 (0.178)
Export		5.741*** (0.038)	5.814*** (0.039)	5.829*** (0.039)
RD		1.891*** (0.091)	1.931*** (0.090)	1.873*** (0.091)
Constant	26.63*** (0.232)	−6.847*** (0.319)	−10.07*** (0.249)	−9.574*** (0.344)
Control industry	No	Yes	No	Yes
Control the city	No	No	Yes	Yes
Observations	213,545	194,865	194,865	194,865
R-squared	0.004	0.262	0.283	0.284

Note.*** indicate significant at the 1% levels, respectively, and the numbers in parentheses are standard errors.

According to the regression results, the regression coefficients of the services variables in columns (1) to (4) are 8.209, 1.720, 1.791, and 1.803, respectively, and all are significant at the 1% level. This indicates that firms’ adoption of customized services will be beneficial to firm performance; thus, research hypothesis 1 is confirmed. The above findings contribute to the richness of the literature in customized services research, especially adding to that examining the impact of customized services on firm performance from the perspective of e-commerce platforms. The findings provide some answers to the question of why firms are more proactive in implementing customized service strategies in an era of rapid digital technology development. In addition, in terms of the effects of the control variables, age, price, web, exports, and RD all have positive effects on firm performance. That is, the longer the firm has been established, the better its performance is; the level of product price is also beneficial to the firm’s performance, and a firm with a website will perform better than a firm without one. The higher the export level is, the better the firm’s performance. The higher the firm’s R&D, the more it improve the firm’s performance.

Endogenous Treatment

Although this paper includes a series of control variables and fixed effects to control for certain variables in the baseline regression, there may still be other unobservable factors that impact firm performance. Additionally, certain reverse causality issues lead to an endogeneity problem of the results in this paper. The higher the performance of Taobao store firms is, the more capital and ability they have to customize personalized services for customers, and firm performance in turn affects firm personalized service customization. Therefore, this paper adopts the instrumental variable method for endogeneity. Since personalized service customization by firms is itself a firm digital service innovation behavior, this paper draws on Huang et al. (2019) and uses the historical number of post offices per million people in 1984 in each city as an instrumental variable to examine the endogeneity issue. The instrumental variable is eligible. First, Taobao stores are a product of the internet era and benefit from the technological development of the internet, which began with the traditional communication technology of landline telephones. Therefore, the selection of historical variables is relatively exogenous. Second, the better the internet development in the cities where Taobao stores are located is, the more efficiently the Taobao stores there operate, and the easier it is for them to pay attention to consumers’ individual needs and thus carry out service innovation activities. Therefore, the instrumental variables satisfy the relevance requirement. Additionally, relative to the rapid development of the internet, the influence of the historical number of post offices on firm performance gradually disappears, which can indicate that the instrumental variable satisfies the exclusion requirement. In addition, since the instrumental variables vary at the city level, controlling for city fixed effects will be absorbed, so city fixed effects are not included. The results of the two-stage least squares regression are shown in Table 3.

Table 3.

Endogeneity Treatment Results.

Variables	(1)	(2)
	Service	Eff
	Phase I	Phase II
Post office	0.012*** (0.004)
Service		153.863*** (27.811)
Age	0.002*** (0.000)	0.965*** (0.0558)
Price	0.001** (0.000)	1.913*** (0.111)
Web	0.035*** (0.000)	−5.452*** (1.023)
Export	0.009*** (0.000)	4.401*** (0.254)
RD	0.005*** (0.001)	1.069*** (0.210)
Constant	0.766*** (0.003)	−122.0*** (21.05)
Control industry	Yes	Yes
Kleibergen-Paap rk LM		44.200
Cragg-Donald Wald F		44.470
Observations	191,579	191,579

Note.**, and ***indicate significant at the 5%, and 1% levels, respectively, and the numbers in parentheses are standard errors.

The first column in Table 3 shows the regression of the instrumental variables on whether the firm provides customized services. The coefficient of the instrumental variables is 0.012, which is significant at the 1% level, indicating that the instrumental variables satisfy the correlation. As seen in column (2) of Table 3, the Kleibergen–Paap rk LM statistic is 44.2 (p value of 0.000), rejecting unidentifiability. The Cragg-Donald Wald F-statistic is greater than the 10% Stock-Yogo criterion, rejecting the weak instrumental variable, which indicates that the instrumental variable is reliable. The estimated coefficients from the service and effort regressions show that firms’ personalized and customized services can significantly contribute to firm performance, which is consistent with the previous findings.

Results of Industry Heterogeneity Analysis

Table 4 shows the regression results of the industry heterogeneity analysis, with column (1) denoting the raw materials industry, column (2) denoting the consumer goods industry, and column (3) denoting the industrial goods industry. In these results, the coefficients of the Services variable for the raw materials, consumer goods, and industrial goods industries are 4.075 (p < .01), 1.276 (p < .01), and 1.188 (p < .01), respectively, thus indicating that customized services have a catalytic effect on firm performance in all three industries. Additionally, comparing the magnitudes of the coefficients reveals that the contribution of customized services to firm performance is in the order of raw materials, consumer goods, and industrial goods. Therefore, research hypothesis 2 is confirmed. The findings further validate that customized services contribute more to firm performance in industries with low product standardization and rich and diverse consumer needs, as proposed in the theoretical analysis section of this paper. These findings have significant managerial implications and provide meaningful guidance for firms in different industries for implementing customized service strategies.

Table 4.

Regression Results by Industry.

Variables	(1)	(2)	(3)
	Raw material	Consumer goods	Industrial goods
	Performance	Performance	Performance
Services	4.075*** (0.510)	1.276*** (0.338)	1.188*** (0.434)
Age	1.247*** (0.050)	1.031*** (0.042)	0.738*** (0.048)
Price	1.991*** (0.134)	1.148*** (0.154)	2.001*** (0.118)
Web	0.259 (0.393)	−1.065*** (0.248)	2.079*** (0.340)
Export	5.577*** (0.084)	5.614*** (0.055)	6.474*** (0.074)
RD	2.636*** (0.203)	1.772*** (0.124)	0.946*** (0.184)
Constant	6.157 (9.400)	−8.979*** (0.360)	−24.97*** (4.488)
Control the city	Yes	Yes	Yes
Observations	45,898	96,903	49,526
R-squared	0.286	0.271	0.333

Note.*** indicate significant at the 10%, 5%, and 1% levels, respectively, and the numbers in parentheses are standard errors.

Results of the Mediating Mechanism Test

Based on the baseline regression and industry heterogeneity analysis, we continue to test and reveal the mechanism of the role of consumer satisfaction in the relationship between customized services and firm performance. The regression model uses the test of the mediating effect set by models (1) to (3). Table 5 reports the results of the mediating mechanism test.

Table 5.

Mechanism of Action: Consumer Satisfaction as a Mediating Variable.

Variables	(1)	(2)	(3)
Variables	Performance	Satisfaction	Performance
Services	1.803*** (0.234)	0.0126*** (0.001)	1.767*** (0.234)
Satisfaction			2.818*** (0.854)
Age	1.048*** (0.027)	0.001*** (0.000)	1.044*** (0.027)
Price	1.816*** (0.076)	−0.012*** (0.000)	1.849*** (0.077)
Web	0.098 (0.178)	0.009*** (0.000)	0.072 (0.178)
Export	5.829*** (0.039)	0.000*** (0.000)	5.828*** (0.039)
RD	1.873*** (0.091)	0.001*** (0.000)	1.869*** (0.091)
Constant	−9.574*** (0.344)	0.987*** (0.001)	−12.35*** (0.903)
Control industry	Yes	Yes	Yes
Control the city	Yes	Yes	Yes
Observations	194,865	194,865	194,865
R-squared	0.284	0.044	0.284

Note.*** indicate significant at the 1% levels, respectively, and the numbers in parentheses are standard errors.

Column (1) in Table 5 analyzes the effect of customized services on firm performance, column (2) analyzes the effect of customized services on consumer satisfaction, and column (3) adds both customized services and consumer satisfaction to the model. In all three regression models, control variables are included, and industry and city factors are taken into account.

As reported in Table 5, the baseline regression in column (1) reflects a coefficient of 1.803 (p < .01) for the Services variable. The coefficient of the Services variable in column (2) is 0.0126 (p < .01), indicating that customized services positively contribute to consumer satisfaction. This means that the higher the level of customized services is, the higher the level of satisfaction that consumers experience in the process of purchasing the product is. In column (3), the coefficients of the two variables of Services and Satisfaction are 1.767 (p < .01) and 2.818 (p < .01), respectively. This indicates that Services and Satisfaction positively contribute to firm performance when both variables are placed in the model. Therefore, combining the results in the three columns of Table 5, it can be concluded that consumer satisfaction is a mediating variable for the effect of customized services on firm performance. This means that customized services contribute to improved consumer satisfaction and thus to better corporate performance. Therefore, research hypothesis 3 is confirmed. This finding indicates that consumer satisfaction is an important aspect of the role of customized services in firm performance, which explains from a different perspective why customized services are increasingly important and that consumers prefer customized service products because they can achieve higher satisfaction after purchasing such product.

Robustness Tests

To further verify that customized services contribute to the level of firm performance by enhancing consumer satisfaction, this study uses substitution of mediating variables to test the robustness of the results through two substitutions.

First, consumer repeat consumption behavior is used to replace satisfaction, which is measured by using the consumer repeat consumption rate. Consumer repeat purchase behavior can reflect consumer satisfaction to some extent (Anderson & Sullivan, 1993), if consumers are more satisfied after purchasing from a firm, they are more likely to continue to purchase from with that firm, which means that consumer repeat purchase behavior is another expression of consumer satisfaction. The higher the consumer repurchase rate is, the higher consumer satisfaction is. Table 6 reports the results of the mechanism test with consumer repeat consumption behavior as the mediating variable. The results in column (1) of Table 6 are consistent with the previous paper, and column (2) shows, that the coefficient of the services variable is 0.015 (p < .01), which indicates that customized services make a positive contribution to consumers’ repeat consumption behavior. The coefficients for Service and Recon in column (3) are 1.787 (p < .01) and 0.991 (p < 0.1), respectively. By combining these results, the mediating effect of consumer repeat consumption was verified.

Table 6.

Mechanism Test: Repeated User Purchase as a Mediating Variable.

Variables	(1)	(2)	(3)
Variables	Performance	Recon	Performance
Services	1.803*** (0.234)	0.015*** (0.001)	1.787*** (0.234)
Recon			0.991* (0.538)
Age	1.048*** (0.027)	0.002*** (0.000)	1.046*** (0.027)
Price	1.816*** (0.076)	−0.023*** (0.000)	1.839*** (0.078)
Web	0.098 (0.178)	0.005*** (0.001)	0.094 (0.178)
Export	5.829*** (0.039)	0.003*** (0.000)	5.826*** (0.039)
RD	1.873*** (0.091)	−0.007*** (0.000)	1.880*** (0.091)
Constant	−9.574*** (0.344)	0.127*** (0.002)	−9.700*** (0.350)
Control industry	Yes	Yes	Yes
Control the city	Yes	Yes	Yes
Observations	194,865	194,865	194,865
R-squared	0.284	0.086	0.284

Note.* and *** indicate significant at the 10%, and 1% levels, respectively, and the numbers in parentheses are standard errors.

Second, the Satisfaction variable is replaced with the Evaluation variable, which is measured using the consumer evaluation level indicator. Consumer evaluation reflects consumer satisfaction from another angle. Table 7 reports the results of the mechanism test with the consumer evaluation level as the mediating variable. The coefficient of the Services variable in column (2) is 0.061 (p < 0.01), while the coefficients of the Services and Evaluation variables in column (3) are 1.757 (p < 0.01) and 0.753 (p < 0.01), respectively. It can be concluded that this verifies the mediating effect with the level of consumer evaluation. The results of the above tests indicate the robustness of our conclusions.

Table 7.

Mechanism Test: Mediating Variable with Consumer Evaluation Level.

Variables	(1)	(2)	(3)
Variables	Performance	Evaluation	Performance
Services	1.803*** (0.234)	0.061*** (0.004)	1.757*** (0.234)
Evaluation			0.753*** (0.173)
Age	1.048*** (0.027)	0.005*** (0.000)	1.045*** (0.027)
Price	1.816*** (0.076)	−0.059*** (0.002)	1.861*** (0.077)
Web	0.098 (0.178)	0.039*** (0.002)	0.070 (0.178)
Export	5.829*** (0.039)	0.003*** (0.000)	5.827*** (0.039)
RD	1.873*** (0.091)	0.008*** (0.001)	1.867*** (0.091)
Constant	−9.574*** (0.344)	4.937*** (0.005)	−13.290*** (0.916)
Control industry	Yes	Yes	Yes
Control the city	Yes	Yes	Yes
Observations	194,865	194,864	194,864
R-squared	0.284	0.045	0.284

Note.*** indicate significant at the 1% levels, respectively, and the numbers in parentheses are standard errors.

Research Conclusions and Implications

Research Conclusions

How firms adapt to the changes in consumer demand diversity brought by the global digital economy, is important. Although the literature on the relationship between customized services and firm performance is extensive, some issues have not yet been clearly explained. In this paper, we investigate the impact of customized services on firm performance based on data from Taobao.com in China. The results show that customized services contribute to firm performance. There is heterogeneity in the promotion effect of customized services on firm performance among different industries, and this effect ranges from large to small in the raw material, consumer goods, and industrial goods industries. In addition, customized services enhance firm performance by increasing consumer satisfaction. This study further enriches the literature related to the field of customized services and firm performance from a platform perspective and draws the affirmative conclusion that customized services promote firm performance. This provides some ideas for the next step to expand and deepen the literature in this area. Also, the data sources in this paper provide some useful references for enriching the data sources of research on customized services and broadening the direction of data sources for research in this field. In addition, the results of the analysis of industry heterogeneity in this paper are a nice addition to the literature in the field of customized services and firm performance. It is also a creative attempt to use user satisfaction, user repeat purchase behavior, and user rating as proxy variables in the process of selecting mediating variables in the examination of the mechanism of action of the effect of customized services on firm performance.

However, there are some limitations in this study. First, this paper only examines online customized services based on Taobao and do not cover offline customized services, but offline firms are now also actively adopting customized services as a business strategy, and there may be differences in the impact caused by online customized services and offline customized services on firm performance, and even the mechanism of action may be different. In particular, the impact of offline customized services on firm performance is also influenced by the distance between consumers and firms. Second, it should be observed that the core explanatory variable of this paper, customized service, is a very complex concept, and despite the corresponding efforts to quantify the metrics, this paper still ignores other dimensions of the variable measuring customized service, such as the pattern and efficiency of customized service, among other factors. Customization in this paper is not a continuous variable measuring the degree of customization, but a binary 0 to 1 variable, and there is a significant difference between these two variables. Continuous variables may be able to draw more valuable conclusions, and the use of continuous variables allows the study to derive the magnitude of the change in the level of firm performance caused by the degree of change in the level of firm customization services. Third, limited by the availability of data, only the Taobao platform was selected as the sample for this paper. In practical terms, whether there are significant differences in the impact and mechanism of action of customized services on firm performance across different platforms of the same type and between different types of platforms are questions that need to be further verified through empirical evidence. Therefore, in future research, it is recommended to explore the differences in the impact of online customized services and offline customized services on firm performance from the perspective of comparing online and offline. Also, data will be further enriched to study the impact of customized services on firm performance from the perspective of the degree of customized services by obtaining continuous variables about firm customized services. Moreover, data on customized services from different platforms will be collected for comparative analysis among different platforms.

Theoretical Implications

In the rapidly growing digital economy, it is becoming common for firms to use customized services to deliver their products and services. This study theoretically analyzes the relationship between customized services and firm performance and provides an analysis of the mediating role of industry heterogeneity and consumer satisfaction. It makes three main theoretical contributions. First, it reveals the positive effect of customized services on firm performance. The impact of customized services on firm performance has received attention from scholars, but there are still issues that need clarification. For example, there is no unanimous conclusion on whether the effect of customized services on firm performance is one of positive promotion or negative inhibition. At the same time, due to the difficulty of obtaining data on customized services, there is a lack of empirical examination of the relationship between customized services and firm performance through data. This paper focuses on this relationship, proposes research hypotheses through theoretical analysis, and empirically tests these hypotheses with the help of Taobao’s firm data. It finds that customized services make a positive contribution to firm performance, which complements the literature studying the relationship between customized services and firm performance.

Second, this paper identifies industry heterogeneity in the contribution of customized services to firm performance. Because there are differences among industries, the magnitude of the effect of customized services on firm performance may also vary by industry. This study explores industry heterogeneity based on existing research and finds that the impact of customized services on firm performance varies significantly across industries, with the degree of impact ranging from large to small in the raw materials, consumer goods, and industrial goods industries.

Third, this paper finds a mediating role of consumer satisfaction between customized services and firm performance. There literature contains less research on the path of the role of customized services in influencing firm performance in terms of theoretical and empirical tests. Some literature has explored the relationship between consumer satisfaction and firm performance and concluded that the former promotes the latter. However, empirical studies on the relationship among customized services, consumer satisfaction and firm performance are lacking. In this paper, theoretical analysis assumes that consumer satisfaction mediates the impact of customized services on firm performance and that customized services contribute to the improvement of firm performance by increasing consumer satisfaction. The empirical analysis based on the econometric model confirms the mediating effect of consumer satisfaction.

Managerial Implications

This study makes three main managerial contributions. First, we suggest that firms pay attention to customized services for firm development. In his economic development theory, Schumpeter points out that innovation help improve firm performance, and in the process of firm development, firms need to catalyze transformation and upgrading through innovation. Our findings suggest that the innovative behavior of customized services will contribute to firm performance by improving consumer satisfaction, suggesting that firms should pay attention to customized services in the process of innovation. Although studies note that customized services may lead to increased costs due to the loss of scale, the findings of this paper tell us that firms still need to pay attention to customized services because their benefits will compensate for or even exceed the increased cost.

Second, firms in the raw materials and consumer goods industries should pay more attention to customized services. In these industries, consumers have a greater demand for individualized products, while in industrial goods, they place more emphasis on the level of product standardization, and the demand for individualized industrial goods is weaker than that for diversified raw materials and consumer goods. This in turn leads to the variability of the effect of customized services on the improvement of corporate performance. Therefore, in management practice, firms in the raw materials and consumer goods industries should consider customized services an important business strategy. They should establish customized service platforms based on their own industries, increase investment in customized service technology, and strive to improve their customized services.

Third, good communication with consumers is crucial. Consumer satisfaction is an important path for customized services to promote firm performance, and firms should maintain good communication with consumers. Specifically, firms need to not only allow consumers to participate more in the product production process but also establish a consumer feedback platform or channel. Users can participate in the design and production of products to learn more about the firm. With the help of the feedback platform, consumers can give their opinions or suggestions on the use of the product to the firm in a timely manner, which will help the firm improve and perfect the product.

Footnotes

Author Contributions

Yuanbin Xu: Conceptualization, Methodology, Writing—Original draft preparation; Fucai Lu: Supervision, Writing—Reviewing and Editing; Quan Xiao: Conceptualization, Validation; Yuanbin Xu, Yuchen Wang: Writing—review & editing; Quan Xiao, Yuchen Wang: Writing—review & editing.

Declaration of Conflicting Interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Natural Science Foundation of China [grant numbers: 72201114, 71861014, and 72063010], Humanities and Social Sciences Youth Program of the Ministry of Education [grant number 22YJC790148], Social Science Foundation of Jiangxi Province Project [grant number 22Y36], and Innovative projects for postgraduate students in Jiangxi Province [grant number YC2022-s471].

ORCID iD

Yuanbin Xu

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