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The paradox of aging population and firm digital transformation in China

BMC Geriatrics volume 24, Article number: 705 (2024) Cite this article

Abstract

Although a number researchers have acknowledged that the aging population inhibits firm digital transformation, others find it promoting digital transformation in some firms. As the relevant literature to clarify such paradox is still scare, this paper wants to fill the gap regarding the labor cost theory, the capital-skill complementarity hypothesis, and the human capital externality theory. Based on the empirical tests of Chinese A-share listed companies from 2001 to 2022, this study detected a U-shaped relationship between the aging population and digital transformation. In terms of the institutional environment, higher marketization strengthens the U-shaped relationship by making the slopes on either side of it steeper. However, higher minimum wage levels weaken the U-shaped relationship. In terms of firm strategy, firms with stronger marketing capabilities strengthened the U-shaped relationship. However, firms with higher customer concentration weakened the U-shaped relationship. Overall, we enriched scholarly understanding of the impact of the aging population on digital transformation and demonstrated the dual potential impact of aging populations. Instead of assuming they are detrimental to the economy and society, positive contributions in the form of innovation and progress for companies can be detected.

Highlights

• Aging is an unavoidable demographic problem in China, with very complex social roots behind it.

• Increased aging has thwarted China's digital economy, but aging is not the only negative impact on digitization.

• The digital transformation process of Chinese listed companies is distinctly heterogeneous in the face of aging shocks.

• By taking into account the institutional environment and strategic development, Chinese companies can seek a proactive path of development to adapt to aging, and even accelerate digital transformation.

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Introduction

The life expectancy of the Chinese population has significantly increased over the past few decades due to improvements in medical care and living standards [1, 2]. However, China is also entering an aging society [1]. According to China’s Fifth National Population Census (2000), individuals aged 65 and above accounted for about 7% of the total population [1]. Data from China’s National Bureau of Statistics indicates that this aging trend is becoming increasingly apparent. As of February 2023, people in this age group accounted for about 15% of the country’s total population (C.-C. Lee et al., [3]. Figure 1 presents data from China’s fifth, sixth, and seventh national censuses, illustrating the aging trend of China’s urban population over the past 20 years. Some scholars predict that this percentage will reach 28% by 2050 [4].

With an aging population, the labor supply in Chinese society is gradually decreasing, posing a long-term threat to business growth [5]. However, digital transformation—defined as the full integration of business management with digital technology through the efficient transfer of information and the optimal allocation of resources, thereby significantly increasing productivity—is an important means for companies to mitigate external risks [6]. Digital transformation is driving Chinese enterprises toward comprehensive upgrades in intelligence and informatization [7]. This transformation may partially offset the negative effects of an aging labor market.

Fig. 1
figure 1

China’s fifth, sixth and seventh national censuses

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Does an aging population necessarily impede digital transformation in enterprises? The answer is not necessarily; this question requires more empirical testing. Previous literature that supports the view that aging has a negative impact suggests that older people are less able to understand digital technologies [6]. Similarly, some studies indicate that firms may reduce their investment in innovative life service technologies to meet the needs of older customers [8]. Additionally, an aging population can reduce per capita consumption and return on capital, further inhibiting business innovation [9].

In contrast, some scholars argue that aging can have a positive impact due to a feedback mechanism [10]. They believe that population aging forces companies to undergo digital transformation, for example, by encouraging them to enhance employee training and hire highly educated individuals [11, 12]. Other scholars point out that the innovation effect of population aging is greater than its cost effect, asserting that digital transformation is better driven by skilled and experienced employees, including older employees [13]. Indeed, some studies show that population aging pushes companies to invest in R&D for innovation [14, 15]. Thus, we argue that the impact of population aging on firms’ digital transformation may be more complex and nonlinear than previously thought.

Current research primarily explores the negative or positive linear effects of population aging on digital transformation, with few studies synthesizing and considering both scenarios. Our study attempts to fill this gap by developing a nonlinear model. Specifically, we propose and validate a nonlinear relationship model to explain the relationship between population aging and digital transformation from the perspectives of labor cost theory, the capital-skill complementarity hypothesis, and human capital externalities. We analyze the boundary mechanisms by which population aging affects digital transformation, considering institutional environment and corporate strategy.

Our empirical tests involve Chinese A-share listed companies from 2001 to 2022, revealing a U-shaped relationship between population aging and digital transformation. From the perspective of the institutional environment, we find that in regions with a higher degree of marketization, the slopes on both sides of the U-shaped relationship curve are steeper, and the vertex shifts to the left. Conversely, in regions with higher minimum wage levels, the slopes on both sides of the U-shaped relationship curve are flatter, and the vertex shifts to the right.

From the perspective of corporate strategy, our study also finds that higher levels of marketing capability make the slopes on both sides of the U-shaped relationship curve steeper, with the vertex shifting to the left. Conversely, higher levels of customer concentration make the slopes on both sides of the U-shaped relationship curve flatter, with the vertex shifting to the right.

The theoretical contributions of our study are as follows. First, we challenge the assumption in previous literature that there is a linear relationship between population aging and digital transformation. Our findings extend and complement the literature by providing a more nuanced assessment of the micro impacts of population aging. Second, our model incorporates factors such as the degree of marketization, minimum wage levels, marketing capability, and customer concentration, analyzing their moderating effects on the U-curve. This extends the boundary mechanism of the impact of population aging on digital transformation and enriches the context for the application of labor cost theory, the capital-skill complementarity hypothesis, and human capital externalities. Third, our findings help policymakers better understand and assess the scope of the impact of population aging in China, informing corporate managers’ strategic decisions and digital transformation efforts in the face of population aging and labor cost shocks.

Theory and hypothesis development

The U-curve relationship between the aging Chinese population and firm digital transformation

According to the labor cost theory, in the long-term, an aging population can reduce staff availability for companies, and thus increase labor costs [16]. As the theory of finite resources postulates, rising labor costs crowd out resources for digital transformation [17]. At the same time, the aging population has also created a surge in the Government’s financial pressure [18, 19]. This exposes companies to more significant regulatory pressure and tax burdens and impacts adversely the amount of resources for digital transformation [19]. However, under the active aging perspective and based on the capital-skill complementarity hypothesis, some scholars have suggested that the aging population actually facilitates corporate digital transformation [20,21,22].

Griliches, a Harvard-based Israeli economist, first proposed the capital-skill complementarity hypothesis in 1969. This hypothesis analyzes the substitutable relationship between factors of production from the perspective of labor factor flows and labor skill premiums [23]. In the context of an aging population, the substitution effect of capital for low-skilled labor is more pronounced, which increases the share of high-level talent and facilitates digital transformation [24, 25]. Previous studies have shown that the development of artificially intelligent production technologies has prompted companies to replace low-level, low-skill posts with digital technology [26]. Similarly, studies have shown that the increase in the aging population has led to a relative decline in the youth labor force, which has accelerated the replacement of routine jobs with mainly repetitive tasks by digital technologies [27, 28].

The human capital externality theory also suggests that an aging population leads to a decrease in the effective workforce and forces companies to improve the quality of their human capital and recruit well-educated, highly skilled employees, thus facilitating digital transformation, whereas social interactions facilitate the exchange of information and create learning opportunities within the organization [29, 30]. Clustering skilled and well-educated employees within a company boosts employee interaction and can generate more active ideas, more opportunities for innovation, and more significant economies of scale [31, 32]. Furthermore, as labor costs rise, companies tend to recruit high-level talent for additional revenue [33].

What exactly is the relationship between an aging population and enterprise digital transformation? The answer is complex and not strictly negative or positive. Scholars who argue that population aging negatively impacts enterprise digital transformation believe that as the population ages, the transformation process is hampered [34, 35]. However, businesses are adaptable to their environment. They will continue to adjust to societal aging and higher labor costs [36]. This adaptability means that the negative impact of aging on enterprises will gradually diminish over time [37]. In other words, the relationship between population aging and the digital transformation of enterprises is not strictly linear. Instead, the negative impact of population aging on enterprise digital transformation may gradually decline as the population continues to age [38].

In addition, scholars who support the positive impact of an aging population argue that this effect is not strictly linear [39]. They suggest that companies can adapt to an aging society by training a portion of their older workforce to master digital technologies [40]. This training can reduce the workload of older employees and increase their productivity [41]. Moreover, experienced older employees who master digital technology can effectively pass on their knowledge, creating more benefits for the enterprise [42]. This transfer of expertise is conducive to the digital transformation of the enterprise.

In summary, the impact of population aging on enterprise digital transformation is not a simple, strictly linear relationship. Initially, as the population ages, the degree of digital transformation in enterprises is hindered and negatively impacted. However, this negative effect gradually decreases over time. As companies adapt to an aging society, their digital transformation efforts improve. Consequently, the positive effect of an aging population on digital transformation gradually increases.

Based on the above analysis, we propose our first hypothesis (H1): a U-shaped relationship exists between the aging population and enterprise digital transformation in China.

The moderating role of minimum wage

Previous studies have shown that increases in minimum wage can have an incentive, an over-protection, and a perceived unfairness effect on employees [43,44,45]. Low increases in minimum wage create an incentive effect according to the efficiency wage theory, which postulates a positive relationship between a worker’s income and their efficiency, and that higher wages boost productivity due to increased effort at work and motivation (especially for low-skilled workers) to upskill and train [46, 47]. Therefore, an increase in the minimum wage helps mitigate the dampening effect of an aging population on digital transformation in the short term. However, minimum wages trigger negative impacts when increases exceed a specific size [48,49,50]. According to the relevant provisions of Labor Contract Law, enterprises are required to pay compensation for the dismissal of employees, whose amount is directly linked to the minimum wage standard [48]. The minimum wage reduces the cost of employee advocacy, increases the cost of dismissal for companies, and increases job stability. Companies cannot easily let even poor-performing employees go, which dampens others motivations [51]. In summary, we believe that minimum wage increases will mitigate the negative impact of aging on the digital transformation of businesses in the short term. In the long run, however, the minimum wage will also mitigate the positive impact of aging on firms’ digital transformation. This dual effect will flatten the slopes on both sides of the U-curve, prolonging the negative effects of aging and delaying the onset of its positive effects.

Based on the above analysis, we propose our hypothesis (H2a): higher levels of minimum wage decrease the slopes on both sides of the U-curve and move the vertex of the curve to the right.

The moderating role of marketization

At the institutional environment, the extent of government intervention in the economy and the level of development of formal or informal institutions varies considerably across different regions of China [52]. In more market-oriented areas, government intervention is relatively low [53]. Managers’ business behavior and market activities are less regulated by the government, and a high level of trust develops in the region, alongside a relatively high level of financial development and foreign investment [53].

According to signaling theory, information spreads faster in more market-oriented areas, and the more market-oriented a region, the more sensitive firms are to an aging population. Therefore, the negative effects of the early stages of an aging population are more likely to be transmitted to the digital transformation, as firms in more market-oriented regions are more vulnerable to aging population shocks [54, 55]. However, the greater the degree of marketization of the region in which the enterprise is located, the greater the autonomy of the enterprise to engage in business management activities [56].

When the aging population increases to a certain level, companies cannot cope with labor cost shocks by cutting digital transformation investments [57, 58]. At this point, it is easier for firms in regions with a higher degree of marketization to adjust their corporate strategies, which motivates them to implement digital transformation [58, 59]. In contrast, less market-oriented regions are characterized by relatively high levels of government intervention in the economy, poorer financing and market environments, weak rule of law, and non-transparent government decision-making processes [59]. Faced with the pressure of an aging population, governments often increase corporate taxes to alleviate the fiscal crisis, further hindering digital transformation [60, 61]. In summary, we believe that higher levels of marketization will amplify the negative impact of aging on the digital transformation of enterprises in the short term. However, in the long run, higher levels of marketization will also enhance the positive impact of aging on firms’ digital transformation. This dual effect will steepen the slopes on both sides of the U-curve. Furthermore, higher levels of marketization will shorten the duration of the negative impact of aging and accelerate the onset of its positive impact.

Based on the above analysis, we propose our hypothesis (H2b): higher levels of marketization increase the slopes on both sides of the U-curve and move the vertex of the curve to the left.

The moderating role of marketing capabilities

Mishra and Modi [62], based on stakeholder theory, resource base theory, signaling theory, and agency theory, showed that the stronger the marketing capability, the more rapid the flow of information and the more significant the influence of the external environment on corporate strategy [62]. A high level of marketing capability provides a more convenient signaling channel for enterprises, which in turn improves the efficiency of information transmission [55, 63]. While in the short term, companies are more likely to suffer the negative impacts of an aging population on their digital transformation strategies. In time firms with more substantial marketing capabilities increase their hiring of high-level human capital by delivering practical information to the labor market in a more targeted manner, which in turn increases the aggregation of high-level human capital and facilitates the digital transformation of firms [64]. Similarly, companies with strong marketing capabilities are able to respond to the impact of changes in the external environment by quickly realizing the allocation of internal resources and facilitating digital transformation [65]. In summary, we believe that higher levels of marketing capability will amplify the negative impact of aging on firms’ digital transformation in the short term. However, in the long run, higher levels of marketing capability will also enhance the positive impact of aging on firms’ digital transformation. This dual effect will steepen the slopes on both sides of the U-curve. Additionally, higher levels of marketing capability will shorten the duration of the negative impact of aging and accelerate the onset of its positive impact.

Based on the above analysis, we propose our hypothesis (H3a): higher levels of marketing capability increase the slopes on both sides of the U-curve and move the vertex of the curve to the left.

The moderating role of customer concentration

In traditional business relationships, there is often significant information asymmetry between the company and the external environment due to organizational boundaries [66]. Companies cannot accurately estimate customer loyalty, thus leading to higher trust costs and reverse shuffling problems [67]. In the formative stages of an aging population, enterprises are willing to spend more energy on maintaining extensive customer relationships to cut costs and establish a relationship-trust-based operational model with big customers, which in turn improves market resilience through information sharing [68, 69]. Thus, to a certain extent, they can mitigate the rising costs caused by an aging population, which in turn mitigates the dampening effect of an aging population on a firm’s digital transformation. However, long-term trust in big customers by firms with high customer concentration reduces the firm’s ability and sensitivity to changes in market information [70, 71]. Moreover, according to the transaction cost theory, enterprises with higher customer concentration have smaller bargaining power and are prone to be encroached upon by the interests of big customers or even lack of independence [72, 73]. Losing big customers undermines operational efficiency; arguably, enterprises with high customer concentration also face a higher business risk [73]. In summary, we believe that higher customer concentration will mitigate the negative impact of aging on enterprise digital transformation in the short term. At the same time, higher customer concentration will also reduce the positive impact of aging on digital transformation in the long run. This will flatten the slopes on both sides of the U-curve. Additionally, higher customer concentration will prolong the negative impact of aging and delay the positive impact of aging.

Based on the above analysis, we propose our hypothesis (H3b): higher customer concentration decreases the slopes on both sides of the U-curve and moves the vertex of the curve to the right.

Research design

Sample selection and data source

Chinese A-share companies listed on the Shanghai and Shenzhen stock exchanges from 2001 to 2022 were selected as the study sample. We manually collected information on the aging population in provincial administrative units in mainland China from the China Statistical Yearbook [74]. We manually collected information on aging population from China Family Panel Studies (CFPS) for city-level administrative units in mainland China for robustness testing [4, 75]. The information on the degree of digital transformation of enterprises come from the Digital Transformation Index of Chinese Listed Companies published by the National Finance Discipline Team of Guangdong Institute of Finance in conjunction with the editorial board of Research in Financial Economics [53, 76]. Data on minimum wage standards are from the China Statistical Yearbook [77]. Data on the degree of marketization come from the China Sub-Provincial Marketization Index Report compiled by Fan et al. [54, 78]. Marketing capabilities and customer concentration data were obtained from the CSMAR database [79]. Control variable data were obtained from the CSMAR database [53, 58]. We then selected the sample as follows: (1) we excluded financial, insurance, and securities listed companies with special operating characteristics and accounting systems; (2) we excluded special treatment companies coded as ST (company’s loss for 2 consecutive years) and *ST (company’s loss for 3 consecutive years); (3) we excluded observations that do not disclose geographic information about donations and revenues; and (4) we excluded samples with missing data. Our final sample consisted of 44,418 observations. To reduce the influence of outliers, all of the continuous variables were winsorized at the 1% and 99% levels.

Measures

The independent variable of this paper is the degree of aging (Aging). Referring to previous studies, this paper used the percentage of the population over 65 years old in each province from China Statistical Yearbook data to measure this variable [74]. In the robustness test, the degree of the aging population (Aging0) was measured using the share of the population aged 65 years and over as a proportion of adolescents [80]. In addition, we measure aging using city-level old-age population ratios and this data is from CFPS (Aging_city) [75].

The dependent variable of this paper is the degree of digitalization (Digitaltrans). Concerning previous studies, this paper adopted the Digital Transformation Index of Chinese Listed Companies, jointly published by the National Finance Team of Guangdong Institute of Finance and the Editorial Board of Research in Financial Economics, to measure the degree of digital transformation (Digitaltrans) [7, 76]. The National Research Center for Finance at the Guangdong Institute of Finance in China has launched a research report on the evaluation of the digital transformation index of Chinese listed companies. The report focuses on analyzing the digital transformation status of 2,906 listed enterprises in Shanghai and Shenzhen A-shares from 2016 to 2020. The index is constructed using big data to identify “digital transformation” related words in the text of listed companies’ annual reports and innovatively uses dual quantitative tools—text analysis and factor analysis—to portray the intensity of digital transformation for each listed company in the corresponding year. The enterprise digital transformation rating indicators in the report are analyzed according to the criteria of “three zones and nine levels” (AAA (highest), AA, A, BBB, BB, B, CCC, CC, C (lowest)). The database is managed by the National Finance Discipline Team of the Guangdong Institute of Finance and the Editorial Board of Research in Financial Economics. The larger the value of the Digital indicator, the higher the enterprise’s digital transformation degree. In the robustness test, referring to previous studies, this paper adopted the proportion of intangible assets to total assets to measure the degree of digital transformation (Digitaltrans0) [81].

Minimum wage is one of the moderating variables in this paper (Miwage). Referring to previous studies, this paper searched the official government websites, such as the provincial human resources and social security departments. It manually organized the data of minimum wage standards in the local areas [77].

One of the moderating variables in this paper is the degree of marketization (marketization or MK in Table 2). The degree of marketization indicates the degree of marketization in the province where the enterprise is located, and the marketization index published by Fan Gang’s team was used as a measure of this variable [78, 82].

One of the moderating variables in this paper is customer concentration (Customer_concentration or CC in Table 2). Referring to Patatoukasl and Irvine et al., this paper used the proportion of sales revenue of the top five customers to the total sales of the firm to measure customer concentration [73, 83]. The higher the percentage of this indicator, the more the firm relies on its top five customers and the more its business is concentrated in that customer group.

The moderating variable in this paper is marketing capability (Marketing_capability or MC in Table 2). In this paper, the stochastic frontier model (SFA) was used to measure the marketing capability. The stochastic frontier production function reflects the functional relationship between the input mix and the maximum output under the specific technical conditions and the given combination of production factors [62]. In this paper, sales revenue was taken as the output index, and sales expenses, intangible assets and customer relationship management were taken as the input index of marketing capability [62]. Among them, sales expense reflects marketing expenditure; Intangible assets reflect brand effect, intellectual property and goodwill, etc. The level of CRM reflects the amount of sales obtained from repeat customers [62]. Based on the above analysis, this paper build a stochastic frontier model of marketing capability: sales revenue = f (sales expenses, intangible assets, customer relationship management). Then, through the regression analysis of the above model, the non-negative inefficiency item in the model was calculated, and then the exponential operation was carried out to obtain the value of marketing capability.

Referring to previous studies, in order to control the influence of other factors, this paper started from the enterprise level, and chose enterprise Size, Growth, Leverage and Cashflow as control variables [57, 70].

Empirical model specification

The following model was developed in this paper for Hypothesis 1 presented in the previous section, as shown in Eq. 1. We used a fixed effects model for the regression:

$$Digitaltrans_{it}\;=\;\beta_0+\beta_1Aging_{it}\;+\:\beta_2Aging_{it}^2+\sum_{j=1}^n\beta_{j+2}Controls_{it}+YEAR+FIRM+INDUSTRY+u_i+\varepsilon_{it}$$
(1)

In this paper, the following models were developed for hypotheses 2a and 2b presented in the previous section, as shown in Eqs. 2 and 3.

$$Digitaltrans_{it}=\beta_0+\beta_1Aging_{it}+\beta_2Aging_{it}^2+\beta_3Marketization_{it}+\beta_4Aging_{it}\times Marketization+\beta_5Aging_{it}^2\times Marketization+\sum_{j=1}^n\beta_{j+5}Controls_{it}+YEAR_{it}+FIRM+INDUSTRY+u_i+\varepsilon_{it}$$
(2)
$$Digitaltrans_{it}+\beta_0+\beta_1Aging_{it}+\beta_2Aging_{it}^2+\beta_3Miwage_{it}+\beta_4Aging_{it}\times Miwage+\beta_5Aging_{it}^2\times Miwage+\sum_{j=1}^n\beta_{j+5}Controls_{it}+YEAR+FIRM+INDUSTRY+u_i+\varepsilon_{it}$$
(3)

In this paper, the following models were developed for hypotheses 3a and 3b presented in the previous section, as shown in Eqs. 4 and 5.

$$Digitaltrans_{it}=\beta_0+\beta_1Aging_{it}+\beta_2Aging_{it}^2+\beta_3Marketing\_capability_{it}+\beta_4Aging_{it}\times Marketing\_capability+\beta_5Aging_{it}^2\times Marketing\_capability+\sum_{j=1}^n\beta_{j+5}Controls_{it}+YEAR+FIRM+INDUSTRY+u_i+\varepsilon_{it}$$
(4)
$$Digitaltrans_{it}=\beta_0+\beta_1Aging_{it}+\beta_2Aging_{it}^2+\beta_3Customer\_concentration_{it}+\beta_4Aging_{it}\times Customer\_concentration+\beta_5Aging_{it}^2\times Customer\_concentration+\sum_{j=1}^n\beta_{j+5}Controls_{it}+YEAR+FIRM+INDUSTRY+u_i+\varepsilon_{it}$$
(5)

Results

Descriptive statistics and correlation analysis

Table 1 showed descriptive statistics of variables.

Table 1 Descriptive statistics
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This paper reported and observed the correlation coefficient matrix between the two variables to test whether there is a strong correlation between the variables. It was easy to find that the correlation between the explained variables and the explanatory variables in Table 2 was significant, which initially confirmed the rationality of the main regression. Moreover, the absolute values of correlation coefficients among independent variable, dependent variables, and moderating variables were all less than 0.7, so a strong correlation between variables is excluded, indicating that there is no severe correlation between variables [84].

Table 2 Correlation coefficient matrix
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The impact of the population aging on enterprise digital transformation

In Table 3, Model 1 was the regression results without adding individual, industry, and annual controls. Model 2 was the regression result with individual, industry and year controls added. From the results of Model 1 and Model 2, it could be seen that the coefficient of the linear term of aging population was significant, and the coefficient of the quadratic term was significantly positive, so there is a U-shaped relationship between aging population and digital transformation of enterprises, and H1 was verified.

Table 3 The impact of the aging population on the digital transformation
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Further, we conducted additional research after discovering that some firms operate on a national scale, conducting nationwide recruitment and providing products and services across the country. Consequently, we replaced the independent variable with the national aging variable. We obtained a coefficient of 0.758 for the national aging primary term, which is significant at the 0.01 level. The coefficient for the quadratic term is 0.048, also significant at the 0.01 level. This suggests a U-shaped relationship between aging and the digital transformation of firms, proving that our preliminary findings are robust.

Minimum wage moderating

Further, this paper tested the moderating roles of the institutional environments. Model 1 in Table 4 demonstrated the coefficients in Eq. 3. Referring to the previous analysis [85], \(\beta_5\) showed to be significantly negative. This indicated that the slopes on both sides of the curve decrease, and the shape of the curve flats out, and H2a was proven. And, we found that \(\beta_1\beta_{5-}\beta_2\beta_4\) <0, thus, the curve’s vertex moved to the right.

Table 4 The moderating roles of institutional environments
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Marketization moderating

Similarly, Model 2 demonstrated the coefficients in Eq. 2. \(\beta_5\) was significantly positive. This indicated that the slopes on both sides of the curve increase, and H2b was proved. And, we found that \(\beta_1\beta_{5-}\beta_2\beta_4\) <0, thus, the curve’s vertex moved to the left.

To test the concavity of the adjusted model, we perform the second derivative of Eqs. 3, 4 and 5. The second-order derivative is given in Eq. 6 as:

$${Digitaltrans}"_{it}=2\beta_2\times Moderating\;variables\;\left(6\right)$$
(6)

The second-order derivatives of all the moderated effects models were calculated to be consistently greater than zero, as shown in the last row of Table 4. This indicates that the curves of the moderated effects models are concave functions.

Marketing capability moderating

Further, this paper tested the moderating effects of corporate strategies. As Model 1 in Table 5 demonstrated, the coefficients in Eq. 4. \(\beta_5\) was significantly positive, indicating that the slopes on both sides of the curve increase, and the shape of the curve flats out. Then,  \(\beta_1\beta_{5-}\beta_2\beta_4\) <0, so the curve’s vertex is moved to the left, and H3a was proved.

Table 5 The moderating roles of corporate strategies
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Customer concentration moderating

Similarly, Model 2 in Table 5 demonstrated the coefficients in Eq. 5. \(\beta_5\) was significantly positive, indicating that the slopes on both sides of the curve decrease. Then, \(\beta_1\beta_{5-}\beta_2\beta_4\) >0, so the curve’s vertex was moved to the right, H3b was proved.

The second-order derivatives of all the moderated effects models were calculated to be consistently greater than zero, as shown in the last row of Table 5. This indicates that the curves of the moderated effects models are concave functions.

Endogeneity - instrumental variable approach

This part of the study addresses the endogeneity issue in the empirical analysis [86]. Population aging has deep social roots and is a typical macro-demographic issue. However, the development of population aging may be slightly influenced by companies undergoing digital transformation, as they provide society with increasingly high-quality services and products. This leads to a more inclusive society with better social and medical conditions. Therefore, there may be an endogeneity problem, i.e., a slight reverse causality, between digital transformation and population aging. Additionally, certain omitted variables, such as the increase in economic development and inflation, that affect both population aging and firms’ digital transformation, may also cause another endogeneity problem, a spurious causality that is effected by additional variables that affect the independent and dependent variables, such as social, economic, etc.

This section aims to further identify the causal relationship between population aging and digital transformation by introducing an instrumental variable approach to address possible reverse causality and omitted variable bias. Drawing on previous studies, we selected the reverse-coded historical birth rate (Birth rate) [87] and life expectancy per capita (Lifetime) [88] as instrumental variables for endogeneity testing. These two instrumental variables were chosen because, on one hand, historical birth rate and life expectancy per capita do not affect firms’ digital transformation in the current period, and on the other hand, they are highly correlated with the original independent variables.

Table 6 presents the results of these tests: models (1) and (3) show that historical birth rate and life expectancy per capita are highly correlated with population aging in the first-stage regression. Models (2) and (4) demonstrate that in the second-stage regression, population aging and enterprise digital transformation still show a significant U-shaped relationship. In summary, the endogeneity problem, i.e., reverse causation and spurious relationship, is resolved, and the benchmark regression results in this paper remain robust.

Table 6 Instrumental variable method tests
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Robustness tests

This part of the study attempts to robustly test the results of the benchmark regression by replacing the measures of the independent and dependent variables.

Robustness tests for replacing dependent variable

Referring to previous research, this paper replaced the dependent variable using intangible asset share for robustness testing [81]. As shown in Table 7, in Model 1, the coefficient of the quadratic term of the aging population was significantly positive, which proves that the aging population has a U-shaped relationship with the digital transformation and demonstrates the robustness of the original H1 conclusion. The coefficients in Model 2 indicated that the degree of marketization steepens the curve’s shape. The coefficients in Model 3 suggested that the minimum wage flattens the shape of the curve. The coefficients in Model 4 indicated that marketing ability steepens the curve’s constitution. The coefficients in Model 5 suggested that customer concentration flattens the shape of the curve. All of these findings were the same as the original findings, which proves the robustness of the results H2a, H2b, H3a, and H3b, to a certain extent.

Table 7 Robustness tests for replacing the dependent variable
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Robustness tests for replacing the independent variable

For the dependent variable, referring to previous studies, this paper used the proportion of the population aged 65 to the youth population as a replacement [80]. As shown in Table 8, in Model 1, the coefficient of the quadratic term of the aging population were significantly positive, which proves that the aging population has a U-shaped relationship with the digital transformation of enterprises and demonstrates the robustness of the original H1 conclusion. The coefficients in Model 2 indicated that the degree of marketization steepens the curve’s shape. The coefficients in Model 3 suggested that the minimum wage flattens the shape of the curve. The coefficients in Model 4 indicated that marketing capability steepens the curve’s constitution. The coefficients in Model 5 suggested that customer concentration flattens the shape of the curve. These findings were the same as the original findings, proving the results’ robustness of H2a, H2b, H3a, and H3b, to a certain extent. Table 9 demonstrates, the results of the robustness test for replacing provincial old people by the proportion of city old people, which is consistent with the results of the initial regression, to a certain extent.

Table 8 Robustness tests for replacing the independent variable-aging0
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Table 9 Robustness tests for replacing the independent variable-aging_city
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Conclusions and discussions

The main purpose of this paper is to examine the development and changes in the digital transformation process of enterprises in the context of the aging population in mainland China. That is, this paper explored the impact of the aging population on the digital transformation of enterprises. To achieve this purpose, this paper tested and proved the non-linear relationship of the aging population on the digital transformation based on the complex impact of the aging population on the economy and society. In further research, this paper incorporated four moderating variables at the levels of institutional environments (marketization and minimum wage) and corporate strategies (marketing capability and customer concentration) into the model to explore and examined the moderating effects of institutional environments and corporate strategies on the U-shaped relationship.

Main conclusions

The main findings of this paper are as follows: First, different degrees of an aging population can have differential impacts on enterprise digital transformation. This paper found that the aging population inhibits digital transformation within a specific range. However, an aging population beyond a particular scope promotes digital transformation. The differential impact of the aging population involves two processes: elevating labor costs and changing firms’ human capital structure. The aging population significantly raises labor costs, which causes firms’ hiring costs to rise. According to the theory of limited resources, hiring costs crowd out firms’ resources for digital transformation. In addition, according to the capital-skill complementarity hypothesis, the aging population promotes the substitution of high-skilled employees for low-skilled employees in firms and the recruitment of highly-educated employees. According to the theory of human capital externalities, aggregating high-level talent promotes corporate innovation and generates additional benefits and premiums. By changing the human capital structure of enterprises, the aging population promotes the digital transformation. The aging population and digital transformation are U-shaped nonlinear relationships.

Second, the institutional environments significantly moderate the U-shaped relationship between the aging population and digital transformation. The results of this paper found that the degree of marketization strengthens the U-shaped relationship between the aging population and digital transformation and moves the curve’s vertex to the left. While the degree of marketization exacerbates the negative impact of the aging population in the short term, it promotes the digital transformation process for a long time. Moreover, firms in regions with more marketization upgrade their digital transformation earlier. Minimum wage mitigates the U-shaped relationship between the aging population and digital transformation and moves the curve’s vertex to the right. While the degree of marketization mitigates the negative impact of the aging population in the short run, it hinders the digital transformation process in the long run. Firms in regions with higher minimum wages upgrade their digital transformation later.

Finally, corporate strategies significantly moderate the U-shaped relationship between the aging population and digital transformation. This paper found that marketing capabilities strengthen the U-shaped relationship between the aging population and digital transformation and move the curve’s vertex to the left. While marketing capabilities exacerbate the negative impact of the aging population in the short term, they facilitate the digital transformation process for a long time. Firms with higher marketing capabilities ramp up digital transformation earlier. Customer concentration mitigates the U-shaped relationship between the aging population and digital transformation and moves the curve’s vertex to the right. While customer concentration mitigates the negative impact of the aging population in the short term, it hinders the digital transformation process for a long time. Firms with higher customer concentration ramp up their digital transformation later.

Theoretical contributions

First, this paper breaks away from the previous literature’s hypothesis of a linear relationship between the aging population and firms’ digital transformation. Previous studies have shown that as the population ages, the decrease in labor supply raises firms’ hiring costs, which hinders high-quality growth [46, 89]. However, some scholars have acknowledged the positive effects of the aging population [12, 13, 90]. Similar to previous studies, this paper likewise argues that population aging promotes firms to upgrade their human capital structure in addition to causing cost pressures [12]. Once the innovation effect of the aging population outweighs the cost effect, it facilitates digital transformation by promoting technological innovation [12]. Based on the labor cost theory, capital-skill complementarity assumption, and human capital externality theory, this paper integrates previous ideas, breaks through the linear correlation assumption, and proposes and demonstrates the nonlinear relationship between the aging population and the digital transformation of enterprises. This paper reveals the theoretical mechanism of the digital transformation process of enterprises in the social context of the aging population and, at the same time, helps to provide micro-level evidence for the labor cost theory, the capital-skills complementarity hypothesis, and the human capital externality theory from a new perspective of how aging population affects the digital transformation of enterprises.

Second, based on the social environment and economic background of China’s aging population, this paper further clarifies the controversy about the digital transformation process of Chinese enterprises from the perspective of the institutional environment. This paper incorporates the degree of marketization and minimum wage into the nonlinear relationship model between population aging and firms’ digital transformation, further revealing the boundary mechanism by which the degree of marketization affects the relationship between population aging and firms’ digital transformation. Compared with previous studies on the degree of marketization [53, 54], this paper analyzes the moderating mechanism of the degree of marketization on the nonlinear relationship between aging population and digital transformation of firms, and finds that, at lower levels of aging population, the firms located in the in regions with higher levels of marketization reinforces the inhibition of aging population on firms’ digital transformation. This suggests that, in the short run, firms in more market-oriented regions are more sensitive to the shocks of the aging population. The paper also finds that firms located in most market-oriented areas reinforce the promotion of firms’ digital transformation by an aging population when population aging is at a high level. This suggests that, in the long run, firms located in regions with a higher degree of marketization are more likely to undergo digital transformation in the context of the aging population. Moreover, the moderation of the degree of marketization shifts the vertex of the U-curve to the left, which advances the time for firms to enhance their digital transformation.

Furthermore, in contrast to previous studies on minimum wage [46, 77], this paper analyzes the mechanism by which the minimum wage moderates the nonlinear relationship between an aging population and firms’ digital transformation and finds that, at lower levels of the aging population, firms located in regions with higher minimum wage mitigate the inhibition of digital transformation of firms by the aging population. This suggests that in the short run, firms located in areas with higher minimum wages weaken the negative shock of the aging population. This paper also finds that firms located in regions with higher minimum wages undermine the promotion of firms’ digital transformation by aging population when the aging population is at a high level. This suggests that, in the long run, firms located in regions with higher minimum wages are less likely to undergo digital transformation in the context of the aging population. Moreover, the regulation of the minimum wage shifts the vertex of the U-curve to the right, which delays the time for firms to upgrade their digital transformation. Therefore, this study further enriches and expands the explanatory scope of the impact of the aging population on firms’ digital transformation from the institutional environment perspective.

Finally, this paper incorporates corporate strategy into the model of the impact of the aging population on corporate digital transformation, further revealing the boundary mechanisms of the effects of the aging population on corporate digital transformation. In contrast to previous studies on marketing capabilities [65, 91], this paper analyzes the moderating mechanism of marketing capabilities on the nonlinear relationship between the aging population and firms’ digital transformation and finds that when the aging population is at a lower level, firms with higher marketing capabilities reinforce the digital transformation of firms by aging population inhibition. This suggests that firms with increased marketing capabilities are more sensitive to shocks from the aging population in the short run. This paper also finds that firms with higher marketing capabilities reinforce the facilitation of digital transformation of firms by the aging population when the aging population is at a high level. This suggests that, in the long run, firms with increased marketing capabilities are more likely to undergo digital transformation in the context of the aging population. Moreover, the moderation of marketing capabilities shifts the vertex of the U-curve to the left, which advances the time for firms to enhance their digital transformation.

Furthermore, in contrast to previous studies on customer concentration [73, 79], this paper analyzes the moderating mechanism of customer concentration on the nonlinear relationship between aging population and firms’ digital transformation and finds that, at lower levels of aging population, higher customer concentration in the firms mitigate the inhibition of aging population on firms’ digital transformation. This suggests that in the short run, firms with higher customer concentration weaken the negative shock of the aging population. This paper also finds that firms with higher customer concentration undermine the promotion of firms’ digital transformation by the aging population when the aging population is at a high level. This suggests that, in the long run, firms with higher customer concentration are less likely to undergo digital transformation in the context of the aging population. Moreover, the moderation of customer concentration shifts the vertex of the U-curve to the right, which delays firms’ enhancement of digital transformation. Therefore, this study further enriches and expands the explanatory scope of the impact of the aging population on firms’ digital transformation from a firm strategy perspective.

Management implications

The findings of this paper are conducive for policymakers to better understand and assess the specific process of enterprise digital transformation under the impact of the aging population. In the social context of the aging population, policymakers should fully consider the role of the institutional environment and enterprise strategies and implement tax incentives and other complementary policies to assist enterprises in pursuing digital transformation strategies and moving towards high-quality development. In addition, the findings of this paper are conducive to enterprise managers facing the impact of an aging population or even other crisis scenarios to clarify the optimization direction of enterprise digital transformation and strive to proactively adapt to the aging population society to promote the process of enterprise digital transformation. Enterprise managers should be prepared for crisis in times of peace and prepare for rainy days, actively predict changes in the external environment, actively adapt to fluctuations in the labor market, continue to absorb and receive high-quality resources, and timely adjustments and planning of corporate strategies to promote digital transformation.

First, enterprises should increase the employment of highly educated employees. This is because strengthening employee training and introducing high-level human capital can effectively enhance the innovation output of enterprises and promote high-quality development and sustainable operation [29, 30]. In business practice, enterprises should accelerate digital transformation and upgrading to improve the efficiency of scarce resource acquisition and information transmission to adapt to the social status quo of the aging population rapidly.

Second, enterprises should flexibly formulate their digital transformation strategies based on the characteristics of their institutional environment. The digital transformation process of enterprises varies significantly under different institutional environments [46, 54]. Firms can choose institutional environments conducive to digital transformation, such as marketization processes and minimum wages, through cross-regional mergers and acquisitions and cross-regional recruitment.

Third, firms can choose different corporate strategies to optimize the digital transformation. The improvement of marketing capability can contribute to enhancing the brand image of enterprises, promoting the efficiency of resource transformation and information transmission, and improving the quality of development of enterprises [62]. In specific business management practices, special attention should be paid to the critical role of marketing capabilities. Firms facing a crisis may adopt conservative business strategies and neglect to invest in marketing strategies. However, empirical results show that marketing capabilities facilitate the process of digital transformation of enterprises. Although customer concentration can help firms reduce the cost expenditure of maintaining customer relationships, it dramatically increases the business risk of firms [79]. Moreover, in the digital transformation process in the context of the aging population, enterprises should focus on developing diversification strategies to avoid the phenomenon of over-concentration of customers.

Limitations and future researches

This study encounters some noteworthy procedural and theoretical challenges. First, our sample consists of Chinese A-share listed companies. Although this sample size is large, it may not comprehensively cover the entire population of Chinese firms. Many medium, small, and micro enterprises are not represented due to data unavailability in the database. To address this, future research could conduct on-site surveys and distribute questionnaires to other enterprises, thereby collecting raw data from these smaller firms.

Second, the development and evolution of aging as a demographic issue have extremely complex causes that are not explored in this study. Third, certain firms may engage in substantial cross-regional hiring and operations, which was not considered in our analysis.

Looking ahead, future research efforts could extend and enrich the contributions of this study in several key areas. First, in addition to relying on secondary data, future research could employ methods such as data mining or surveys to conduct more advanced research on aging and the digital transformation of companies. These methods are expected to enhance the measurement of research variables, thereby improving the objectivity and analytical rigor of the study.

Second, incorporating case studies and obtaining primary data through interviews can reveal subtle differences in the development and evolution of enterprise digital transformation in the context of population aging. Third, broadening the research horizon to include foreign samples, such as those from Japan, South Korea, and Germany, for comparative analysis can provide multidimensional insights, thus expanding the breadth and depth of the understanding of aging.

Availability of data and materials

The data that support the findings of this study are available from China Statistical Yearbook, China Family Panel Studies (CFPS), Digital Transformation Index of Chinese Listed Companies, China Provincial Marketization Index Report, China Stock Market & Accounting Research database (CSMAR), but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of National Bureau of Statistics (China Statistical Yearbook), National Finance Discipline Team of Guangdong Institute of Finance in conjunction with the editorial board of Research in Financial Economics (Digital Transformation Index of Chinese Listed Companies), Economic Science Press (China Provincial Marketization Index Report), Shenzhen CSMAR Data Technology Company Limited (CSMAR).

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Acknowledgements

The authors are grateful to all research staff that contributed to the data collection required for this study.

Funding

This study is supported by the Research Project of Macao Polytechnic University (RP/ESCHS-03/2020).

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Authors and Affiliations

  1. Faculty of Humanities and Social Sciences, Macao Polytechnic University, Rua de Luís Gonzaga Gomes, Macao, China

    Hao Wang, Tao Zhang, Xi Wang & Jiansong Zheng

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  4. Jiansong Zheng
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Contributions

Hao Wang: conceptualization, methodology, literature analysis, theoretical analysis, data organization and analysis, writing. Tao Zhang: conceptualization, thematic guidance, literature analysis, theoretical analysis. Xi Wang: validation, data investigation, financial support, writing-review and editing. Jiansong Zheng: conceptualization, literature analysis, data analysis, theoretical analysis, editing and writing.

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Correspondence to Tao Zhang.

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Wang, H., Zhang, T., Wang, X. et al. The paradox of aging population and firm digital transformation in China. BMC Geriatr 24, 705 (2024). https://doi.org/10.1186/s12877-024-05217-5

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Keywords

BMC Geriatrics

ISSN: 1471-2318

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