New ESRI Working Paper No.53

The Impact of Data Activities on Innovation Performancein Service Industries
Fumihira Nishizaki, Masaaki Maruyama, Shuichi Matsuta,
Kyoko Deguchi, Nobuko Hori, Ryo Kitagawa
February 2021

Economic and Social Research InstituteCabinet Office
Tokyo, JapanThe views expressed in “New ESRI Working Paper” are those of the authors and not those of the
Economic and Social Research Institute, the Cabinet Office, or the Government of Japan.
(Contact us: https://form.cao.go.jp/esri/en_opinion-0002.html)

New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

The Impact of Data Activities on Innovation Performancein Service Industries
Fumihira Nishizaki*, Masaaki Maruyama*, Shuichi Matsuta*,
Kyoko Deguchi*, Nobuko Hori*, Ryo Kitagawa*

Abstract
Data-driven decision making, the collection of and dependence on data for management activities, has
expanded swiftly in the United States, and many studies abroad have measured the impacts of the changes
in decision-making processes on productivity. However, in Japan, large-scale data on the use of data by
establishments has been lacking, and the analysis was limited. In this paper, we exploit survey data of the"Japanese Management and Organizational Practices Survey," the first large-scale survey about
management activities by establishments in Japan, carried out by the Economic and Social ResearchInstitute of Cabinet Office in 2017 and 2018. We construct a measure of data activities and estimate its
impact on innovation performance. We find that (i) establishments that use data intensively have a better
performance on innovation outcomes, (ii) the impact of data activities, by which we mean data-driven
decision making and data analytics, on innovation is comparable in magnitude to that of the “good”
management practice, and (iii) the relationship between the intensiveness of data activities and theinnovation outcomes varies depending on industries, establishment size and the degree of decentralizationin decision making. For example, more decentralization in decision making may relate to more frequentinnovations in terms of introducing new products/services and processes.

Keywords: Data, Management, Innovation, Decentralization in decision making*

Currently or formerly in Economic and Social Research Institute, Cabinet Office, Japan.

This paper benefited greatly from discussions with Akihiko Shinozaki, Hidemichi Fujii, Satoru Washio,
Atsushi Ohyama, Ryo Kambayashi, Keiko Sawai, Takuma Kawamoto, Taisuke Kameda, and participantsat seminars for many helpful comments. Any remaining errors rest entirely upon the authors.

1New ESRI Working Paper No.53
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1. Introduction
As information technology develops rapidly, firms can collect a huge amount of digital
data on customers, sales, operations, and the market, structured or unstructured. A numberof analytical tools are available at a reasonable cost, which can transform raw data into
potentially useful information. Though a vague concept, big data is attracting great
attention across all sectors - manufacturing and services alike. Whether named as big dataor not, data and data analytics's increased importance in today’s businesses is undeniable.
In these circumstances, data-driven decision making (DDD), which refers to the practiceof basing decisions on the analysis of data rather than purely on intuition (Provost andFawcett 2013), is expected to improve the performance of firms.
Among many aspects of firm performances, we focus on innovation, both concerning
products and processes, which is a major source of productivity improvements andeconomic growth. This focus is because the effects of DDD have not been examined indetail, especially regarding service-sector innovation. Our novel feature is the use of alarge-scale survey on establishments in road freight transport, wholesale trade, andmedical and other health services to empirically examine how the “data activities,” bywhich we mean data-driven decision making and data analytics, of an establishment isrelated to its propensity to innovate. We also add retail trade (food and beverages) andinformation services to the above list for supplementary exercises. The survey, theJapanese Management and Organizational Practices Survey (JP-MOPS), was conductedby the Economic and Social Research Institute of Cabinet Office (ESRI) in cooperation
with Hitotsubashi University and Kyushu University in 2017 and 2018.
Our empirical results can be summarized as follows. First, establishments that use dataintensively have a better performance on innovation outcomes. Second, the impact of data
activities on innovation is comparable in magnitude to that of the ‘good’ management
practice. Third, the relationship between the intensiveness of data activities and theinnovation outcomes varies depending on industry, establishment size, and the degree ofdecentralization in decision making. For example, more decentralization in decision
making may relate to more frequent innovation in terms of introducing new
products/services and processes.

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This paper is organized as follows. Section 2 describes related literature. Section 3
explains our empirical framework, and Section 4 describes the data used. Section 5
presents empirical results and interpretations. Section 6 concludes our paper.

2. Related Literature
Our present paper is inspired by a few important studies on the relationship between datadriven decision making or data analytics and firms’ productivity. Brynjolsson et al.
(2011), examining 179 large publicly traded firms, find that the productivity of the firmsthat adopt DDD is 5 - 6 percent higher. Bakhshi et al. (2014) use a survey of 500 UK
firms commercially active on the Internet. The result is that a one-standard-deviation
greater use of online data is associated with an 8 percent higher level of total factor
productivity. Brynjolfsson and McElheran (2016) analyze large-scale data from theManagement and Organizational Practices Survey (US-MOPS) for 2010. Brynjolfssonand McElheran (2019), using the US-MOPS for 2010 and 2015, show that DDD isstrongly associated with increased productivity.
The US-MOPS is motivated to explain productivity differences among firms withheterogeneous adoption of management practices and therefore designed to collectinformation such as monitoring, targeting, and incentive practices (Bloom et al. (2013),
Buffingtona et al. (2017)). The use of ICT also plays a key role in explaining differencesin the performance of firms, but a complementary effect between ICT and management
practices should be taken into consideration (Brynjolfsson and Hitt (2000), Bresnahan etal. (2002), for example). This kind of complementarities could also exist in the case of
DDD. Brynjolfsson and McElheran (2016) report that the benefit of DDD adoption
appears to be greater for plants that delegate decision making to frontline workers. The
JP-MOPS consists of similar questions to those in the US-MOPS to make international
comparison possible. This design allows us to explore the relative importance andinteraction between management practice and DDD.
Many of the past empirical studies of innovation are centered on the manufacturing sector.
Those studies typically adopt the knowledge production function framework, where R&D
activities are the important factors of product innovation (Griliches 1979). Due attention
was not paid to the service industries since their low R&D intensity and patent application(Salter & Tether, 2006). In fact, the average ratios of internal R&D expenditures to salesin Japanese firms are 0.37 percent for transport and postal services and 0.44 percent for3

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The Impact of Data Activities on Innovation Performance in Service Industries

wholesale trade, which are negligible compared to 4.18 percent for manufacturing,according to the Survey of Research and Development (Ministry of Internal Affairs andCommunications, 2019). At the same time, however, service industries are very
heterogeneous in terms of innovation patterns. Vence and Trigo (2009), for example,
identify three sub-sector groups based on several innovation attributes: low innovationintensive sectors (LIIS, represented by transport and wholesale trade), technologyintensive and moderately innovation-intensive sectors (TIMIIS, financial services), andknowledge and innovation-intensive sectors (KIBS, business services).
The ICT boom in the late 1990s has helped shed light on the importance of service
innovation. Hempell and Zwick (2008) find that ICT fosters product and process
innovations by facilitating employee participation and outsourcing. Gago and Rubalcaba(2007) examine service firms in Madrid and concludes that ICT and clients–providers
interactions are important factors in facilitating service innovation. Polder et al. (2009)
show that ICT is a driver of innovation in both manufacturing and services. They alsosuggest that R&D investment contributes to product innovation in manufacturing but has
no effect on innovation in service sectors. Spiezia (2011), using cross-country data,
confirms that ICT is an enabler of innovation in both manufacturing and services though
it does not increase their inventive capabilities. Álvarez (2016), based on Chilian data,shows that ICT contributes positively to innovation and productivity for the all-industry
sample and for the services sector sample.
There are only a few empirical studies on the impact of DDD or data analytics oninnovation. Lehrer et al. (2017), conducting case studies of four organizations from theinsurance, banking, telecommunications, and e-commerce industries, suggest that big
data analytics enables service innovation through automated service provision and ITsupported service delivery by human service actors. Wu et al. (2020), using survey dataof 331 firms and patent data of more than 2,000 listed firms, suggest that data analytics
are strongly associated with process innovation and innovation by diverse recombination.
Our paper is most closely related to Niebel et al. (2018). They examine the relationshipbetween firms’ use of big data analytics and their innovative performance based on thesurvey data of 2,706 firms in Germany, of which 1,302 are services (158 retail trade, 129
wholesale trade, 149 transport services, 158 ICT services, for example). The survey
includes a binary measure of product innovation broadly in line with the Oslo Manual by
the OECD and Eurostat. They show that big data analytics is associated with a higher
propensity to innovate and a higher innovation intensity in both manufacturing and4

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The Impact of Data Activities on Innovation Performance in Service Industries

services. However, the positive effect disappears in the case of low IT-specific humancapital.

3. Empirical Framework
3.1 The Impact of Data Activities on Innovation Performance
We first estimate the impact of data activities on innovation performance. We envisagethat data can visualize and organize a significant amount of and many kinds ofinformation and that analyzing them for business activity makes it effective to introduce
new products/services and processes and improve existing ones. Hence, we consider thatthe establishments that use data intensively have a higher frequency of innovation thanthose that do not. We evaluate the impact of data activities on innovation performance by
combining three different estimation methods.
3.1.1 Probit ModelFirst, we follow Niebel et al. (2018) and estimate the following model:
=

1 if
0 if

∗

where

∗

′

(1)

∗

is an observable variable that takes 1 if innovation realizes in establishment i.

∗denotes the latent propensity to realize innovation in establishment i. We estimate it by,

the probit model, Pr
1standard normal distribution function.

∙ is a cumulative

denotes the data score (see Section 4 fordetails) of establishment i, ′ denotes the vector of characteristics of establishment i.
denotes an idiosyncratic error term and is assumed to be identically and independently
normally distributed. In this model, the marginal effect means the propensity to realize
innovation when the data score increases by 1.
3.1.2 Ordered Probit Model
Second, as the choice of the frequency of innovation realization has four options in the
JP-MOPS, we extend the estimation model and use the following ordered probit model:∗=
3 if
where ∗
′
(2)
∗
2 if
∗
1 if
∗
0 if
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The Impact of Data Activities on Innovation Performance in Service Industries

is an observable variable that takes 3 if innovation realizes more than once a year inestablishment i, takes 2 if it arises once a year, takes 1 if it arises once every few years,
and takes 0 if no innovation realizes.
,
are cut points and ∗ denotes
the latent propensity to realize innovation in establishment i.
denotes the data scoreof establishment i, ′ denotes the vector of characteristics of establishment i.
denotes an idiosyncratic error term and is assumed to be identically and independently
normally distributed.
3.1.3 Propensity Score Matching
Third, to evaluate the causal effect of data activities on innovation performance, we adopt
another estimation method, propensity score matching, which is used for assessing theimpact of different treatments (Rosenbaum and Rubin, 1983). It matches units by using
propensity scores and can estimate the average marginal effect of a particular treatment
while controlling for other characteristics. We consider two treatments, numbered 1 and
0. In our model, treatment 1 indicates that establishment i uses data intensively, i.e., thedata score of establishment i is greater than its average within the industry, and treatment
0 indicates that the data score of establishment i is equal to or less than its average within
the industry. The quantity to be estimated is the following average treatment effect,
|
E |
′ ,
1
E |
′ ,
0
E
′ （3）
denotes a response that would have resulted if it had
where E ∙ denotes expectation,
received treatment 1, anddenotes a response that would have resulted if it hadreceived treatment 0.
take 1 if innovation realizes in establishment i, and 0
if innovation realization is unobserved.
′ denotes a balancing score which is afunction of the observed characteristics of establishment i, ′ .
1 if establishment
i is assigned to the experimental treatment and
0 if establishment i is assigned tothe control treatment. The propensity score, the conditional probabilities of assignment
to a particular treatment given a vector of observed covariates, is a balancing score. We
estimate the propensity score by the probit model. This estimation method can extract the
precise effect of data activities on innovation performance because it considers the causal
relationship between data activities’ intensiveness and innovation performance.
3.2 The Contribution of Data Activities to Innovation Performance Compared to that ofManagement

6New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

We then measure the relative contribution of data activities compared to management.
Bloom et al. (2019) compare management practices to other factors that are commonly
considered important drivers of productivity: R&D, ICT, and human capital. They findthat management practices account for more than 20 percent of the variation in
productivity, a similar or higher percentage compared to R&D, ICT, or human capital. In
terms of innovation performance, management is a crucial driver because proper
operational management, such as setting targets or giving incentives, leads employees tomake more efforts and make good performances. This paper attempts to measure data
activities’ relative contribution to innovation performance compared to managerial
activities following Bloom et al. (2019). We estimate the following equation by theOrdinary Least Square (OLS) to calculate the explained share of the 90-10 spread ofinnovation performance by each key factor:′（4）is the variable which takes 4 if innovation realizes more than once a year inestablishment i, takes 3 if it arises once a year, takes 2 if it arises once every few years,
1denotes the data score of establishment i, ′denotes the vector of characteristics of establishment i.
denotes an idiosyncratic error
term and is assumed to be identically and independently normally distributed.
and takes 1 if no innovation realizes.

3.3 The Relationship between Data Activities and Innovation Outcome Depending on
Establishment Characteristics
We finally examine how the relationship between data activities and innovation outcomesis different depending on establishment characteristics. We evaluate the change in themarginal effect in equation (1) by establishment characteristics. We conduct split sample
analyses in equation (1).
We first estimate equations by establishment size and single- or multi-unit firms. Our
hypothesis is the following: large-size establishments deal with various types and manytasks, and they can acquire data from them, which means that they have abundantresources for data analysis. Moreover, firms with a headquarter and branches (i.e., multi-

1

Strictly speaking,is not ration scale but ordinal scale. Since the ordinary leastsquare is better for comparing the relative effect than the ordered probit model and thefrequency of innovation have four different choices, we assign the value toaccording to the frequency and treat it as quantitative data.
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The Impact of Data Activities on Innovation Performance in Service Industries

unit firms) can acquire data or reports between the headquarter and branches. Exchanging
data or reports increases the information for coming up with good ideas, which lead to ahigh propensity to innovate. We test our hypothesis that when large-size establishments
or establishments belonging to a multi-unit firm use data more effectively, this isassociated with larger innovation performance improvement than small-size
establishments or single-unit firms.
We then estimate equations by the degree of decentralization in decision making. The
degree of decentralization is higher if headquarters are less involved in the decision
making of branches. We test our hypothesis that additional data usage is associated withlarger innovation performance improvement in establishments with decentralized
decision making than in those with centralized decision making.

4. Data
We base our analysis on the JP-MOPS. There were two waves: in 2017 and 2018 (Table1). It is an establishment-level survey, and the sample consists of those in Japan with 30
or more employees, derived from the Establishment Frame Database, the census ofestablishments in Japan. The survey was conducted in manufacturing and service
establishments2. The targeted industries were manufacturing and two service industries,
retail trade (food and beverages) 3 and information services in 2017 and other threeservice industries, road freight transport, wholesale trade, and medical and other healthservices in 2018. The 2017 JP-MOPS corresponds to the second wave of the US-MOPS.
The number of respondents was 11,405 in manufacturing, 1,273 in retail trade, 936 in
information services, 1,286 in road freight transport, 3,813 in wholesale trade, and 1,650
in medical and other health services. The surveys include management practice andorganization sections whose structures are the same in the US-MOPS. They have othersections that vary depending on survey years and industries. In manufacturing, the survey
has the uncertainty section. In the service sectors in the 2017 JP-MOPS, the survey has
the data and decision-making section, and other characteristics section including
innovation propensity and competitiveness. In the 2018 JP-MOPS, we followed thesurvey of the service sectors in the 2017 JP-MOPS, and we expanded the data and2
3

We divide industries based on the Japan standard industrial classification.
Hereafter, “retail trade” represents “retail trade (food and beverages).”8New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

decision-making section and added the section of the AI usage. The sections about data
and decision making and other characteristics in the 2018 JP-MOPS have many common
questions to the US-MOPS as well as original questions. In order to obtain information
at two periods, many questions ask responses for both the survey year and five years
earlier, specifically, 2015 (present) / 2010 (recall) in the 2017 JP-MOPS and 2018(present) / 2013 (recall) in the 2018 JP-MOPS. Hereafter, we will focus on service
industries.
4.1 Individual Variables
4.1.1 Innovation Outcomes
Both JP-MOPS have a question about the frequency of innovations. The definition of
innovation broadly follows the Oslo Manual by the OECD and Eurostat. The types ofinnovations are product innovations and process innovations. They are categorized into
five types of innovations: the development and introduction of new products/services(“innovation 1”), the improvement of existing products/services (“innovation 2”), the
new combination of existing products/services (“innovation 3”), the introduction of newprocesses (“innovation 4”), and process improvements (“innovation 5”). The survey asks
how frequently those five types of innovations are realized. The frequencies are divided
into four categories: no innovations (1 is assigned as a score), once every few years (2),once a year (3), and more than once a year (4). We calculate average scores by taking asimple mean. We assign these four categories in the ordered probit model and the OLS
estimation. In the probit model and the propensity score matching, we make binary
indicators by diminishing four categories into no innovations (“d.innovation” = 0) and at
least one innovation (“d.innovation” = 1). In Table 2 (a) and Table 2 (b), among othersummary statistics, we show the means of "d.innovation 1" to "d.innovation 5," i.e., theproportion of establishments with at least one innovation in each innovation type. The
number is higher in retail trade, information services, and wholesale trade.
4.1.2 Data Score
Data score is the most crucial variable in our analysis. We construct it to measure data
activities from the questions in the data and decision-making section of the 2018 JPMOPS. In the data and decision-making section, there are eight questions. We use theresponses from seven questions to define a measure that we call data score 1, which is
the unweighted average of the responses where each response is scored on a 0-1 scale4.

4

To construct the score we exclude question 22-2, which askes the problems about data
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The Impact of Data Activities on Innovation Performance in Service Industries

Hence, data score 1 ranges between 0 and 1 (See Appendix 1-1 for details). In thepropensity score matching, we construct a binary variable that takes 0 if the score is at or
below the industry mean and takes 1 if it is above the industry mean. We call it binary
data score.
We define another data score for comparative analysis among the five industries.
Although there are some different questions in the 2017 JP-MOPS and the 2018 JPMOPS, we construct a score called data score 2 from the same questions in the data anddecision-making section of both surveys (See Appendix 1-2 for details). We use it inanalyzing the contribution of data activities on innovation performance among fiveindustries.
Figure 1 plots the histogram of data score 1 in each of the three industries in 2018. The
histogram is near a normal distribution, and the use of data is most intensive in wholesaletrade. Figure 2 indicates that the score becomes higher in five years in all three industries.
Figure 3 plots the histogram of data score 2 in each of the five industries. Since limited
questions are available, the histogram is relatively non-smooth compared to data score 1.
The industries surveyed in the 2018 JP-MOPS have both data score 1 and data score 2,
making it possible to compare them. The mean of data score 2 is almost at the same levelcompared to that of data score 1, but the standard deviation of the former is higher due
to the limited availability of questions.
4.1.3 Management Score
As previous studies such as Bloom et al. (2019) have already shown, management has atight linkage to productivity, and we consider management is also one of the most criticalfactors influencing the propensity to innovate. We construct a variable called
management score by using the questions in the JP-MOPS management section following
Bloom et al. (2019)5 (See Appendix 1-3 for details).

usage by listing examples and is not a quantified question. We also exclude the responseof the influence of “design of new products or services” activity in question 21-1(a) and21-1(b) because the activity itself implies the implementation of innovation and thoseestablishments which implement these activities have a high possibility to realize
innovations.
5
We exclude question 1, which askes how the establishment reacted to an exception inits process, because of the same reason as we exclude the influence of “design of new
10

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Figure 4 plots the histogram of management score in each industry. As is the case for thedata scores, the mean is the highest in wholesale trade. Figure 5 shows that the distributionof management score moves to the right, and its mean becomes higher in five years.
However, the increment of the score in five years is less conspicuous than that of the datascore.
4.1.4 The Difference in Characteristics depending on Headquarter/Branch Status andEstablishment Size
Whether an establishment is a headquarter or a branch may affect data activities’ impact
on innovation outcomes. Table 2 depicts the number of establishments overheadquarter/branch status and size. In road freight transport and retail trade, the numberof branches exceeds that of headquarters. This situation reflects that transport firms tend
to have several branches as transportation points or sales offices and that food retailcompanies often operate a network of supermarket stores. In wholesale trade andinformation services, the headquarters with 30-199 employees occupy a large portion of
JP-MOPS respondents. In particular, the headquarters with 30-99 employees cover nearly
half in wholesale trade.
Table 3 shows the means of data score 1, data score 2, and management score byheadquarter/branch status and establishment size. In general, the scores are high in largesize establishments. Also, the branches’ scores tend to be higher than those inheadquarters in each category.
4.1.5 Decentralization Score
We produce a decentralization measure in decision making called decentralization scoreby using responses in the organization section. We calculate the score following themethod of Bloom et al. (2019). The score takes between 0 (completely centralized) and 1(completely decentralized). (For details in scoring, see Appendix 1-4.) Figure 6 plots thedecentralization score in 2018. Note that this score is calculated only for branches.
Overall, distributions are fat on the left side (i.e., decision making is centralized). In
medical and other health services, decision making is decentralized compared to the othertwo industries.

products or services” activity in question 21-1 (a) and 21-1 (b) in calculating data score
1.
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4.1.6 Other Characteristics
The relationship between innovations and market competition has been a theoretical andempirical issue6 (see Inui et al., 2008, for example). We therefore use the number of
establishments directly competing with an establishment (“degree of competition”).
The logged number of employees in an establishment (“establishment size”) also serves
as a proxy of the monopolistic power.
In addition, we exploit other establishment characteristics: the ratio of non-managers witha bachelor’s degree and the ratio of managers with a bachelor’s degree as proxy variables
for general human capital; the presence of the Chief Information Officer (CIO) inquestion 29 in 2018 as a proxy for tangible and intangible ICT capital, including ICTspecific human capital because those firms with a CIO should have heavily invested in
ICT infrastructure; respondents’ recognitions about two aspects of the important skills in
the establishment in question 34-1 (specialization or coordination) and 34-2 (creativity or
efficiency) in the 2018 JP-MOPS (question 32-1 and 32-2 in the 2017 JP-MOPS). We
use them to control the difference in tasks in each establishment. The questions aboutskills are made based on the theories in organizational economics, arguing therelationship between communications, skills, and organization structures (Garicano andPrat (2013), Garicano and Rayo (2016), Sugihara (2016)).
Our analysis does not use R&D investment at the establishment level due to the data
limitation7. Only in wholesale trade, we use the disaggregated industry group dummies
as a proxy of the intensity of R&D investment because the ratio of R&D investment
relative to sales varies in disaggregated industries taken from “the Basic Survey of

6

There are two different theories regarding to the relationship between innovation andmarket competition. One is that the monopolistic firms tend to have a high innovation
performance due to the abundant capital resource and stability (Schumpeter,1934). The
other one is that the market competition causes the high innovation outcomes (Aghion etal., 2005, Aghion and Griffith, 2005).
7
There are few firms that record or publicize the accounting information about theamount of R&D investment at the establishment level. It is our future work to find theappropriate proxy variables for the amount of R&D investment in analyzing theinnovation performance at the establishment level.
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The Impact of Data Activities on Innovation Performance in Service Industries

Japanese Business Structure and Activities in 2018,” published by the Ministry of
Economy, Trade and Industry, in the estimations of the probit and ordered probit models8.
4.2 Summary Statistics of Variables in Our Analysis
Table 4 (a) (b) show the summary statistics of variables in estimations. In the JP-MOPS
survey, many questions have responses in both the survey year and the recall year (fiveyears before the survey year): 2018 (survey) / 2013 (recall) in the 2018 JP-MOPS, and2015 (survey) / 2010 (recall) in the 2017 JP-MOPS. We choose the establishments withat least ten responses in the management section following Bloom et al. (2019). In
addition, we exclude the establishments that respond “Do not need to use data“ inquestion 22-2 of the 2018 JP-MOPS. Finally, we exclude those with missing values in
explanatory variables used in this paper or with no responses in all five types ofinnovations. After this screening, sample sizes used in estimations become 907 in roadfreight transport, 3,115 in wholesale trade, 1,255 in medical and other health services,
1,042 in retail trade, and 818 in information services. We use the responses in the survey
year because establishment size, competitiveness, CIO presence, and the characteristics
of tasks in the establishment do not have recall answers and because recall answers,
especially those related to rapidly-changing data activities, may have large measurement
errors. To check whether an endogeneity problem arises, we also estimate the probit
model using the recall year’s data if they exist.
4.3 Characteristics of Establishments that Use Data Intensively
Before estimating the relationship between data activities and innovation performance,
we examine the characteristics of establishments that use data intensively by regression
analysis. We estimate the following model:
′

,

(5)

8

There are 22 disaggregated industries within wholesale trade. According to the statistics
in the Basic Survey of Japanese Business Structure and Activities (BSJBSA), the ratio of
R&D investment relative to sales varies from 0.08 percent to 6.29 percent in 2017 by
those disaggregated industries in wholesale trade. We do not include these dummies in
the other industries because they are not the surveyed industries in the BSJBSA and thedata is unavailable. Because of multicollinearity problem, we exclude them in OLS
estimation.
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where
denotes data score of establishment i, and ′ denotes the characteristics ofestablishment i, namely management score, headquarter/branch status, establishment size,
degree of competition, the ratio of managers with a bachelor’s degree, the ratio of nonmanagers with a bachelor’s degree, the presence of CIO, and characteristics of tasks inthe establishment.
denotes an idiosyncratic error term and is assumed to be identicallyand independently normally distributed.
Table 5 shows the estimation results. It provides the OLS estimation results of regressing
data score 1 on management score and other characteristics by industry in the 2018 JPMOPS. In all three industries, management score is positively associated with data score
1. The establishment size is also positively associated. In wholesale trade and medical
and other health services, the ratio of non-managers with a bachelor’s degree is positively
associated and statistically significant. These two results are similar to Brijolfsson andMcElheran (2016), which find that the establishment size and the ratio of employees witha bachelor’s degree are positively correlated with DDD. Also, the presence of a CIO is
an important factor. This result is quite natural because data-driven decision making canbe effectively introduced on the foundation of rich ICT infrastructure, which is proxiedby a CIO.
4.4 The Correlation between Innovation Outcomes and Data Activities and Management
Table 6 shows the correlation between innovation outcomes and data score 1, data score2, and management score of industries in both the 2017 JP-MOPS and the 2018 JP-MOPS.
There exist consistently positive relationships between innovation outcomes and datascore or management score. Correlation coefficients range from 0.23 to 0.53 and are
relatively stable from industry to industry. In estimating the effect of data activities oninnovation performance, controlling management’s effect would be desirable. We see theestimation results and interpret them in the next section9.

5. Results9

Due to the non-negligible non-profit aspect of its activities, we think we should analyze
medical and other health services differently from other industries surveyed in the 2018
JP-MOPS. This paper focuses more on other industries (i.e., road freight transport andwholesale trade).
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5.1 The Impact of Data Activities on Innovation Performance
Table 7 shows the estimation results of regressing innovation performance on data
activities, management, and other characteristics in industries surveyed in the 2018 JPMOPS. It shows a relationship that establishments with intensive data use have better
innovation performance. Column 1 and column 2 report the average marginal effect ofdata activities and management, respectively, in equation (1) for the probit model withother explanatory variables of establishments’ characteristics. The difference between
column 1 and column 2 is that the equation used to obtain the result in column 1 does notinclude management score as an explanatory variable, while the equation used to obtain
the result of column 2 includes it. We will explain the case of innovation 1 in road freighttransport as an example. Column 1 indicates that every 0.1 point increase in data score 1
is associated with the increase in the propensity to innovate by 10.07 percentage points.
Column 2 indicates that every 0.1 point increase in data score 1 is associated with theincrease in propensity to innovate by 7.09 percentage points. Since the inclusion ofmanagement score, which is statistically significant, decreases the marginal effects ofdata score by about 3 percentage points, management must be taken into consideration
when analyzing the impact of data activities on innovation performance. In addition, as
data score 1 has a sample mean of 0.48 and a standard deviation of 0.16, a one-standarddeviation change in data activities is associated with 11.34 (=7.09*0.16/0.1) percentage
points higher propensity to innovate. Column 3 reports the estimation result when thefollowing explanatory variables are changed from those in the survey year to those in therecall year: data score 1, management score, the ratio of non-managers with a bachelor’s
degree, and the ratio of managers with a bachelor’s degree. It indicates that every 0.1
point increase in data score 1 is associated with the increase in the propensity to innovateby 4.09 percentage points. The association of the data activities with innovation
realization drops compared to that in column 2 but still economically and statisticallysignificant. This reduction may indicate that parameters in column 2 are biased upward
due to an endogeneity problem.
Table 8 reports the estimation results of the ordered probit model, which also indicatesthat data activities are positively associated with the propensity to innovate. Table 9 is theresult of the propensity score matching method, which is useful for analyzing causal
effects, showing the average marginal effect of data activities on innovation realization,
the propensity to innovate when binary data score changes from 0 (below the average) to15

New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

1 (above the average). It indicates that more intensive data activities are, on average,
associated with 8 to 15 percentage points higher propensity to innovate10.
In summary, the estimates in Tables 7, 8, and 9 validate our hypothesis that
establishments that use data intensively have better innovation performance 11 . Our
empirical result is consistent with the results in the previous study. Niebel et al. (2018)
find that the application of big data analytics is associated with 6.5 – 6.7 percentage points
higher innovation propensity over all samples12.
5.2 The Contribution of Data Activities to Innovation Performance Compared to that ofManagement
Table 10 report the estimation results of regressing innovation performance on data
activities, management, and other characteristics by OLS and the contributions of data
activities and management to innovation performance. We use the three-sector dataset
from the 2018 JP-MOPS in conjunction with data score 1 in Table 10 (a), and the fivesector dataset from the 2017 JP-MOPS and the 2018 JP-MOPS with data score 2 in Table
10 (b), respectively.
Table 10 (a) shows that the contributions of data activities and management to the 90-10
spread of the innovation performance in the three industries surveyed in the 2018 JP-

10

See the result of balancing check in Table 9. With only two exceptions out of 135
independent variables (CIO in innovation 2 and innovation 3 in medical and other
healthcare services), the absolute values of standardized differences are below 0.1 acrossindustries.
11
While we are interested in whether complementarity between data activities andmanagement on innovation performance exists, we find that the interaction term between
data score 1 (or data score 2) and management score is in general not statisticallysignificant in each industry. See Appendix 3 as an example of estimation results by theordered probit model.
12
The data they use is different from ours, besides industries, in the following three
aspects: (i) their survey year is 2015 and there is a three-year gap from our survey year;(ii) we do not limit data activities to the use of big data; (iii) we examine the impact ofdata activities on both product and process innovation.
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New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

MOPS 13 . In road freight transport, the data activities’ contribution is less thanmanagement’s contribution but close to that in process innovation categories. For
example, data activities’ contribution is 24.3 percent, and management’s contribution is27.5 percent in innovation 1, and data activities’ contribution is 27.7 percent, andmanagement’s contribution is 28.5 percent in innovation 4. In wholesale trade, data
activities’ contribution is higher than management’s contribution in all innovation
categories except for innovation 5. In medical and other healthcare services, results are
mixed.
Table 10 (b) reports that the contribution of data activities and management to the 90-10
spread of the innovation performance in the five industries surveyed in the 2017 JPMOPS and the 2018 JP-MOPS. In road freight transport, wholesale trade, and medical
and other healthcare services, results are similar to those in Table 10 (a). For example, ininnovation 1, data activities’ contribution is 21.3 percent, and management’s contribution
is 22.7 percent in road freight transport. In retail trade and information services, data
activities’ contribution exceeds management’s contribution in all innovation categories.
For example, in innovation 1, data activities’ contribution is 29.7 percent, andmanagement’s contribution is 12.6 percent in retail trade.
These exercises suggest that the impact of data activities on innovation is comparable inmagnitude to that of the “good” management practice.
5.3 The Relationship between Data Activities and Innovation Outcomes Depending on
the Establishment Characteristics
Table 11 and Table 12 show the subsample analysis of equation (1) using the probit modelin the three industries surveyed in the 2018 JP-MOPS. They show that the relationshipbetween data activities and innovation outcomes are different by industry andestablishment characteristics. Table 11(a) and Table 11(b) report the average marginal
effects on innovation 1 to innovation 3 and innovation 4 to innovation 5, respectively.
Column 1 and column 2 show the estimation results by using the samples divided into
relatively small-size establishments (“Small”) and relatively large-size ones (“Large”).
We classify the establishments that are at or below the industry median of the logged
number of employees (i.e., “lemp”) as “Small.” We classify the others as “Large.”

13

The 90th percentile of the innovation performance is 3 or 4 and the 10th percentile is
1 in the results reported in Table 10 (a) and Table 10 (b).
17New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

Column 3 and column 4 show the estimation results by using the subsample of branches(“Branch”) and headquarters (“Hq”). Column 5 and column 6 show the estimation results
by using the subsample of headquarters divided into two according to the number of
employees. Since we cannot identify whether an establishment is a single-unit status or
multi-unit status from JP-MOPS responses, we assume that the proportion of
establishments of single-unit status is high in the small-size establishments. Hence, weclassify the establishments that are headquarters and have at or below the industry medianof “lemp” as “Hq-sm,” as a proxy of establishments of single-unit status in column 5. We
classify the others as “Hq-lg,” as a proxy of establishments of multi-unit status in column
6. Table 12 reports the average marginal effects on innovation realization by the degreeof decentralization in decision making. The samples are divided into two, relatively
centralized (“Centralized”) or relatively decentralized (“Decentralized”). We classify theestablishments at or below the industry median of decentralization score as “Centralized.”
We classify the others as “Decentralized.”14
5.3.1 Establishment Size and Single- or Multi-Unit Firms
In the case of road freight transport, the estimates in Table 11 are consistent with our
hypothesis that the impact of data activities on innovation performance in large
establishments is higher than in small ones. Column 2 in Table 11 (a) indicates that every
0.1 point increase in data score 1 relates to an increase in the propensity to innovate by
9.26 percentage points in large-size establishments in innovation 1. The estimate is higher
than that in the whole sample, 7.09 percentage points. The other innovation categories
have similar results. Therefore, large establishments have the advantage of raising thepropensity to innovate by using data intensively compared to small establishments in roadfreight transport.
However, in wholesale trade, the estimates except innovation 2 in Table 11 are notconsistent with our hypothesis. For example, columns 1 and 5 in Table 11 (a) indicatethat every 0.1 point increase in data score 1 relates to an increase in the propensity toinnovate by 6 to 7 percentage points in small-size establishments and small-size
headquarters (as a proxy of single-unit firms) in innovation 1. It shows that the effect ofdata activities on innovation performance is higher in small-size establishments or
establishments with single-unit status in wholesale trade. Similar results are observed in

14

Those median numbers we use in dividing the samples are calculated among
establishments with at least ten responses in the management section (see Section 4.2).
18

New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

the case of medical and other health services. Further analysis is necessary for the
relationship between establishment size and innovation outcome.
5.3.2 Degree of Decentralization in Decision Making
In road freight transport, the estimates in Table 12 are consistent with our hypothesis thatthe impact of data activity on innovation performance is large in establishments ofdecentralized decision making in terms of the development and introduction of new
products/services and processes. Columns (2) in Table 12 indicate that every 0.1 point
increase in data score 1 relates to the increase in the propensity to innovate by 8.31
percentage points in innovation 1 and 6.47 percentage points in innovation 4, which arenearly equal or higher than the estimates in all branch samples, 7.25 percentage points
and 6.48 percentage points, respectively. The estimation results in wholesale trade show
similar relationships in innovation 1 and innovation 4, although the results are less evident
in medical and other health services. From the analysis above, we interpret that
decentralization in decision making may relate to more frequent innovations in terms ofintroducing new products/services (i.e., innovation 1) and process (i.e., innovation 4).
However, the estimates in innovation 2 and innovation 5 in Table 11 in all three industries
are not consistent with our hypothesis. The estimates indicate that centralized decision
making may relate to more frequent innovations in terms of existing products/services
and processes. These types of innovations are incremental in nature, and less creative thanthose measured by innovation 1, innovation 3 and innovation 4, and therefore can be
efficiently realized by performing data analysis routinely and even mechanically. Basedon the above result, this situation is applicable to the branches operating under theheadquarters’ tight control.

6. Conclusion
This paper examines the impact of data activities on innovation performance in service
industries. In the age of digitalization, accumulating and using data effectively is essential
to introduce new tools such as AI and increase productivity. Japan faces low productivity,
particularly in service industries. Measuring the effect of data usage and finding how to
use data effectively are worth investigating for researchers, and obtained knowledge isbeneficial for policymakers.

19

New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

We use the large-scale establishment-level data, which cover five service industries andhave rich responses, for the first time in Japan, about the use of data and relevantcharacteristics of establishments. We find that (i) establishments that use data intensively
have a better performance on innovation outcomes. The impact is consistent withprevious research, and the results are robust to alternative specifications and econometricmodels. We also find that (ii) the impact of data activities on innovations is comparable
in magnitude to that of the ‘good’ management practice and that (iii) the relationshipbetween the intensiveness of data activities and the innovation outcomes varies depending
on industries, establishment size and the degree of decentralization in decision making.
For example, more decentralization in decision making may relate to being connectedwith more frequent innovations in terms of introducing new products/services andprocesses.
Analysis in this paper is the first step in examining the relationship between data activities
and establishment performance in the age of digitalization. There is much room for further
investigation. First, in this paper, performance indicators are the innovation outcomes. In
future research, labor productivity or total factor productivity (TFP) indicators derived
from other sources can be used to augment our analysis. Also, this paper’s empiricalanalysis does not use the amount of ICT or R&D capital due to the data limitation.
Matching the JP-MOPS dataset with other statistics and taking into account them make itpossible to estimate the effect of data activities more precisely. Finally, we conduct
empirical analysis in some limited types of service industries. Examining the impact ofdata activity on the establishment/firm’s performance in other services industries isvaluable for further research.

20

New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

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The Impact of Data Activities on Innovation Performance in Service Industries

Figures and Tables
Figure 1: The Histogram of Data Score 1 in Three Industries in 2018

Figure 2: The Distribution of Data Score 1 in Three Industries in 2013 and 2018

*Blue lines denote the recall year, and red lines denote the present year.

25New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

Figure 3: The Histogram of Data Score 2 in Five Industries in the Present Year

Figure 4: The Histogram of Management Score in Five Industries in the Present Year

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New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

Figure 5: The Distribution of Management Score in Five Industries in the Recall and the
Present Year***Blue lines denote the recall year, and red lines denote the present year.

Figure 6: The Histogram of Decentralization Score in Decision Making in Three
Industries in 201827

New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

Table1: Basic Characteristics of Two Waves of JP-MOPS

2017

SurveyRecallIndustries

Number of

Number ofContents

TimingYear

Year(Division and

Mailout

Responses

in Each SectionofGroup Code)*

Samples(Response Rate)

Manufacturing

36,052

11,405

A: Management Practices(31.6%)

B: Organization

2015

2010

JP-

(E 09-32)

MOPS

Survey2017

C: Uncertainty
D: Background Characteristics

Retail Trade(Food3,573

and Beverages)

1,273

A: Management practices(35.6%)

B: Organization(I 58)

C: Data and Decision Making
D: Background Characteristics

Information

3,503

936

Services(26.7%)(G 39)
2018

2018

2013

Road

JP-

Transport

MOPS(H 44)

Freight

3,725

1,286

A: management practices(34.5%)

B: Organization
C: Data and Decision Making
D: Aritificial Intelligence(AI)Wholesale Trade12,2773,813(I 50-55)

Medical and Other(31.1%)

5,161

1,650

Health Services(32.0%)(P 83)* We follow the Japanese Standard Industry Classification.

28

E: Background Characteristics

2018

New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

Table 2: Number of Establishments by Headquarter/Branch Status and Size
Road Freight Transport
Size
Branch30-49
50-99
100-199200-299
300Total

Hq
152
156
136
37
28
509

Medical and Other Health Services
Size
Branch
30-49
64
50-99
51
100-19963
200-299
68
300104Total
350
Retail Trade
Size
30-49
50-99
100-199200-299
300Total

Branch

Total
146
127
103
14
8
398

Hq

298
283
239
51
36
907

Hq

Total
874
660
316
60
75
1,985

1,361
1,026
523
92
113
3,115

Total
228
221
183
163
110
905

Hq
146
263
249
31
8
697

Wholesale Trade
Size
Branch
30-49
487
50-99
366
100-199207
200-299
32
30038
Total
1,130

292
272
246
231
214
1,255
Total

120
77
86
43
19
345

266
340
335
74
27
1,042

Information Services
Size
Branch
30-49
74
50-99
85
100-19956
200-299
14
30029
Total
258

Hq

Total
181
161
119
57
42
560

255
246
175
71
71
818

Table 3: Mean of Data Score 1, Data Score 2, and Management Score by Headquarter/Branch
Status and Establishment Size
Road Freight Transport
Data Score 1
Size
Branch
Hq
30-49
0.47
0.41
50-99
0.48
0.45
100-1990.53
0.50
200-2990.51
0.47
3000.59
0.50

Data Score 2
Branch
Hq
0.44
0.38
0.50
0.40
0.53
0.45
0.50
0.45
0.62
0.49

Management Score
Branch
Hq
0.48
0.36
0.54
0.41
0.56
0.44
0.57
0.45
0.64
0.63

Retail Trade
Size
30-49
50-99
100-199200-299
300-

Data Score 2
Branch
Hq
0.46
0.50
0.61
0.49
0.62
0.59
0.59
0.50
0.49
0.57

Management Score
Branch
Hq
0.51
0.36
0.54
0.47
0.60
0.52
0.58
0.48
0.47
0.59

Wholesale Trade
Data Score 1
Size
Branch
Hq
30-49
0.55
0.50
50-99
0.57
0.53
100-1990.58
0.54
200-299
0.59
0.56
3000.62
0.60

Data Score 2
Branch
Hq
0.54
0.48
0.57
0.50
0.61
0.55
0.60
0.60
0.58
0.70

Management Score
Branch
Hq
0.55
0.47
0.58
0.50
0.60
0.54
0.59
0.56
0.65
0.61

Information Services
Data Score 2
Size
Branch
Hq
30-49
0.36
0.26
50-99
0.42
0.34
100-1990.43
0.38
200-299
0.58
0.43
3000.60
0.50

Management Score
Branch
Hq
0.49
0.39
0.54
0.44
0.53
0.49
0.54
0.53
0.59
0.55

Medical and Other Health Services
Data Score 1
Data Score 2
Size
BranchHq
Branch
Hq
30-49
0.45
0.44
0.44
0.44
50-99
0.47
0.40
0.53
0.38
100-199
0.46
0.43
0.52
0.43
200-299
0.48
0.48
0.53
0.50
3000.53
0.52
0.58
0.55

Management Score
Branch
Hq
0.37
0.35
0.43
0.33
0.46
0.39
0.50
0.47
0.52
0.50

Notes: Data score 1, data score 2, and management score are calculated by the present year’s responses.

29

New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

Table 4 (a): Summary Statistics - Road Freight Transport, Wholesale Trade, and Medical andOther Health Services
Road Freight Transport
2018

N

mean

sd

Innovation 1
Innovation 2
Innovation 3
Innovation 4
Innovation 5
d.Innovation 1
d.Innovation 2
d.Innovation 3
d.Innovation 4
d.Innovation 5
Data Score
Data Score 2
Management Score
d.Data Score
lemp
dumhq
cmp
univm
unive
CIO
Q34_1score
Q34_2score
Decentralization Score
2013
Data Score
Data Score 2
Management Score
d.Data Score
dumhq
univm
unive

866
869
860
870
871
866
869
860
870
871
907
897
907
907
907
907
907
907
907
907
907
907
508

1.63
1.88
1.83
1.76
1.97
0.42
0.54
0.51
0.47
0.57
0.48
0.46
0.48
0.56
4.34
0.44
3.21
1.69
2.32
1.27
2.17
2.33
0.33

0.87
0.98
0.97
0.95
1.03
0.49
0.50
0.50
0.50
0.50
0.16
0.25
0.19
0.50
0.71
0.50
1.48
1.26
0.88
0.59
0.80
0.76
0.19

10%
point
1
1
1
1
1
0
0
0
0
0
0.27
0.13
0.21
0
3.53
0
1
1
1
1
1
1
0.06

869
0.41
858
0.39
869
0.45
869
0.52
869
0.44
869
1.61
869
2.24
Wholesale Trade

0.17
0.26
0.19
0.50
0.50
1.21
0.89

0.18
0.06
0.19
0
0
1
1

2018

N

mean

sd

Innovation 1
Innovation 2
Innovation 3
Innovation 4
Innovation 5
d.Innovation 1
d.Innovation 2
d.Innovation 3
d.Innovation 4
d.Innovation 5
Data Score
Data Score 2
Management Score
d.Data Score
lemp
dumhq
cmp
univm
unive
CIO
Q34_1score

3047
3036
3027
3037
3043
3047
3036
3027
3037
3043
3115
3093
3115
3115
3115
3115
3115
3115
3115
3115
3115

2.20
2.33
2.19
1.98
2.17
0.67
0.71
0.66
0.64
0.69
0.54
0.53
0.53
0.52
4.17
0.64
3.65
3.28
3.46
1.26
2.05

1.10
1.10
1.09
0.95
1.02
0.47
0.45
0.47
0.48
0.46
0.14
0.23
0.16
0.50
0.69
0.48
1.44
1.61
0.87
0.55
0.79

30

10%
point
1
1
1
1
1
0
0
0
0
0
0.36
0.25
0.31
0
3.47
0
1
1
2
1
1

1
2
2
1
2
0
1
1
0
1
0.49
0.47
0.49
1
4.20
0
3
1
2
1
2
3
0.33

90%
point
3
3
3
3
4
1
1
1
1
1
0.68
0.75
0.73
1
5.25
1
5
4
4
2
3
3
0.58

90-10
spread
2
2
2
2
3
1
1
1
1
1
0.41
0.63
0.51
1
1.72
1
4
3
3
1
2
2
0.52

0.41
0.38
0.45
1
0
1
2

0.63
0.75
0.70
1
1
4
4

0.45
0.69
0.51
1
1
3
3

90%
point
4
4
4
3
4
1
1
1
1
1
0.71
0.79
0.73
1
5.06
1
5
5
4
2
3

90-10
spread
3
3
3
2
3
1
1
1
1
1
0.35
0.54
0.41
1
1.60
1
4
4
2
1
2

medianmedian
2
2
2
2
2
1
1
1
1
1
0.54
0.53
0.53
1
3.99
1
4
4
4
1
2

New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

Q34_2score
Decentralization Score
2013
Data Score
Data Score 2
Management Score
d.Data Score
dumhq
univm
unive

3115
1109

2.07
0.28

0.76
0.20

3025
0.46
0.15
2999
0.49
0.24
3025
0.51
0.16
3025
0.50
0.50
3025
0.64
0.48
3025
3.18
1.64
3025
3.40
0.91
Medical and Other Health Services

1
0

2
0.25

3
0.54

2
0.54

0.28
0.19
0.29
0
0
1
2

0.46
0.50
0.51
1
1
3
4

0.66
0.75
0.71
1
1
5
4

0.38
0.56
0.42
1
1
4
2

1
2
1
1
2
0
1
0
0
1
0.46
0.50
0.41
1
4.78
1
4
2
3
1
2
2
0.42

90%
point
3
3
3
3
3.5
1
1
1
1
1
0.63
0.75
0.64
1
6.07
1
5
5
4
1
3
3
0.79

90-10
spread
2
2
2
2
2.5
1
1
1
1
1
0.38
0.63
0.47
1
2.51
1
3
4
2
0
2
2
0.60

0.38
0.44
0.38
1
1
2
3

0.56
0.75
0.61
1
1
5
4

0.40
0.69
0.46
1
1
4
2

2018

N

mean

sd

Innovation 1
Innovation 2
Innovation 3
Innovation 4
Innovation 5
d.Innovation 1
d.Innovation 2
d.Innovation 3
d.Innovation 4
d.Innovation 5
Data Score
Data Score 2
Management Score
d.Data Score
lemp
dumhq
cmp
univm
unive
CIO
Q34_1score
Q34_2score
Decentralization Score
2013
Data Score
Data Score 2
Management Score
d.Data Score
dumhq
univm
unive

1204
1200
1193
1196
1200
1204
1200
1193
1196
1200
1255
1240
1255
1255
1255
1255
1255
1255
1255
1255
1255
1255
341

1.67
1.87
1.71
1.73
1.96
0.47
0.55
0.46
0.50
0.58
0.45
0.47
0.41
0.58
4.80
0.72
3.61
2.72
2.97
1.10
1.99
2.23
0.46

0.84
0.95
0.90
0.88
1.00
0.50
0.50
0.50
0.50
0.49
0.15
0.24
0.18
0.49
0.97
0.45
1.34
1.57
0.93
0.37
0.77
0.74
0.22

10%
point
1
1
1
1
1
0
0
0
0
0
0.25
0.13
0.17
0
3.56
0
2
1
2
1
1
1
0.19

1195
1176
1195
1195
1195
1195
1195

0.37
0.42
0.38
0.55
0.74
2.65
2.88

0.16
0.24
0.17
0.50
0.44
1.57
0.95

0.16
0.06
0.15
0
0
1
2

medianSource: The JP-MOPS(2018)
Notes: Innovations 1, 2, 3, 4, and 5 denote the development and introduction of new products/services, the improvement ofexisting products/services, the new combination of existing products/services, the introduction of new processes, and process
improvements, respectively.

31

New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

Table 4 (b): Summary Statistics - Retail Trade and Information Services
Retail Trade
2015

N

mean

sd

Innovation 1
Innovation 2
Innovation 3
Innovation 4
Innovation 5
d.Innovation 1
d.Innovation 2
d.Innovation 3
d.Innovation 4
d.Innovation 5
Data Score 2
Management Score
lemp
dumhq
cmp
univm
unive
Q34_1score
Q34_2score
2010
Data Score 2
Management Score
dumhq
univm
unive

1018
1018
993
1013
1012
1018
1018
993
1013
1012
1042
1042
1042
1042
1042
1042
1042
1042
1042

3.22
3.22
3.08
2.60
2.75
0.86
0.87
0.84
0.79
0.80
0.56
0.52
4.41
0.33
3.11
3.05
2.96
2.13
2.23

1.12
1.08
1.13
1.08
1.12
0.35
0.33
0.37
0.41
0.40
0.25
0.17
0.67
0.47
1.03
1.71
1.07
0.78
0.75

10%
point
1
1
1
1
1
0
0
0
0
0
0.22
0.29
3.56
0
2
1
1
1
1

984
0.51
984
0.49
984
0.32
984
2.88
984
2.86
Information Services

0.25
0.17
0.47
1.74
1.07

0.15
0.26
0
1
1

2015

N

mean

sd

Innovation 1
Innovation 2
Innovation 3
Innovation 4
Innovation 5
d.Innovation 1
d.Innovation 2
d.Innovation 3
d.Innovation 4
d.Innovation 5
Data Score 2
Management Score
lemp
dumhq
cmp
univm
unive
Q34_1score
Q34_2score
2010
Data Score 2
Management Score
dumhq
univm
unive

807
803
801
808
809
807
803
801
808
809
818
818
818
818
818
818
818
818
818

2.13
2.55
2.21
2.15
2.51
0.73
0.79
0.70
0.71
0.81
0.37
0.48
4.47
0.68
2.98
3.55
3.77
1.82
1.92

0.94
1.10
1.04
0.97
1.02
0.44
0.41
0.46
0.45
0.39
0.27
0.16
0.85
0.46
1.63
1.48
0.60
0.77
0.76

10%
point
1
1
1
1
1
0
0
0
0
0
0
0.25
3.53
0
1
1
3
1
1

794
794
794
794
794

0.33
0.44
0.70
3.45
3.70

0.26
0.16
0.46
1.52
0.68

0
0.22
0
1
3

Source: The JP-MOPS(2017)

Table 5: Characteristics of Establishments that Use Data Intensively
Data
(1)

(2)

(3)

32

4
4
4
3
3
1
1
1
1
1
0.57
0.52
4.41
0
3
3
3
2
2

90%
point
4
4
4
4
4
1
1
1
1
1
0.85
0.74
5.28
1
5
5
4
3
3

90-10
spread
3
3
3
3
3
1
1
1
1
1
0.64
0.45
1.72
1
3
4
3
2
2

0.50
0.50
0
3
3

0.82
0.71
1
5
4

0.68
0.45
1
4
3

2
3
2
2
2
1
1
1
1
1
0.36
0.48
4.29
1
3
4
4
2
2

90%
point
4
4
4
4
4
1
1
1
1
1
0.75
0.68
5.61
1
5
5
4
3
3

90-10
spread
3
3
3
3
3
1
1
1
1
1
0.75
0.43
2.08
1
4
4
1
2
2

0.29
0.44
1
4
4

0.75
0.66
1
5
4

0.75
0.43
1
4
1

medianmedianNew ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

management
dumhq
univm
unive
lemp
cmp
Q34_1score
Q34_2score
CIO
_cons

0.365***(0.0285)0.00451(0.0101)-0.00610(0.00407)
0.00641(0.00594)
0.0165**(0.00693)
0.00798**(0.00321)
0.00641(0.00595)-0.0122**(0.00620)
0.0154*(0.00849)
0.195***(0.0377)0.301***(0.0154)-0.0134***(0.00489)
0.00228(0.00183)
0.0107***(0.00340)
0.0126***(0.00342)
0.00272*(0.00158)-0.000383(0.00289)-0.00463(0.00301)
0.0287***(0.00426)
0.254***(0.0197)

0.298***(0.0244)-0.00311(0.00898)
0.00343(0.00289)
0.0120**(0.00505)
0.00931**(0.00443)
0.00613**(0.00293)
0.00358(0.00508)-0.00781(0.00526)
0.0439***(0.0107)0.184***(0.0312)

N
907
3115
1255
adj. R-sq
0.244
0.203
0.186
F
33.51
89.26
32.86
Notes: OLS coefficients with standard errors in parentheses. The dependent variable is data score 1. Columns 1, 2, and 3 report theestimation results in road freight transport, wholesale trade, and medical and other health services, respectively. Management denotes
management score. Dumhq denotes the dummy of headquarter status, which takes 1 if the establishment is a headquarter. Univm andunive denote the ratio of managers and non-managers with a bachelor’s degree, respectively. Lemp denotes the logged number of
employees. Cmp denotes the number of establishments directly competing with an establishment. Q34_1score (from 1(specialization) to 3 (coordination)) and Q34_2score (from 1 (creativity) to 3 (efficiency)) denote characteristics of jobs. CIO denotes
the presence of the CIO. All results shown in Appendices follow the same definitions. N denotes the number of observations.
*** Significant at the 1 percent level. ** Significant at the 5 percent level. * Significant at the 10 percent level.

Table 6: The Correlation between Innovation Outcomes and Data Score 1, Data Score 2, andManagement Score
Road Freight Transport
Data Score 1
Data Score 2
Management Score
Wholesale Trade
Data Score 1
Data Score 2
Management Score
Medical and Other Health Services
Data Score 1
Data Score 2
Management Score
Retail Trade
Data Score 2
Management Score
Information Services
Data Score 2
Management Score

Innovation 1
0.38
0.39
0.40
Innovation 1
0.27
0.30
0.25
Innovation 1
0.28
0.31
0.29
Innovation 1
0.38
0.23
Innovation 1
0.47
0.29

Innovation 2
0.36
0.38
0.39
Innovation 2
0.28
0.32
0.27
Innovation 2
0.31
0.31
0.27
Innovation 2
0.39
0.23
Innovation 2
0.45
0.33

Innovation 3
0.36
0.39
0.40
Innovation 3
0.30
0.31
0.28
Innovation 3
0.30
0.31
0.28
Innovation 3
0.41
0.23
Innovation 3
0.44
0.32

Innovation 4
0.38
0.39
0.40
Innovation 4
0.32
0.31
0.29
Innovation 4
0.30
0.30
0.28
Innovation 4
0.48
0.27
Innovation 4
0.43
0.33

Notes: Data score 1, data score 2, and management score are calculated by the present year’s responses.

33

Innovation 5
0.39
0.39
0.41
Innovation 5
0.31
0.31
0.32
Innovation 5
0.34
0.28
0.31
Innovation 5
0.53
0.34
Innovation 5
0.39
0.32

New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

Table 7: Data Activities and Innovation Performance by the Probit Model
Innovation 1

Innovation 2

Innovation 3

Innovation 4

(1)

(2)

(3)

(1)

(2)

(3)

(1)

(2)

1.007***

0.709***

0.409***

0.984***

0.676***

0.314**

1.015***(0.126)(0.135)(0.127)(0.125)(0.135)(0.127)(0.129)0.759***

0.787***

0.828***

0.822***

0.840***

0.820***

Innovation 5

(3)

(1)

(2)

(3)

(1)

(2)

(3)

0.702***

0.342***

0.894***

0.608***

0.319**

0.871***

0.599***

0.262**(0.139)(0.129)(0.125)(0.135)(0.128)(0.122)(0.132)(0.124)0.720***

0.670***

0.677***

0.702***

Road Freight Transport
data
management(0.122)(0.126)(0.122)(0.125)(0.126)(0.128)(0.122)(0.125)(0.119)(0.122)

Pseudo R2

0.151

0.185

0.159

0.132

0.171

0.138

0.148

0.187

0.152

0.133

0.163

0.138

0.124

0.151

0.129

N

866

866

833

869

869

835

860

860

827

870

870

837

871

871

837

0.679***

0.529***

0.379***

0.676***

0.531***

0.337***

0.766***

0.606***

0.389***

0.927***

0.751***

0.502***

0.786***

0.598***

0.348***(0.0669)(0.0704)(0.0664)(0.0634)(0.0667)(0.0628)(0.0684)(0.0718)(0.0670)(0.0711)(0.0744)(0.0689)(0.0663)(0.0693)(0.0645)0.429***

0.419***

0.416***

0.436***

0.482***

0.486***

0.542***

0.474***

0.575***

0.533***

Wholesale
Trade
data
management(0.0643)(0.0656)(0.0607)(0.0623)(0.0653)(0.0666)(0.0669)(0.0678)(0.0632)(0.0643)

Pseudo R2

0.088

0.099

0.089

0.089

0.103

0.088

0.097

0.111

0.095

0.104

0.121

0.098

0.100

0.123

0.097

N

3047

3047

2961

3028

3028

2945

3027

3027

2943

3028

3028

2947

3043

3043

2959

0.852***

0.629***

0.378***

0.849***

0.648***

0.383***

0.880***

0.675***

0.422***

0.896***

0.683***

0.440***

0.968***

0.740***

0.496***(0.108)(0.114)(0.108)(0.106)(0.112)(0.107)(0.109)(0.114)(0.108)(0.109)(0.114)(0.108)(0.107)(0.113)(0.107)0.675***

0.665***

0.604***

0.561***

0.622***

0.572***

0.655***

0.564***

0.718***

0.601***

Yes(0.105)(0.100)(0.103)(0.101)(0.104)(0.101)(0.104)(0.101)(0.103)

0.099

0.082

0.093

0.072

0.095

0.072

0.102

0.077

0.118

0.087

Medical and Other Health Services
data
management
Pseudo R2

0.072

0.071

0.071

0.076

0.087

N
1204
1204
1151
1200
1200
1148
1193
1193
1141
1196
1196
1142
1200
1200
1148
Notes: Marginal effects with the standard errors in parentheses. Dependent variables are the dummy variables of innovation outcomes constructed from the responses in question 33, which take 1 if the innovation realizes at least once in five years (20132018). Columns 1 and 2 show the marginal effects by the probit model without and with management score, respectively. Column 3 shows the marginal effects by the probit model, which uses independent variables in the recall year if they exist (i.e., datascore 1 (data), management score (management), the dummy of headquarter status, the ratio of managers with a bachelor’s degree, the ratio of non-managers with a bachelor’s degree). Independent variables are data score 1 (data), management score(management), the dummy of headquarter status, the ratio of managers with a bachelor’s degree, the ratio of non-managers with a bachelor’s degree, the logged number of employees, the number of establishments directly competing with an establishment,characteristics of jobs, and the presence of the CIO. In wholesale trade, disaggregated industry group dummies are also included. See Appendix 2-1 for the full results of all dependent variables. Pseudo R2 denotes McFadden’s pseudo R-squared, and N
denotes the number of observations.
*** Significant at the 1 percent level.
** Significant at the 5 percent level.
* Significant at the 10 percent level.

34

New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

Table 8: Data Activities and Innovation Performance by the Ordered Probit Model
Road Freight Transport

Wholesale Trade

Medical and Other Health Services

Innovation 1

Innovation 2

Innovation 3

Innovation 4

Innovation 5

Innovation 1

Innovation 2

Innovation 3

Innovation 4

Innovation 5

Innovation 1

Innovation 2

Innovation 3

Innovation 4

Innovation 5

2.023***

1.773***

1.728***

1.975***

1.885***

1.483***

1.567***

1.693***

1.927***

1.756***

1.641***

1.880***

1.828***

1.789***

2.052***(0.313)(0.289)(0.295)(0.302)(0.288)(0.163)(0.163)(0.164)(0.166)(0.163)(0.258)(0.250)(0.261)(0.254)(0.249)1.814***

1.642***

1.746***

1.642***

1.537***

1.127***

1.165***

1.150***

1.309***

1.551***

1.438***

1.258***

1.424***

1.434***

1.552***(0.282)(0.262)(0.272)(0.271)(0.260)(0.149)(0.148)(0.149)(0.150)(0.149)(0.225)(0.217)(0.225)(0.222)(0.216)-0.781***-0.703***-0.689***-0.785***-0.736***-0.528***-0.511***-0.610***-0.714***-0.606***-0.653***-0.743***-0.725***-0.714***-0.800***(0.120)(0.114)(0.118)(0.120)(0.113)(0.058)(0.053)(0.059)(0.062)(0.057)(0.103)(0.099)(0.103)(0.101)(0.097)

2._predict

0.383***

0.186***

0.208***

0.289***

0.144***-0.005-0.092***-0.0130.146***-0.0190.310***

0.186***

0.265***

0.281***

0.144***(0.067)(0.039)(0.043)(0.052)(0.034)(0.010)(0.014)(0.011)(0.020)(0.013)(0.054)(0.033)(0.044)(0.046)(0.029)

3._predict

0.286***

0.310***

0.303***

0.315***

0.315***

0.165***

0.192***

0.238***

0.298***

0.283***

0.228***

0.346***

0.317***

0.293***

0.368***(0.050)(0.055)(0.056)(0.054)(0.054)(0.020)(0.022)(0.026)(0.029)(0.029)(0.039)(0.051)(0.049)(0.045)(0.050)

4._predict

0.112***

0.207***

0.179***

0.180***

0.276***

0.367***

0.412***

0.385***

0.270***

0.342***

0.115***

0.212***

0.143***

0.140***

0.288***(0.023)(0.038)(0.035)(0.033)(0.046)(0.041)(0.043)(0.038)(0.025)(0.033)(0.022)(0.032)(0.025)(0.024)(0.039)-0.700***-0.651***-0.697***-0.652***-0.600***-0.401***-0.380***-0.414***-0.485***-0.535***-0.572***-0.497***-0.565***-0.572***-0.605***(0.109)(0.104)(0.108)(0.107)(0.101)(0.053)(0.049)(0.054)(0.056)(0.052)(0.089)(0.086)(0.089)(0.089)(0.084)

2._predict

0.343***

Yes

0.210***

0.241***

0.118***-0.004-0.069***-0.0090.099***-0.0170.272***

0.124***

0.206***

0.225***

0.109***(0.060)(0.036)(0.041)(0.047)(0.029)(0.008)(0.012)(0.008)(0.016)(0.012)(0.047)(0.026)(0.037)(0.040)(0.023)

3._predict

0.257***

0.287***

0.306***

0.262***

0.257***

0.126***

0.142***

0.162***

0.203***

0.250***

0.200***

0.232***

0.247***

0.235***

0.279***(0.044)(0.050)(0.052)(0.047)(0.048)(0.018)(0.020)(0.022)(0.025)(0.026)(0.034)(0.042)(0.042)(0.039)(0.042)

4._predict

0.100***

0.192***

0.181***

0.150***

0.225***

0.279***

0.307***

0.262***

0.183***

0.302***

0.100***

0.142***

0.112***

0.112***

0.218***(0.021)(0.035)(0.033)(0.029)(0.041)(0.037)(0.039)(0.034)(0.022)(0.030)(0.019)(0.027)(0.021)(0.021)(0.033)Pseudo R

0.127

0.100

0.109

0.115

0.106

0.062

0.070

0.069

0.075

0.070

0.069

0.066

0.069

0.069

0.077

N

866

869

860

870

871

3047

3036

3027

3037

3043

1204

1200

1193

1196

1200

Coefficients
data
management

Marginal Effects
data
1._predict

management
1._predict

2

Notes: Coefficients and marginal effects with the standard errors in parentheses. Dependent variables are innovation outcomes constructed from the responses in question 33, which takes 0 if the innovation unrealizes, 1 if it realizes once every few years,
2 if it realizes once a year, 3 if it realizes more than once a year. Independent variables are data score 1 (data), management score (management), the dummy of headquarter status, the ratio of managers with a bachelor’s degree, the ratio of non-managers
with a bachelor’s degree, the logged number of employees, the number of establishments directly competing with an establishment, characteristics of jobs, and the presence of the CIO. In wholesale trade, disaggregated industry group dummies are also
included. See Appendix 2-2 for the full results of all dependent variables. Pseudo R2 denotes McFadden’s pseudo R-squared, and N denotes the number of observations.
*** Significant at the 1 percent level.

** Significant at the 5 percent level.

* Significant at the 10 percent level.

35

New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

Table 9: Data Activities and Innovation Performance by the Propensity Score Matching
Road Freight Transport
Innovation 1

Wholesale Trade

Innovation 2

Innovation 30.112**

0.125***(0.0452)(0.0419)

Medical and Other Health Services

Innovation 4

Innovation 5

Innovation 1

Innovation 2

Innovation 3

Innovation 4

Innovation 5

Innovation 1

Innovation 2

Innovation 3

Innovation 4

Innovation 50.147***

0.114**

0.0918**0.124***

0.0847***0.129***

0.147***(0.0403)(0.0478)(0.0395)(0.0210)(0.0197)(0.0217)(0.0208)0.118***

0.101**

0.109***

0.0863**0.110***

0.104***(0.0199)(0.0395)(0.0393)(0.0352)(0.0356)(0.0385)

Average Marginal EffectdataStandardized Differences
management

-0.04

-0.01

-0.01

-0.05

-0.01

-0.01

-0.01

0.01

-0.01

0.00

0.05

0.06

0.05

-0.01

0.03

dumhq

-0.02

0.00

-0.08

-0.01

0.02

-0.02

0.02

0.05

-0.01

-0.01

0.02

0.02

0.01

0.00

0.04

univm

0.01

0.02

0.08

-0.01

-0.03

0.01

0.05

-0.02

0.01

0.02

0.01

-0.02

0.01

0.07

0.01

unive

-0.01

0.00

0.00

0.08

-0.04

-0.02

0.03

0.02

0.00

0.03

0.04

0.04

0.10

0.10

0.05

lemp

0.07

0.03

0.01

0.09

0.01

0.01

0.04

0.05

0.02

0.02

-0.04

-0.03

-0.02

-0.01

-0.04

cmp

0.09

0.01

0.09

0.04

0.04

-0.05

-0.06

-0.01

-0.02

0.02

-0.03

0.02

0.03

0.07

0.04

Q34_1score

0.10

0.04

0.05

0.05

0.01

0.01

0.04

0.03

-0.01

0.06

0.02

0.02

0.07

0.02

0.02

Q34_2score

-0.02

-0.02

0.04

-0.04

0.00

-0.07

0.01

0.00

-0.03

0.02

-0.02

-0.03

-0.03

-0.01

-0.01

CIO

-0.06

-0.03

0.00

-0.02

0.01

-0.04

-0.03

0.00

-0.01

-0.05

-0.07

-0.11

-0.12

-0.04

-0.05

N

866

869

860

870

871

3047

3036

3027

3037

3043

1204

1200

1193

1196

1200

Notes: Average marginal effects with the standard errors in parentheses. Outcome variables are the dummy variables of innovation outcomes constructed from the responses in question 33, which take 1 if the innovation realizes at least once in five years(2013-2018). We construct a binary variable (called binary data score) that takes 0 if data score 1 is at or below the industry mean and takes 1 if it is above the industry mean. The Table shows the average marginal effect of data activities on innovation
realization, the propensity to innovate when binary data score (data) changes from 0 to 1. The propensity score is calculated by the estimated probit model whose independent variables include management score, the dummy of headquarter status, the ratioof managers with a bachelor’s degree, the ratio of non-managers with a bachelor’s degree, the logged number of employees, the number of establishments directly competing with an establishment, characteristics of jobs and the presence of the CIO. N
denotes the number of observations. Standardized Differences show standardized differences of independent variables after matched for dependent variables. In wholesale trade, they are below 0.1 for all disaggregated industry group dummies (not shownin the Table).
*** Significant at the 1 percent level.
** Significant at the 5 percent level.
* Significant at the 10 percent level.

36

New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

Table 10 (a): The Contribution of Data Activities Compared to that of Management in Three Industries
Innovation 1(1)

Innovation 2
(2)

(3)

(1)

1.643***

1.185***(0.182)(0.191)

Innovation 3
(2)

(3)

(1)

1.781***

1.277***(0.198)(0.211)

Innovation 4
(2)

(3)

(1)

1.704***

1.195***(0.202)(0.215)Innovation 5
(2)

(3)

(1)

1.831***

1.359***

1.971***(0.192)(0.202)(0.194)(2)(3)

Road Freight Transport
data

management

1.509***

1.063***

1.655***

1.182***

1.639***

1.192***

1.614***

1.106***

1.462***(0.207)1.732***

1.189***(0.191)(0.201)(0.166)(0.172)(0.181)(0.192)(0.183)(0.196)(0.181)(0.188)

adj. R-sq

0.179

0.176

0.212

0.162

0.162

0.194

0.170

0.174

0.203

0.183

0.174

0.213

0.194

0.184

0.223

F

21.26

24.00

25.08

20.29

21.74

25.41

21.78

26.00

27.99

23.25

24.86

27.16

26.63

26.95

31.71

N

866

866

866

869

869

869

860

860

860

870

870

870

871

871

871

24.3%

36.2%

26.0%

34.8%

24.4%

37.3%

27.7%

26.5%

39.0%

27.5%

41.6%

28.5%

Share of 90-10 explained
data

33.7%

management

42.8%

30.6%

42.2%

30.7%

19.7%
29.8%

20.5%

Wholesale Tradedata

1.638***

1.304***

1.724***

1.378***

1.799***

1.457***

1.750***

1.416***

1.886***(0.139)(0.148)(0.138)(0.147)(0.135)(0.143)(0.117)(0.125)(0.124)

management

1.306***

0.909***

1.351***

0.928***

1.373***

0.930***

1.338***

0.911***

1.425***(0.134)
1.687***

1.257***(0.114)(0.122)(0.127)(0.134)(0.128)(0.136)(0.125)(0.132)(0.106)(0.113)

adj. R-sq

0.100

0.090

0.112

0.121

0.110

0.134

0.127

0.113

0.141

0.132

0.115

0.150

0.120

0.118

0.149

F

44.53

41.45

48.23

54.27

49.22

56.86

57.92

51.52

60.16

57.48

49.05

62.37

54.03

51.99

65.57

N

3047

3047

3047

3036

3036

3036

3027

3027

3027

3037

3037

3037

3043

3043

3043

15.2%

20.2%

16.2%

21.1%

17.1%

30.8%

24.9%

22.0%

Share of 90-10 explained
data

19.1%

management

18.0%

12.5%

18.7%

12.9%

19.0%

12.9%

28.0%

19.1%

16.6%
23.3%

17.4%

Medical and Other Health Services
data

1.375***

1.028***

1.778***

1.424***

1.609***

1.235***

1.545***

1.184***

2.026***(0.153)(0.167)(0.161)(0.177)(0.158)(0.169)(0.153)(0.164)(0.171)

management

1.255***

0.944***

1.393***

0.963***

1.392***

1.018***

1.353***

0.994***

1.571***(0.182)
1.723***

1.248***(0.176)(0.186)(0.147)(0.160)(0.170)(0.183)(0.155)(0.165)(0.154)(0.163)

adj. R-sq

0.097

0.098

0.125

0.113

0.095

0.136

0.104

0.098

0.133

0.101

0.096

0.130

0.129

0.118

0.164

F

15.49

15.01

19.68

20.60

14.98

23.07

17.96

15.28

21.46

18.28

15.33

21.97

25.08

19.32

29.56

N

1204

1204

1204

1200

1200

1200

1193

1193

1193

1196

1196

1196

1200

1200

1200

19.6%

33.9%

27.1%

30.7%

23.6%

29.7%

22.7%

31.0%

Share of 90-10 explained
datamanagement

26.2%
29.8%

22.4%

33.0%

22.8%

33.0%

24.1%

32.1%

23.6%

24.0%
32.5%

23.5%

Notes: OLS coefficients with the standard errors in parentheses. Dependent variables are innovation outcomes constructed from the responses in question 33. Key independent variables are data score 1
in column 1, management score in column 2, and both in column 3. All regressions also include as independent variables the dummy of headquarter status, the ratio of managers with a bachelor’s degree,
the ratio of non-managers with a bachelor’s degree, the logged number of employees, the number of establishments directly competing with an establishment, characteristics of jobs and, the presence of
the CIO. OLS coefficients of data score 1 (data) and management score (management) are shown. Share of 90-10 explained is calculated by multiplying the estimated coefficient by the 90-10 spread ofeach key variable and dividing it by the 90-10 spread of innovation outcome. N denotes the number of observations.
*** Significant at the 1 percent level.
** Significant at the 5 percent level.
* Significant at the 10 percent level.

37

New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

Table 10 (b): The Contribution of Data Activities Compared to that of Management in Five Industries
Innovation 1(1)

Innovation 2
(2)

(3)

(1)

1.178***

0.853***(0.112)(0.121)

Innovation 3
(2)

(3)

(1)

1.285***

0.942***(0.127)(0.135)Innovation 4
(2)

(3)

(1)

1.314***

0.975***(0.125)(0.134)

Innovation 5
(2)

(3)

(1)

1.284***

0.954***

1.365***(0.118)(0.123)(0.128)(2)(3)

Road Freight Transport
data2

management

1.645***

1.178***

1.760***

1.249***

1.776***

1.244***

1.742***

1.217***

1.009***(0.136)1.870***

1.323***(0.160)(0.173)(0.179)(0.190)(0.178)(0.192)(0.175)(0.183)(0.186)(0.197)

adj. R-sq

0.169

0.167

0.212

0.163

0.158

0.202

0.176

0.167

0.215

0.177

0.167

0.215

0.187

0.180

0.226

F

24.99

25.17

29.97

25.14

23.65

29.71

28.09

27.79

34.72

25.49

26.43

30.27

27.74

29.73

33.93

N

858

858

858

861

861

861

852

852

852

862

862

862

862

862

862

21.3%

32.1%

23.6%

32.9%

24.4%

40.1%

29.8%

28.4%

Share of 90-10 explained
data

29.4%

management

31.7%

22.7%

33.9%

24.1%

34.2%

23.9%

44.5%

31.1%

21.0%
31.8%

22.5%

Wholesale Tradedata2

1.284***

1.104***

1.388***

1.205***

1.295***

1.097***

1.157***

0.958***

1.268***(0.0871)(0.0918)(0.0850)(0.0894)(0.0858)(0.0898)(0.0763)(0.0798)(0.0801)

management

1.399***

0.947***

1.445***

0.950***

1.486***

1.038***

1.426***

1.033***

1.005***(0.0842)1.777***

1.363***(0.126)(0.131)(0.128)(0.132)(0.125)(0.130)(0.106)(0.110)(0.114)(0.118)

adj. R-sq

0.112

0.083

0.126

0.137

0.100

0.152

0.127

0.101

0.145

0.127

0.107

0.150

0.117

0.111

0.153

F

54.19

41.73

60.23

69.30

48.07

72.64

62.58

49.81

68.98

58.06

50.14

67.29

56.01

53.43

74.43

N

3020

3020

3020

3008

3008

3008

3000

3000

3000

3007

3007

3007

3013

3013

3013

19.7%

24.6%

21.3%

22.9%

19.4%

30.7%

25.4%

22.4%

Share of 90-10 explained
data

23.0%

management

19.3%

13.1%

20.0%

13.2%

20.6%

14.4%

29.7%

21.5%

17.8%
24.5%

18.8%

Medical and Other Services
data2

0.973***

0.755***

1.133***

0.894***

1.082***

0.835***

1.023***

0.783***

1.069***(0.101)(0.110)(0.110)(0.118)(0.108)(0.117)(0.106)(0.114)(0.119)management

1.294***

0.936***

1.441***

1.013***

1.451***

1.051***

1.397***

1.020***

0.730***(0.126)
1.783***

1.431***(0.148)(0.159)(0.171)(0.182)(0.157)(0.167)(0.155)(0.165)(0.177)(0.190)adj. R-sq

0.105

0.096

0.133

0.108

0.093

0.133

0.104

0.096

0.135

0.100

0.093

0.129

0.093

0.115

0.139

F

18.15

16.66

22.67

20.40

16.29

23.14

18.76

17.10

23.50

18.20

16.57

22.33

17.21

20.13

23.40

N

1193

1193

1193

1190

1190

1190

1183

1183

1183

1186

1186

1186

1190

1190

1190

23.6%

35.4%

27.9%

33.8%

26.1%

32.0%

24.5%

22.3%

Share of 90-10 explained
data

30.4%

management

30.7%

22.2%

34.2%

24.0%

34.4%

24.9%

33.1%

24.2%

15.2%
28.2%

22.6%

Retail Tradedata2

1.523***

1.393***

1.504***

1.385***

1.701***

1.597***

1.789***

1.664***

2.027***(0.142)(0.148)(0.139)(0.145)(0.140)(0.146)(0.128)(0.134)(0.129)management

1.349***

0.774***

1.280***

0.704**

1.296***

0.689**

1.430***

0.750***

1.826***(0.137)
1.949***

1.202***(0.240)(0.228)(0.229)(0.219)(0.242)(0.230)(0.226)(0.215)(0.218)(0.204)

adj. R-sq

0.170

0.099

0.180

0.176

0.099

0.185

0.188

0.089

0.195

0.264

0.150

0.273

0.316

0.200

0.340

F

27.57

14.24

25.18

28.86

14.29

26.58

31.79

13.51

29.34

48.74

25.00

44.33

61.89

33.64

59.89

N

1018

1018

1018

1018

1018

1018

993

993

993

1013

1013

1013

1012

1012

1012

29.7%

64.2%

59.2%

36.3%

34.1%

38.2%

35.5%

43.3%

Share of 90-10 explained
data

32.5%

management

21.9%

12.6%

41.7%

22.9%

21.3%

11.3%

23.2%

12.2%

39.0%
31.5%

19.4%

Information Services
data2

1.429***

1.371***

1.625***

1.454***

1.513***

1.358***

1.315***

1.175***

1.250***(0.122)(0.133)(0.134)(0.142)(0.132)(0.140)(0.123)(0.135)(0.130)management

1.081***

0.299

1.715***

0.894***

1.585***

0.813***

1.400***

0.732**

1.070***(0.140)1.541***

0.927***(0.215)(0.214)(0.246)(0.246)(0.228)(0.224)(0.214)(0.226)(0.229)(0.238)

adj. R-sq

0.249

0.135

0.250

0.228

0.144

0.240

0.213

0.130

0.223

0.228

0.157

0.237

0.185

0.140

0.199

F

33.59

17.95

30.85

34.78

20.14

32.89

30.09

18.95

29.66

36.32

22.88

36.25

27.19

18.46

26.35

N

807

807

807

803

803

803

801

801

801

808

808

808

809

809

809

27.6%

49.1%

43.9%

30.5%

27.3%

26.5%

23.7%

37.8%

Share of 90-10 explained
data

28.8%

management

11.1%

3.1%

26.3%

13.7%

16.2%

8.3%

14.3%

7.5%

32.3%
23.2%

14.0%

Notes: OLS coefficients with the standard errors in parentheses. Dependent variables are innovation outcomes constructed from the responses in question 33. Key independent variables are data score 2(data) in column 1, management score (management) in column 2, and both in column 3. All regressions also include as independent variables the dummy of headquarter status, the ratio of managers
with a bachelor’s degree, the ratio of non-managers with a bachelor’s degree, the logged number of employees, the number of establishments directly competing with an establishment, and characteristics
of jobs. Share of 90-10 explained is calculated by multiplying the estimated coefficient by the 90-10 spread of each key variable and dividing it by the 90-10 spread of innovation outcome. N denotes thenumber of observations.
*** Significant at the 1 percent level.

** Significant at the 5 percent level.

* Significant at the 10 percent level.

38New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

Table 11 (a): Subsample Analysis (Establishment Size and Single- or Multi-Unit Firms: Innovation 1, 2, and 3)
Innovation1

Innovation2

Innovation3

(1)

(2)

(3)

(4)

(5)

(6)

(1)

(2)

(3)

(4)

(5)

(6)

(1)

(2)

(3)

(4)

(5)

(6)

Small

Large

Branch

Hq

Hq-sm

Hq-lg

Small

Large

Branch

Hq

Hq-sm

Hq-lg

Small

Large

Branch

Hq

Hq-sm

Hq-lg

0.477**

0.926***

0.725***

0.610***

0.342

0.900***

0.681***

0.661***

0.533***

0.759***

0.582**

0.913***

0.665***

0.742***

0.523***

0.851***

0.649**

1.026***(0.196)(0.187)(0.186)(0.192)(0.262)(0.295)(0.208)(0.173)(0.171)(0.209)(0.293)(0.308)(0.213)(0.181)(0.176)(0.212)(0.285)(0.327)0.916***

0.573***

0.928***

0.523***

0.599***

0.354

0.790***

0.836***

1.014***

0.586***

0.609***

0.594**

0.900***

0.741***

0.944***

0.660***

0.803***

0.407(0.164)(0.174)(0.180)(0.156)(0.189)(0.260)(0.169)(0.170)(0.171)(0.167)(0.207)(0.277)(0.174)(0.174)(0.173)(0.170)(0.209)(0.280)Pseudo R2

0.207

0.171

0.199

0.149

0.130

0.171

0.175

0.154

0.165

0.153

0.145

0.156

0.212

0.159

0.168

0.175

0.169

0.178

N

412

454

488

378

200

178

413

456

490

379

201

178

410

450

487

373

200

173

0.624***

0.453***

0.513***

0.560***

0.730***

0.432***

0.505***

0.583***

0.357***

0.659***

0.679***

0.702***

0.653***

0.576***

0.364***

0.775***

0.851***

0.718***(0.0984)(0.104)(0.109)(0.0922)(0.127)(0.140)(0.0926)(0.0995)(0.100)(0.0884)(0.122)(0.136)(0.101)(0.105)(0.108)(0.0958)(0.132)(0.145)0.369***

0.531***

0.390***

0.460***

0.362***

0.675***

0.468***

0.407***

0.401***

0.430***

0.414***

0.517***

0.429***

0.588***

0.449***

0.492***

0.381***

0.684***

Road Freight Transport
data
management

Wholesale Tradedatamanagement(0.0906)(0.0929)(0.104)(0.0825)(0.113)(0.124)(0.0853)(0.0876)(0.0955)(0.0785)(0.108)(0.117)(0.0920)(0.0939)(0.104)(0.0840)(0.115)(0.126)

Pseudo R2

0.103

0.113

0.110

0.102

0.103

0.125

0.110

0.107

0.105

0.110

0.107

0.126

0.104

0.129

0.106

0.119

0.119

0.137

N

1553

1478

1099

1937

1004

917

1550

1458

1093

1930

1004

911

1551

1455

1093

1927

1002

910

Medical and Other Health Services
data

0.657***

0.576***

0.876***

0.569***

0.581***

0.493**

0.712***

0.550***

1.046***

0.546***

0.657***

0.330

0.745***

0.588***

1.274***

0.528***

0.662***

0.309(0.152)(0.170)(0.234)(0.130)(0.166)(0.209)(0.153)(0.166)(0.230)(0.129)(0.169)(0.204)(0.155)(0.169)(0.254)(0.130)(0.169)(0.204)0.573***

0.573***

0.690***

0.681***

0.562***

0.781***

0.643***

0.634***

0.751***

0.594***

0.529***

0.680***

0.662***

0.585***

0.805***

0.598***

0.606***

0.545***(0.142)(0.144)(0.208)(0.116)(0.156)(0.173)(0.147)(0.139)(0.206)(0.116)(0.162)(0.170)(0.144)(0.141)(0.217)(0.115)(0.158)(0.169)Pseudo R2

0.094

0.106

0.124

0.094

0.086

0.107

0.118

0.088

0.149

0.089

0.107

0.084

0.115

0.083

0.168

0.088

0.096

0.085

N

577

627

336

868

458

410

574

626

335

865

455

410

569

624

334

859

450

409

management

Notes: Marginal effects with the standard errors in parentheses. Dependent variables are the dummy variables of innovation outcomes constructed from the responses in question 33, which take 1 if the innovation realizes at least once in five years (2013-2018).
Independent variables are data score 1, management score, the dummy of headquarter status, the ratio of managers with a bachelor’s degree, the ratio of non-managers with a bachelor’s degree, the logged number of employees, the number of establishments directlycompeting with an establishment, characteristics of jobs. In wholesale trade,disaggregated industry group dummies are also included. The marginal effects of data score 1 (data) and management score (management) are shown. See the full results of all dependent

variables in Appendix2-3, 2-4, and 2-5. Column 1 and Column 2 show the marginal effects by the probit model in samples below the median of the logged number of employees (Small) and above the median (Large). Column 3 and Column 4 show the marginal
effects by the probit model in samples in branches (Branch) and headquarters (Hq). Columns 5 and 6 show the marginal effects by the probit model using samples of headquarters, at or below the median of the logged number of employees (Hq-sm) and above themedian (Hq-Lg). Pseudo R2 denotes McFadden’s pseudo R-squared, and N denotes the number of observations.
*** Significant at the 1 percent level.

** Significant at the 5 percent level.

* Significant at the 10 percent level.

39

New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

Table 11 (b): Subsample Analysis (Establishment Size and Single- or Multi-Unit Firms: Innovation 4 and 5)
Innovation 4

Innovation 5

(1)

(2)

(3)

(4)

(5)

(6)

(1)

(2)

(3)

(4)

(5)

(6)

Small

Large

Branch

Hq

Hq-sm

Hq-lg

Small

Large

Branch

Hq

Hq-sm

Hq-lg

data

0.468**

0.721***

0.648***

0.487**

0.545**

0.517*

0.425**

0.717***

0.502***

0.587***

0.389

0.804**(0.198)(0.184)(0.178)(0.196)(0.277)(0.292)(0.200)(0.174)(0.163)(0.206)(0.286)(0.315)management

0.759***

0.642***

0.755***

0.613***

0.736***

0.441*

0.632***

0.710***

0.829***

0.456***

0.457**

0.539*(0.165)(0.174)(0.173)(0.161)(0.200)(0.265)(0.164)(0.169)(0.163)(0.167)(0.207)(0.278)

Pseudo R

0.161

0.161

0.149

0.150

0.176

0.124

0.139

0.160

0.144

0.127

0.094

0.171

N

416

454

490

380

204

176

416

455

491

380

203

177

data

0.793***

0.705***

0.553***

0.849***

1.004***

0.675***

0.695***

0.499***

0.371***

0.725***

0.884***

0.553***(0.104)(0.107)(0.110)(0.0987)(0.137)(0.146)(0.0982)(0.0992)(0.0989)(0.0941)(0.132)(0.138)management

0.597***

0.535***

0.408***

0.622***

0.549***

0.782***

0.594***

0.569***

0.441***

0.655***

0.581***

0.768***(0.0953)(0.0935)(0.105)(0.0863)(0.119)(0.127)(0.0903)(0.0883)(0.0954)(0.0833)(0.115)(0.121)

Pseudo R

0.126

0.120

0.107

0.122

0.134

0.126

0.127

0.124

0.111

0.125

0.133

0.129

N

1548

1473

1086

1932

1002

922

1560

1481

1102

1933

1006

926

0.734***

1.076***

0.583***

0.570***

0.559***

0.812***

0.648***

1.037***

0.665***

0.703***

0.574***

Road Freight Transport

2

Wholesale Trade

2

Medical and Other Health Services
data0.643***(0.152)(0.173)(0.240)(0.130)(0.167)(0.210)(0.155)(0.166)(0.235)(0.130)(0.168)(0.207)

management

0.681***

0.681***

0.651***

0.683***

0.658***

0.693***

0.660***

0.820***

0.777***

0.729***

0.624***

0.847***(0.144)(0.145)(0.210)(0.117)(0.160)(0.174)(0.148)(0.140)(0.207)(0.117)(0.162)(0.173)Pseudo R

0.102

0.116

0.137

0.106

0.097

0.121

0.132

0.118

0.157

0.119

0.118

0.131

N

575

621

335

861

454

407

573

627

336

864

454

410

2

Notes: See Table 11 (a).

40

New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

Table 12: Subsample Analysis (Degree of Decentralization in Decision-Making)Innovation 1

Innovation 2

Innovation 3

Innovation 4

Innovation 5

(1)

(2)

(1)

(2)

(1)

(2)

(1)

(2)

(1)

(2)

Centralized

Decentralized

Centralized

Decentralized

Centralized

Decentralized

Centralized

Decentralized

Centralized

Decentralized

0.661***

0.831***

0.550**

0.521*

0.420*

0.616**

0.619***

0.647**

0.606***

0.331(0.253)(0.293)(0.224)(0.285)(0.234)(0.285)(0.239)(0.280)(0.215)(0.260)1.007***

0.853***

0.826***

1.294***

0.966***

0.911***

0.859***

0.606**

0.679***

0.957***(0.256)(0.261)(0.231)(0.269)(0.239)(0.261)(0.244)(0.253)(0.220)(0.251)

Pseudo R2

0.230

0.183

0.159

0.225

0.186

0.187

0.173

0.138

0.147

0.169

N

278

209

280

209

277

209

277

212

279

211

0.449***

0.531***

0.359**

0.283*

0.285*

0.326**

0.344**

0.792***

0.367***

0.363**(0.162)(0.160)(0.153)(0.147)(0.161)(0.161)(0.153)(0.178)(0.140)(0.158)0.486***

0.381**

0.483***

0.447***

0.772***

0.275*

0.446***

0.426***

0.466***

0.457***

Road Freight Transport
data
management

Wholesale Tradedatamanagement(0.159)(0.149)(0.155)(0.138)(0.166)(0.152)(0.158)(0.165)(0.143)(0.151)

Pseudo R2

0.110

0.156

0.104

0.151

0.126

0.131

0.113

0.155

0.140

0.126

N

538

524

519

510

525

511

519

500

515

512

1.106***

0.584*

1.105***

0.978***

1.311***

1.209***

0.930***

1.173***

1.165***

0.901***(0.335)(0.332)(0.324)(0.326)(0.353)(0.378)(0.320)(0.357)(0.337)(0.323)0.813***

0.607**

0.978***

0.561**

1.045***

0.595*

0.876***

0.500*

0.805***

0.769***

Medical and Other Health Services
data
management(0.302)(0.285)(0.310)(0.272)(0.316)(0.308)(0.297)(0.295)(0.300)(0.279)Pseudo R2

0.164

0.164

0.186

0.124

0.214

0.143

0.140

0.145

0.198

0.143

N

173

156

173

155

172

155

172

154

173

156

Notes: Marginal effects with the standard errors in parentheses. Dependent variables are the dummy variables of innovation outcomes constructed from the responses in question 33, which take 1 if the innovation realizes at least once in five years (2013-2018).
Independent variables are data score 1, management score, the dummy of headquarter status, the ratio of managers with a bachelor’s degree, the ratio of non-managers with a bachelor’s degree, the logged number of employees, the number of establishments directlycompeting with an establishment, characteristics of jobs. In wholesale trade, disaggregated industry group dummies are also included. The marginal effects of data score 1 (data) and management score (management) are shown. See the full results of all dependent
variables in Appendix 2-6. Columns 1 and 2 show the marginal effects by the probit model in samples at or below the median of the decentralization score in decision-making (Centralized) and above the median (Decentralized). Pseudo R2 denotes McFadden’s
pseudo R-squared, and N denotes the number of observations.
*** Significant at the 1 percent level.

** Significant at the 5 percent level.

* Significant at the 10 percent level.

41New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

Appendix 1-1 Scoring JP-MOPS Survey Questions - Data Score 1
Question

19-1

19-2

20

21

Text

In 2013 and 2018, what best describes the availability of data to
support decision making at this establishment?

Following up on Question 19-1 regarding data to support decisionmaking: In 2018, how much is the data collected as“systematically organized electronic data”? Mark one box for thedata with the longest accumulation period.

In 2013 and 2018, what best describes the use of data to support
decision making at this establishment?

Consider each of the following
sources of data and rate howfrequently each source is used indecision making at thisestablishment in 2018 and wasused in 2013.

Basic Performanceindicators (e.g., sales, costs,
inventories, etc.)
Formal or informal feedback
from managers
Formal or informal feedback
from non-managers
Data from outside the firm
(suppliers, customers, outside
data providers)

Demand forecasting

22-1

23-1

How frequently is/was each ofthese activities influenced by data
analysis at this establishment?
Mark one box for each year.

Response
Data to support decision making are notavailable
A small amount of data to support
decision making is available
A moderate amount of data to support
decision making is available
A great deal of data to support decision
making is available
All the data we need to support decision
making is available
Not being collected at all
Has been collected for the past year
Has been collected for the past 2 years
Has been collected for the past 3 to 5
years
Has been collected for the past 6 to 10
years
Has been collected for more than 10
years
Decision making does not use data
Decision making relies slightly on data
Decision making relies moderately on
data
Decision making relies heavily on data
Decision making relies entirely on data
Daily
WeeklyMonthly
YearlyNever

Following up on Question 23-1, is artificial intelligence (AI; fordetails, see Section D on the next page) used for predictive
analytics? Please choose the most appropriate answer below.
(Notes) Question numbers and texts in the 2018 JP-MOPS are shown.
23-2

42

0
1/4
1/2
3/4
1
0
1/5
2/5
3/5
4/5
1
0
1/4
1/2
3/4
1
1
3/4
1/2
1/4
0

See above
See above
See above
Never
Slightly
Moderately
Generally
Entirely

Investment decisions (e.g.,regarding capital equipment,
new branches)
Advertising
Purchasing, shipping,inventory control, distribution
Back office tasks (e.g.,human resources, accounting)

How frequently does this establishment typically rely on predictive
analytics (statistical models that provide forecasts in areas such as
demand, production, or human resources)?

Score

0
1/4
1/2
3/4
1
See above
See above
See above
See aboveDaily
WeeklyMonthly
Yearly
Never
Used

1
3/4
1/2
1/4
0
1

Not used0New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

Appendix 1-2 Scoring JP-MOPS Survey Questions - Data Score 2
Question(2017)

Question(2018)

Text

Question Text

Key Performance
Indicator

24

Consider each of
the following
sources of data andrate how frequently
each source is usedin decision making
at thisestablishment in2015 and was usedin 2010.

Formal or informal
feedback from managers
21
Formal or informal
feedback from nonmanagers

Consider each of
the following
sources of data
and rate howfrequently eachsource is used indecision making
at thisestablishment in2018 and wasused in 2013.

Data from outside thefirm (suppliers,
customers, outside data
providers)25

How frequently
is/was each ofthese activities
influenced by data
analysis at thisestablishment?
Mark one box foreach year.

Demand forecasting

22_1
Supply chain management

Advertising

43

How frequently
is/was each ofthese activities
influenced bydata analysis at
thisestablishment?
Mark one box foreach year.

Basic
Performanceindicators(e.g., sales,
costs,
inventories,
etc.)
Formal or
informalfeedback frommanagers
Formal or
informalfeedback from
non-managers
Data fromoutside thefirm (suppliers,
customers,
outside data
providers)
Demandforecasting
Purchasing,shipping,
inventory
control,
distribution
Advertising

Response

Score

Daily
WeeklyMonthly
Yearly

1
3/4
1/2
1/4

Never

0

See above

See above

See above

Never
Slightly
Moderately
Generally
Entirely

See above

See above

0
1/4
1/2
3/4
1

New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

Appendix 1-3 Scoring JP-MOPS Survey Questions - Management Score
Question

Text

2

In 2013 and 2018, how many key performance indicators(KPIs) were monitored at this establishment?

3(a) and3(b)During 2013 and 2018, how frequently were the key
performance indicators reviewed by (a) managers and (b)non-managers at this establishment?

4

During 2013 and 2018, where were the display boards
showing KPIs located at this establishment?

5

In 2013 and 2018, what best describes the time frame ofsales targets at this establishment?

6

How much effort was required (or is expected to berequired) for this establishment to meet its sales targets?
Please choose the most appropriate answer below.

7

At this establishment, who was aware of the sales
targets? Please choose the most appropriate answer
below.

8(a) and8(b)In 2013 and 2018, what were (a) non-managers' and (b)managers’ performance bonuses usually based on at thisestablishment?

9(a) and9(b)In 2013 and 2018, when sales targets were achieved,
what percentage of employees received (or is expected toreceive ) a bonus based on the degree to which sales
targets were achieved at this establishment? Pleasechoose the most appropriate answer for (a) non-managers
and (b) managers.

10(a)
and
10(b)

In 2013 and 2018, what were (a) non-managers' and (b)managers’ performance bonuses usually based on at thisestablishment? At this establishment, when deciding
whether to promote an employee, what is the decision
mainly based on? Please choose the most appropriate
answer for (a) non-managers and (b) managers.

At this establishment, if an employee is found to be11(a)doing a poor job, how long does it take for them to beandtransferred to another position or be dismissed? Please11(b)choose the most appropriate answer for (a) non-managers
and (b) managers.
(Notes) Question numbers and texts in the 2018 JP-MOPS are shown.

44

Response
1-2 key performance indicators
3-9 key performance indicators
10 or more key performance indicators
No key performance indicators (if no key performanceindicators in both years, SKIP to Question 5)
YearlyQuarterlyMonthly
WeeklyDaily
Hourly or more frequently
Never
All display boards were located in one place (e.g., at
the sales place, the backyard, the office )Display boards were located in multiple places
We did not have any display boards
Main focus was on short-term (less than one year) sales
targets
Main focus was on long-term(more than one year)
sales targets
Combination of short-term and long-term sales targets
No sales targets (If no sales targets in both years, SKIP
to Question 10)
Possible to achieve without much effort
Possible to achieve with some effort
Possible to achieve with normal amount of effort
Possible to achieve with more than normal effort
Only possible to achieve with extraordinary effort
Only senior managers (e.g., general managers,
directors)
Most managers and some non-managers
Most managers and most non-managers
All managers and most non-managers
Their own performance as measured by sales targets
Their team or group performance as measured bysales targets
Their establishment's performance as measured bysales targets
Their company's performance as measured by sales
targets
No performance bonuses
0%
1-33%
34-66%
67-99%
100%
Sales targets not met
Promotions were based solely on performance andability
Promotions were based partly on performance andability, and partly on other factors (e.g., tenure or
family connections)
Promotions were based mainly on factors other thanperformance and ability (e.g., tenure or family
connections)Non-managers/Managers are normally not promoted
Less than six months
More than six months
There were few or no cases in which employees weretransferred or dismissed.

Score
1/3
2/3
1
0
1/6
1/3
1/2
2/3
5/6
1
0
1/2
1
0
1/3
2/3
1
0
0
1/4
1/2
3/4
1
0
1/3
2/3
1
1
3/4
1/2
1/4
0
1/5
2/5
3/5
4/5
1
0
1
2/3

1/3
0
1
1/2
0

New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

Appendix 1-4 Scoring JP-MOPS Survey Questions - Decentralization Score
Question

Text

13

In 2013 and 2018, where were decisions on hiring permanent fulltime employees made for this establishment? Mark one box foreach year.

14

15

16

17

18

In 2013 and 2018, where were decisions to give an employee a pay
increase of at least 10% made for this establishment? Mark one
box for each year.

In 2013 and 2018, where were decisions on new products/services
introductions made for this establishment? Mark one box for eachyear.

In 2013 and 2018, where were product/service pricing decisions
made for this establishment? Mark one box for each year.

In 2013 and 2018, where were advertising decisions for
products/services made for this establishment? Mark one box foreach year.

In 2013 and 2018, what was the yen amount that could be used topurchase a fixed/capital asset for this establishment without priorauthorization from headquarters? Mark one box for each year.

(Notes) Question numbers and texts in the 2018 JP-MOPS are shown.

45

Response
Only at this establishment
Only at headquarters
Both at this establishment and at
headquarters
Other
Only at this establishment
Only at headquarters
Both at this establishment and at
headquarters
Other
Only at this establishment
Only at headquarters
Both at this establishment and at
headquarters
Other
Only at this establishment
Only at headquarters
Both at this establishment and at
headquarters
Other
Only at this establishment
Only at headquarters
Both at this establishment and at
headquarters
Other
Under 100,000 yen
100,000 to 1 million yen
1 million to 10 million yen
10 million to 100 million yen
100 million yen or more

Score
1
0
1/2
1
0
1/2
1
0
1/2
1
0
1/2
1
0
1/2
0
1/4
1/2
3/4
1

New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

Appendix 1-5 Scoring JP-MOPS Questions - Other Categorical Variables
Variable Name

Question(2018)The dummy ofheadquarter

12

statusResponse

Was the headquarters for this company at the sameYes (If yes in both years, SKIP to Question 19)

1

No

0

location as this establishment? Mark one box for eachyear.

The number ofAt present, in 2018, about how many establishments are

establishments
directly

Text

31

competing withdirectly competing with this establishment to win
customers, etc. Please choose the most appropriate
answer (one answer only).

an establishment
The ratio ofmanagers with abachelor’s28(a)degreeemployees had a university degree? Please choose themost appropriate answer for (a) managers and (b) non-The ratio of nonmanagers with abachelor’s

At this establishment, roughly what percentage ofmanagers.
28(b)degreeThe presence ofCIO

At present in 2018, does the firm to which this29

establishment belongs have a CIO?

Please choose themost appropriate answer (one answer only).

Specialization or
coordination
Creativity or
efficiency

34-1

34-2

Which of the following do you feel is more important
for the performance of this establishment?

Score

0

1

1–2 establishments

2

3–5 establishments

3

6–10 establishments

4

More than 10 establishments

5Less than 20%

1

20% or more, but less than 40%

2

40% or more, but less than 60%

360% or more, but less than 80%

480% or more

5

0%

1

Less than 10%2

10% or more, but less than 20%

3

20% or more

4

Has a full-time CIO

3

Has a CIO who concurrently holds other positions

2

Does not have a CIO

1

Do not know-

Specialization is more important

1

Equally important2

Coordination is more important3

Which do you feel is more important in thisCreativity is more important1

establishment, employees’ creativity or the efficiency

Equally important2of performing tasks?

Efficiency is more important

3(Notes) Question numbers and texts in the 2018 JP-MOPS are shown. In questions 34-1 and 34-2, establishments are asked to mark somewhere on the linein the figure below. We give a numerical number of 1-9 from the points where the establishments mark. We categorize 1-4 as “specialization/creativity ismore important,” 5 as “equally important,” and 6-9 as “coordination/efficiency is more important.”

1

Specialization/Creativity

3

5

7

Equally important

9

Coordination/Efficieny
is more importantis more important46

New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

Appendix 2-1: Data Activities and Innovation Performance by the Probit Model - Marginal Effects in All Independent
Variables
Road Freight Transport

data

Innovation

Innovation

Innovation

Innovation

1

2

3

4

(1)

(2)

(3)

(1)

2.598***

1.836***

1.049***

2.487***(0.326)(0.351)(0.327)(0.316)

management
dumhq-0.160*(0.097)

univm
unive

1.964***

2.020***(0.316)(0.325)-0.018-0.008(0.101)-0.256***

(2)

(3)

(1)

0.796**

2.546***(0.341)(0.322)(0.323)2.094**

2.084****(0.309)(0.318)-0.106-0.1091.709***(3)

1.761**

0.858****(0.348)(0.325)

2.107**

2.059****(0.315)(0.321)-0.136-0.1322.249***(0.314)

(2)

(3)

(1)

0.800**

2.231***(0.339)(0.320)(0.313)(0.338)(0.321)1.812**

1.681**

1.737**

1.809**

*

*

*

*

1.530***(0.307)(0.314)-0.276***-0.150-0.130(0.098)-0.305***(2)
1.536***(3)
0.676**(0.306)(0.316)-0.181*-0.130(0.101)(0.094)(0.099)(0.096)(0.100)(0.094)(0.099)(0.094)-0.046-0.080*-0.079*-0.019-0.055-0.047-0.034-0.072*-0.073*-0.040-0.072*-0.0620.024-0.0050.014(0.042)(0.044)(0.040)(0.041)(0.043)(0.040)(0.041)(0.043)(0.040)(0.041)(0.043)(0.041)(0.041)(0.043)0.182***

0.139**

0.101*

0.079

0.065

0.065

0.035

(0.060)(0.061)(0.058)(0.060)(0.060)(0.059)(0.060)(0.060)(0.058)(0.059)(0.059)(0.058)(0.060)(0.060)

0.096

0.030

0.048

0.102

0.026

0.042

0.082

0.004

0.021

0.032

-0.032-0.0140.032-0.037-0.018(0.067)(0.070)(0.070)(0.067)(0.070)(0.070)(0.068)(0.067)(0.069)(0.070)cmp

0.081**

0.065**

0.069**

0.065**

0.047

0.066**

0.105***

0.067**

0.052*

0.076**(0.032)(0.033)(0.033)(0.031)(0.032)(0.032)(0.031)(0.031)(0.032)

Q34_2score

CIO

Constantterm

Pseudo R2
N

0.117**

0.071

0.054

0.119**(0.070)(0.070)0.089**

0.103**(0.066)**(0.032)(0.032)(0.031)0.111***

0.101*(0.099)(0.059)e

0.055(0.097)lemp

Q34_1scor

0.089(0.100)5(1)(0.041)0.132**(0.099)-0.281***(2)

Innovation(0.068)(0.068)0.098**

0.124****(0.032)(0.032)(0.031)-0.082-0.059-0.050-0.025-0.003-0.007-0.005

0.021

0.021

-0.056-0.036-0.034-0.051-0.033-0.028(0.058)(0.060)(0.060)(0.058)(0.059)(0.059)(0.058)(0.059)(0.060)(0.057)(0.058)(0.059)(0.058)(0.059)(0.059)-0.093-0.109*-0.087-0.077-0.100-0.034-0.024-0.051(0.060)(0.061)(0.061)(0.061)(0.062)(0.062)0.276**

0.302**

0.254**

0.281****(0.087)(0.088)(0.091)-1.548***-1.601***-0.127**-0.118*-0.145**-0.094-0.085-0.111*-0.101*(0.061)(0.062)(0.062)(0.060)(0.062)(0.062)(0.060)0.367***

0.265***

0.260***

0.284***

0.168*

0.162*

0.368***(0.085)(0.088)(0.088)(0.088)(0.091)(0.092)(0.090)--2.528***

1.967***-1.436***-2.187***-2.423***-1.966***2.030***(0.062)(0.062)0.257**

0.244****(0.092)(0.092)--2.268**

1.688****

0.371***(0.086)

0.345***-1.889***1.952*****(0.092)(0.094)--1.622**

1.241****(0.391)(0.401)(0.394)(0.381)(0.389)(0.382)(0.385)(0.393)(0.386)(0.376)(0.382)(0.377)(0.377)(0.382)(0.380)

0.151

0.185

0.159

0.132

0.171

0.138

0.148

0.187

0.152

0.133

0.163

0.138

0.124

0.151

0.129

866

866

833

869

869

835

860

860

827

870

870

837

871

871

837

Wholesale Trade
Innovation

Innovation

Innovation

Innovation

1

2

3

4

(1)
data

(2)

(3)

(1)

1.907***

1.490***

1.066***

2.053***(0.188)(0.198)(0.187)(0.193)1.207***

1.177***(0.181)(0.185)manageme
nt

(2)

(3)

1.619**

1.020****(0.204)(0.191)1.270***(0.185)

(1)

(2)

(3)

1.687**

1.083****(0.200)(0.186)1.322**

1.342****(0.189)(0.182)2.128***(0.190)Innovation
5

(1)

(2)

(3)

2.030**

1.355****(0.201)(0.186)1.353**

1.465****(0.185)(0.181)2.503***(0.192)(1)
2.287***(2)(3)

1.748**

1.013****(0.203)(0.188)1.279**

1.681**

1.550*****(0.183)(0.185)(0.187)-0.129**-0.138**(0.057)(0.193)dumhq-0.113**-0.068-0.063-0.134**-0.087-0.091-0.154***-0.107*-0.087-0.208***0.154**-0.165***-0.188***

*
univm
unive

lemp

cmp

Q34_1score(0.054)(0.055)(0.056)(0.056)(0.057)(0.058)(0.055)(0.055)(0.056)(0.054)(0.055)(0.056)(0.056)(0.056)0.042**

0.032

0.017

0.052**

0.042**

0.026

0.032

0.021

0.011

0.039*

0.028

0.015

0.043**

0.029

0.021

(0.020)(0.020)(0.020)(0.021)(0.021)(0.021)(0.020)(0.020)(0.020)(0.020)(0.020)(0.020)(0.020)(0.021)(0.021)

0.034

0.008

0.015

0.000

-0.028-0.035

0.029

0.000

0.003

0.012

-0.021-0.0020.025-0.012-0.011(0.037)(0.037)(0.036)(0.038)(0.038)(0.037)(0.037)(0.037)(0.036)(0.037)(0.038)(0.036)(0.037)(0.038)(0.036)

0.044

0.017

0.030

0.059

0.028

0.057

0.100**

0.069*

0.089**

0.145***

0.111**

0.145****

0.116***

0.078*

0.101**(0.039)(0.040)(0.040)(0.041)(0.041)(0.042)(0.040)(0.040)(0.041)(0.040)(0.040)(0.041)(0.041)(0.042)(0.042)0.043**

0.040**

0.048***

0.051***

0.047**

0.052**

0.057**

0.063****(0.017)(0.017)(0.017)(0.018)(0.018)(0.018)--0.096***

0.101***-0.074**-0.068**(0.032)(0.032)(0.032)(0.033)--0.124***

0.137***-0.101***(0.032)

Q34_2score

CIO

Constant
term-0.132***(0.033)(0.033)(0.034)0.190***

0.209***

0.186***(0.050)(0.050)(0.051)(0.052)-1.284**-1.404**1.605***

0.026

0.022

0.028

0.030*

0.025

0.032*(0.017)(0.017)(0.017)(0.017)(0.017)(0.018)(0.018)(0.018)-0.069**-0.063**-0.058*-0.081**-0.073**-0.088***-0.083**-0.075**(0.033)(0.032)(0.032)(0.032)(0.031)(0.032)(0.032)(0.032)(0.032)(0.032)---0.166***-0.143***0.133**

0.139**-0.095***-0.084**-0.100***-0.046-0.034-0.052**(0.033)(0.033)(0.033)(0.033)(0.033)(0.033)(0.034)(0.034)0.217**

0.237**

0.174**

0.197**

0.229**

0.249**

*

*

*

*

(0.051)(0.051)(0.050)(0.050)--2.137**

1.826****

0.067**

0.152***(0.033)-*(0.017)-0.160***0.211***-1.351***-0.060***(0.034)(0.034)0.165**

0.180****(0.053)(0.052)1.653***

1.314**(0.033)0.241***(0.051)-1.848***0.199***(0.050)-1.635***

1.963***

0.070**

1.532**

0.257***(0.053)**(0.053)(0.053)-1.454**1.825**-1.466***(0.550)(0.556)(0.558)(0.559)(0.565)(0.568)(0.555)(0.561)(0.564)(0.596)(0.601)(0.610)(0.568)(0.574)

Pseudo R2

0.088

0.099

0.089

0.089

0.103

0.088

0.097

0.111

0.095

0.104

0.121

0.098

0.100

0.123

0.097

N

3047

3047

2961

3028

3028

2945

3027

3027

2943

3028

3028

2947

3043

3043

2959

47

(0.610)New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

Medical and Other Health Services
Innovation

Innovation

Innovation

Innovation

1

2

3

4

(1)
data

(3)

(1)

2.142***

1.580***

0.952***

2.148***(0.272)(0.286)(0.272)(0.269)1.695***

1.671***(0.254)(0.263)-0.207**-0.161*-0.205**manageme
nt
dumhq

(2)

-0.169*(2)(3)

1.640**

0.968****(0.283)(0.271)

1.528**
*

(1)

(2)

(3)

1.700**

1.064****(0.288)(0.272)1.420**

1.568****(0.254)(0.262)-0.127-0.174*2.218***(0.274)-0.083Innovation
5

(1)

(2)

(3)

1.712**

1.103****(0.286)(0.272)

1.440**

1.643****(0.254)(0.261)-0.040-0.0762.245***(0.272)-0.128(1)2.479***(2)(3)

1.899**

1.270****(0.289)(0.275)1.414**

1.844**

1.538*****(0.254)(0.261)(0.259)(0.265)-0.081-0.078

0.077

0.042

(0.275)0.015(0.086)(0.087)(0.089)(0.087)(0.088)(0.090)(0.086)(0.087)(0.089)(0.086)(0.087)(0.089)(0.088)(0.089)(0.091)

univm

0.005

0.012

0.027

0.025

0.031

0.038

-0.001

0.005

0.006

-0.009-0.003

0.005

0.024

0.035

0.042

(0.028)(0.028)(0.029)(0.028)(0.028)(0.029)(0.028)(0.028)(0.029)(0.028)(0.028)(0.029)(0.028)(0.029)(0.029)unive

0.093*

0.069

0.061

0.087*

0.067

0.071

0.112**

0.091*

0.088*

0.117**

0.096*

0.084*

0.082*

0.055

0.027

(0.048)(0.049)(0.049)(0.048)(0.049)(0.049)(0.048)(0.049)(0.049)(0.049)(0.049)(0.049)(0.049)(0.050)(0.049)lemp

cmp

Q34_1scoreQ34_2score

0.007-0.075*-0.053-0.023-0.098**-0.077*-0.012-0.087**-0.058-0.017-0.095**-0.051

0.011-0.077*-0.026(0.041)(0.043)(0.044)(0.042)(0.044)(0.044)(0.041)(0.043)(0.044)(0.041)(0.043)(0.044)(0.042)(0.045)(0.045)0.055*

0.036

0.049*

0.047*

0.029

0.056*

0.070**

0.051*

0.068**

0.093***

0.074**

0.078***

0.057*(0.028)(0.029)(0.029)(0.028)(0.029)(0.029)(0.029)(0.029)(0.029)(0.028)(0.029)(0.029)(0.028)(0.029)

0.024

0.035

0.054

0.059

0.057

0.017

0.024

0.021

-0.038-0.034-0.025

0.064

0.073

0.071

(0.050)(0.049)(0.050)(0.050)(0.049)(0.050)(0.051)(0.049)(0.050)(0.051)(0.050)(0.051)(0.051)---0.129**-0.129**-0.105**-0.166***-0.152***-0.141***-0.109**-0.127**-0.126**0.164**

0.157**

0.166**-(0.051)(0.052)(0.051)(0.051)0.139***(0.052)(0.051)(0.052)(0.052)(0.051)(0.051)0.129**-0.164***(0.052)(0.052)**(0.052)(0.053)

0.101

0.100

0.131

0.029

0.030

0.052

0.067

0.064

0.089

0.054

0.052

0.094

0.061

0.066

0.095

(0.104)(0.107)(0.106)(0.105)(0.107)(0.105)(0.104)(0.106)(0.104)(0.105)(0.107)(0.105)(0.107)(0.109)(0.107)

-

-

-

-

-

-

1.258***

1.064***-0.600*-1.365***1.353**

1.111**-1.032***-1.403***1.412**

1.119****(0.312)(0.309)(0.316)(0.311)(0.308)2*(0.029)(0.050)-1.285***term

0.077**(0.049)(0.051)

Constant

*

0.021

*

CIO

0.094**-0.874***0.850***(0.309)(0.312)(0.309)(0.312)-1.216***1.207***(0.311)(0.315)(0.311)(0.315)**(0.320)(0.314)

Pseudo R

0.072

0.099

0.082

0.071

0.093

0.072

0.071

0.095

0.072

0.076

0.102

0.077

0.087

0.118

0.087

N

1204

1204

1151

1200

1200

1148

1193

1193

1141

1196

1196

1142

1200

1200

1148

(Notes) See Table 7.

48

New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

Appendix 2-2: Data Activities and Innovation Performance by the Ordered Probit Model - Coefficients in All Independent Variables
Road Freight Transport
Innovation1

Innovation2

Wholesale Trade
Innovation3

Innovation4

Innovation5

Innovation1

Medical and Other Healthcare Services

Innovation2

Innovation3

Innovation4

Innovation5

Innovation1

Innovation2

Innovation3

Innovation4

Innovation5

Coefficients
data
management
dumhq
univm
unive
lemp
cmp
Q34_1score
Q34_2score
CIO

2.023***

1.773***

1.728***

1.975***

1.885***

1.483***

1.567***

1.693***

1.927***

1.756***

1.641***

1.880***

1.828***

1.789***

2.052***(0.313)(0.289)(0.295)(0.302)(0.288)(0.163)(0.163)(0.164)(0.166)(0.163)(0.258)(0.250)(0.261)(0.254)(0.249)1.814***

1.642***

1.746***

1.642***

1.537***

1.127***

1.165***

1.150***

1.309***

1.551***

1.438***

1.258***

1.424***

1.434***

1.552***(0.282)(0.262)(0.272)(0.271)(0.260)(0.149)(0.148)(0.149)(0.150)(0.149)(0.225)(0.217)(0.225)(0.222)(0.216)-0.004-0.046-0.078-0.115-0.146*-0.099**-0.122***-0.114**-0.181***-0.106**-0.162**-0.081-0.055-0.0820.027(0.090)(0.085)(0.086)(0.087)(0.084)(0.044)(0.044)(0.044)(0.045)(0.044)(0.076)(0.075)(0.077)(0.076)(0.075)-0.066*-0.037-0.043-0.043

0.017

0.026

0.047***

0.026-0.0060.010-0.0080.015-0.007-0.027-0.010(0.037)(0.034)(0.035)(0.035)(0.034)(0.017)(0.016)(0.017)(0.017)(0.016)(0.025)(0.024)(0.025)(0.025)(0.024)0.100*

0.098**

0.081

0.070

0.061

0.001

-0.0240.001-0.001-0.006

0.066

0.067

0.072*

0.088**

0.070(0.053)(0.050)(0.051)(0.051)(0.049)(0.031)(0.031)(0.031)(0.032)(0.031)(0.044)(0.042)(0.044)(0.044)(0.043)

0.009

0.029

0.011

-0.021-0.001

0.037

0.052*

0.074**

0.090***

0.066**-0.066*-0.103***-0.093**-0.108***-0.108***(0.059)(0.056)(0.057)(0.058)(0.057)(0.031)(0.031)(0.031)(0.031)(0.031)(0.038)(0.037)(0.038)(0.037)(0.037)0.063**

0.055**

0.080***

0.098***

0.080***

0.038***

0.039***

0.055***

0.041***

0.023*

0.043*

0.051**

0.055**

0.079***

0.060**(0.029)(0.027)(0.028)(0.028)(0.027)(0.014)(0.014)(0.014)(0.014)(0.014)(0.026)(0.025)(0.026)(0.026)(0.025)-0.039-0.0080.015-0.0030.029-0.083***-0.082***-0.074***-0.068***-0.055**

0.015

0.062

0.043

0.024

0.116***(0.052)(0.049)(0.050)(0.051)(0.049)(0.026)(0.026)(0.026)(0.026)(0.026)(0.044)(0.043)(0.045)(0.044)(0.043)-0.078-0.039-0.055-0.0360.009-0.093***-0.145***-0.122***-0.104***-0.049*-0.146***-0.159***-0.126***-0.081*-0.142***(0.054)(0.051)(0.052)(0.052)(0.051)(0.027)(0.027)(0.027)(0.027)(0.027)(0.046)(0.044)(0.046)(0.045)(0.044)0.187***

0.113*

0.161**

0.156**

0.147**

0.166***

0.182***

0.193***

0.155***

0.153***

0.082

0.082

0.121

0.103

0.132

(0.070)(0.069)(0.069)(0.069)(0.068)(0.038)(0.038)(0.038)(0.038)(0.038)(0.091)(0.089)(0.091)(0.090)(0.088)

Cut
cut1
cut2
cut3

2.426***

2.002***

2.143***

2.200***

2.061***

1.690***

1.896***

2.114***

1.526***

1.638***

1.105***

0.967***

1.367***

1.296***

1.324***(0.347)(0.323)(0.328)(0.334)(0.321)(0.471)(0.477)(0.473)(0.467)(0.438)(0.276)(0.269)(0.280)(0.276)(0.269)3.357***

2.910***

3.008***

3.072***

2.964***

2.627***

2.795***

2.969***

2.687***

2.657***

2.169***

1.874***

2.225***

2.284***

2.202***(0.353)(0.328)(0.333)(0.339)(0.326)(0.472)(0.477)(0.474)(0.468)(0.439)(0.279)(0.271)(0.283)(0.279)(0.272)

4.156***

3.685***

3.803***

3.825***

3.686***

3.145***

3.443***

3.627***

3.356***

3.377***

2.899***

2.695***

3.071***

3.089***

2.983***(0.363)(0.335)(0.340)(0.347)(0.333)(0.473)(0.478)(0.475)(0.469)(0.440)(0.285)(0.276)(0.289)(0.286)(0.276)

Pseudo R2

0.127

0.100

0.109

0.115

0.106

0.062

0.070

0.069

0.075

0.070

0.069

0.066

0.069

0.069

0.077

N

866

869

860

870

871

3047

3036

3027

3037

3043

1204

1200

1193

1196

1200

(Notes) See Table 8. Cut denotes the estimated cutpoints.

49

New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

Appendix 2-3: Subsample Analysis (Establishment Size and Single- or Multi-Unit Firms) - Full Results in Road Freight Transport
data
management
dumhq
univm
unive

Innovation 1
Small
1.321**(0.544)2.535***(0.459)-0.031(0.150)-0.073(0.061)0.118(0.089)Large
2.332***(0.470)1.442***(0.438)-0.038(0.138)-0.098*(0.058)0.175**(0.085)0.114**(0.048)-0.177**(0.087)-0.054(0.092)0.181(0.135)-2.349***(0.454)
0.207
412

0.019
(0.045)0.055(0.083)-0.188**(0.085)0.349***(0.116)-2.456***(0.457)0.171
454

Innovation 4
1.206**(0.509)1.956***(0.426)-0.168(0.140)-0.036(0.059)0.090(0.086)1.811***(0.461)1.610***(0.438)-0.140(0.137)-0.116**(0.058)0.078(0.083)

lemp
cmp
Q34_1score
Q34_2score
CIO
constant_term
Pseudo R2Ndatamanagement
dumhq
univm
unive
lemp
cmp

0.115**(0.046)
Q34_1score-0.086(0.083)
Q34_2score-0.020(0.088)CIO
0.187(0.128)-2.055***constant_term(0.425)
2
Pseudo R
0.161
N
416
(Notes) See Tables 11 (a) and (b).

0.073*(0.044)0.015(0.083)-0.135(0.085)0.373***(0.121)-2.083***(0.449)
0.161
454

0.156
(0.141)
0.162(0.149)Innovation 2
Small
1.709***(0.523)
1.983***(0.423)-0.139(0.142)-0.032(0.060)0.056(0.086)Large
1.735***(0.453)2.193***(0.447)-0.085(0.138)-0.078(0.057)0.106(0.084)0.043(0.070)-0.141(0.126)-0.211(0.141)0.363(0.251)-1.728***(0.625)0.130
200-0.019(0.069)0.071(0.140)-0.131(0.133)0.463(0.295)-2.789***(0.802)0.171
178

0.086*(0.046)0.009(0.084)-0.096(0.091)0.230(0.140)-2.044***(0.437)
0.175
413

0.009
(0.045)-0.020(0.084)-0.076(0.085)0.127(0.121)-1.762***(0.453)0.154
456

1.319**(0.533)1.659***(0.438)1.581*(0.809)2.136***(0.588)1.334*(0.756)
1.139*(0.685)-0.052(0.078)0.187*(0.096)0.062(0.117)0.079*(0.047)-0.003(0.090)-0.103(0.096)
0.554***(0.187)-2.869***(0.633)0.150
380-0.044(0.101)
0.109(0.133)0.006(0.134)0.261*(0.144)

Innovation 5
1.068**(0.502)
1.589***(0.412)-0.265*(0.138)0.023(0.059)0.063(0.085)1.905***(0.461)1.886***(0.450)-0.113(0.139)-0.040(0.058)0.070(0.085)0.050(0.068)0.042(0.125)-0.155(0.141)0.681***(0.247)-2.797***(0.627)
0.176
204

0.104
(0.067)-0.057(0.134)-0.091(0.133)0.317(0.306)-2.349***(0.757)
0.124
176

0.073
(0.045)-0.056(0.083)-0.039(0.089)0.252*(0.136)-1.469***(0.419)
0.139
416

0.031
(0.045)-0.014(0.084)-0.006(0.086)
0.259**(0.126)-2.050***(0.456)0.160
455

Branch
1.821***(0.467)2.328***(0.452)Hq
1.730***(0.549)1.482***(0.444)

Hq-sm
1.072
(0.826)
1.878***(0.599)Hq-lg
2.366***(0.781)0.931(0.685)-0.124**(0.050)0.142*(0.077)-0.010(0.086)0.116**(0.045)-0.054(0.079)-0.087(0.082)0.216**(0.101)-2.652***(0.514)
0.199
488

0.045
(0.080)0.122(0.098)0.082(0.121)
0.011(0.048)-0.058(0.092)-0.167*(0.096)
0.436**(0.185)-2.563***(0.639)0.149
378-0.017(0.105)0.095(0.136)1.642***(0.450)1.914***(0.438)-0.070(0.048)0.004(0.075)-0.086(0.084)0.118***(0.044)-0.065(0.077)-0.058(0.079)0.194*(0.100)-1.584***(0.474)
0.149
490

50

Branch
1.426***(0.457)2.714***(0.458)Hq
1.927***(0.530)1.488***(0.425)

Hq-sm
1.543**(0.781)1.616***(0.550)Hq-lg
2.290***(0.771)1.489**(0.695)-0.075(0.049)0.020(0.076)-0.017(0.088)0.049(0.045)-0.016(0.080)-0.077(0.082)0.080(0.103)-1.703***(0.494)
0.165
490

0.003
(0.078)0.174*(0.097)0.089(0.118)0.042(0.046)0.016(0.090)-0.091(0.094)0.488**(0.205)-2.887***(0.631)0.153
379-0.022(0.099)0.224*(0.133)0.078(0.142)0.110(0.145)0.029(0.066)0.102(0.123)-0.220(0.138)0.578**(0.264)-2.436***(0.633)
0.145
201

0.061
(0.067)-0.058(0.136)0.024(0.132)0.217(0.332)-2.463***(0.774)0.156
178

1.403***(0.455)2.318***(0.457)1.478***(0.520)1.149***(0.420)1.002(0.739)1.178**(0.535)2.018**(0.791)1.353*(0.698)-0.040(0.049)-0.001(0.078)-0.117(0.088)0.060(0.044)-0.011(0.080)-0.004(0.082)0.184*(0.106)-1.305***(0.484)
0.144
491

0.099
(0.076)0.139(0.095)0.098(0.115)0.048(0.046)-0.074(0.089)-0.044(0.094)0.502**(0.200)-2.541***(0.618)
0.127
380

0.066
(0.093)0.135(0.126)0.174(0.149)0.170(0.148)0.028(0.063)-0.063(0.118)-0.196(0.134)0.511**(0.257)-1.520***(0.589)
0.094
203

0.084
(0.068)-0.048(0.137)0.131(0.137)0.365(0.348)-3.046***(0.793)0.171
177

New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

Appendix 2-4: Subsample Analysis (Establishment Size and Single- or Multi-Unit Firms) - Full Results in Wholesale Tradedatamanagement
dumhq
univm
unive

Innovation 1
Small
1.707***(0.269)1.010***(0.248)-0.098(0.079)0.045(0.028)-0.015(0.049)Large
1.320***(0.303)
1.546***(0.271)-0.030(0.079)0.007(0.031)
0.032(0.060)lemp
cmp
Q34_1score
Q34_2score
CIO
constant_term
Pseudo R2Ndatamanagement
dumhq
univm
unive

0.012
(0.025)-0.102**(0.043)-0.126***(0.046)0.224***(0.077)-1.178*(0.628)0.103
1553
Innovation 4
Small
2.061***(0.271)
1.552***(0.247)-0.140*(0.078)0.024(0.028)-0.054(0.050)0.063**(0.025)-0.092*(0.048)-0.140***(0.050)0.158**(0.068)-1.677***(0.578)
0.113
1478
Large
2.026***(0.307)1.537***(0.269)-0.159**(0.080)0.034(0.030)0.025(0.060)

lemp
cmp

0.023
(0.025)
Q34_1score-0.116***(0.043)
Q34_2score-0.108**(0.045)CIO
0.219***(0.076)constant_term-0.950(0.657)
0.126
Pseudo R2
N
1548(Notes) See Tables 11 (a) and (b).

0.020
(0.025)-0.007(0.048)-0.060(0.049)0.138**(0.068)-1.691***(0.617)0.120
1473

Branch
1.589***(0.339)1.207***(0.322)Hq
1.518***(0.250)1.248***(0.224)

Hq-sm
1.929***(0.336)
0.956***(0.298)Hq-lg
1.206***(0.393)
1.886***(0.348)0.019(0.034)0.038(0.070)-0.109(0.069)0.042(0.030)-0.135**(0.056)-0.143**(0.058)0.257***(0.074)4.886***(0.683)
0.110
1099

0.032
(0.026)-0.019(0.045)0.073(0.050)0.037*(0.022)-0.076*(0.039)-0.108***(0.041)0.136*(0.071)-2.239***(0.653)0.102
1937

0.063*(0.035)-0.007(0.059)-0.004(0.040)-0.048(0.076)0.025(0.031)-0.093*(0.053)-0.112**(0.056)0.206*(0.110)-1.539(1.129)
0.103
1004

0.045
(0.032)-0.052(0.061)-0.121*(0.064)0.076(0.097)-1.770**(0.689)0.125
917

Branch
1.696***(0.337)1.251***(0.322)Hq
2.198***(0.255)1.610***(0.223)Hq-sm
2.541***(0.346)1.390***(0.301)

Hq-lg
1.822***(0.393)2.110***(0.342)

0.056*(0.034)-0.031(0.070)0.169**(0.072)0.016(0.030)-0.090(0.056)-0.034(0.058)0.170**(0.073)3.126***(0.700)
0.107
1086

0.016
(0.026)-0.020(0.046)0.081(0.050)0.030(0.022)-0.068*(0.039)-0.101**(0.041)0.171**(0.071)-2.283***(0.673)
0.122
1932

0.004
(0.035)-0.011(0.060)0.037(0.031)-0.111**(0.053)-0.122**(0.056)
0.258**(0.110)-1.497**(0.715)0.134
1002

Innovation 2
Small
1.494***(0.274)
1.385***(0.253)-0.134(0.082)
0.053*(0.029)-0.071(0.050)Large
1.839***(0.315)1.285***(0.277)-0.030(0.082)0.023(0.031)
0.011(0.061)0.024(0.039)-0.038(0.075)0.034(0.026)-0.094**(0.045)-0.128***(0.047)0.228***(0.081)-0.402(0.809)0.110
1550
Innovation 5
Small
1.935***(0.273)1.653***(0.252)-0.131(0.080)0.015(0.028)-0.011(0.050)0.061**(0.025)-0.025(0.049)-0.190***(0.051)0.107(0.070)-1.712***(0.585)
0.107
1458
Large
1.560***(0.311)
1.781***(0.277)-0.135*(0.082)0.041(0.031)0.018(0.061)

0.016(0.032)-0.014(0.060)-0.092(0.063)0.116(0.098)-1.893***(0.703)
0.126
922

0.037
(0.025)-0.158***(0.044)-0.010(0.046)0.246***(0.080)-1.346**(0.657)
0.127
1560

0.015
(0.025)0.016(0.049)-0.060(0.050)0.220***(0.073)-1.577***(0.595)
0.124
1481

0.003
(0.042)-0.055(0.077)

Innovation 3
Small
1.739***(0.268)1.144***(0.245)-0.108(0.078)0.020(0.028)-0.011(0.049)Large
1.695***(0.310)1.729***(0.277)-0.086(0.081)
0.017(0.031)
0.015(0.061)

0.041(0.032)-0.062(0.054)-0.142**(0.057)0.206*(0.115)-1.523(1.158)0.107
1004

0.058*(0.033)-0.047(0.062)-0.207***(0.065)0.005(0.101)-1.821***(0.694)0.126
911

0.055**(0.025)-0.093**(0.043)-0.111**(0.045)0.192***(0.074)-1.403*(0.723)0.104
1551

0.059**(0.025)-0.011(0.049)-0.169***(0.050)0.221***(0.072)-2.072***(0.587)
0.129
1455

Hq
1.985***(0.258)1.794***(0.228)Hq-sm
2.337***(0.348)1.537***(0.305)

Hq-lg
1.598***(0.400)2.218***(0.351)0.017(0.026)-0.029(0.046)0.082(0.051)0.034(0.022)-0.082**(0.040)-0.072*(0.042)0.225***(0.074)-2.386***(0.678)
0.125
1933

0.015
(0.036)-0.005(0.061)0.018(0.040)-0.016(0.076)0.041(0.032)-0.150***(0.054)-0.064(0.057)0.339***(0.115)-2.056*(1.173)
0.133
1006

0.022
(0.032)0.006(0.061)-0.091(0.064)0.151(0.100)-2.013***(0.706)0.129
926

Branch1.235***(0.348)1.386***(0.331)Hq
1.915***(0.257)1.248***(0.228)

Hq-sm
1.904***(0.341)
1.162***(0.303)Hq-lg
2.122***(0.411)1.563***(0.353)0.046(0.035)-0.008(0.072)-0.097(0.071)
0.046(0.031)-0.086(0.058)-0.118*(0.060)0.236***(0.078)4.808***(0.799)0.105
1093

0.045*(0.027)-0.062(0.046)0.084(0.053)0.050**(0.023)-0.062(0.040)-0.162***(0.042)0.088(0.074)-2.283***(0.663)0.110
1930

0.079**(0.036)-0.072(0.061)

Branch1.289***(0.343)1.530***(0.332)0.065*(0.035)0.010(0.071)
0.056(0.075)0.006(0.031)-0.092(0.058)0.061(0.060)0.217***(0.079)4.009***(0.763)
0.111
1102

51

Branch1.120***(0.333)1.384***(0.319)Hq
2.073***(0.256)1.315***(0.225)Hq-sm
2.203***(0.342)0.987***(0.297)Hq-lg
2.000***(0.404)1.906***(0.352)0.009(0.034)0.034(0.070)-0.003(0.070)0.068**(0.030)-0.059(0.056)-0.105*(0.058)0.270***(0.075)4.036***(0.793)
0.106
1093

0.030
(0.026)-0.033(0.046)
0.092*(0.051)
0.056**(0.022)-0.068*(0.040)-0.144***(0.041)0.149**(0.072)-2.641***(0.657)
0.119
1927

0.050
(0.035)-0.038(0.060)0.006(0.040)-0.013(0.076)0.050(0.031)-0.095*(0.054)-0.164***(0.056)0.181*(0.108)-2.067**(0.899)0.119
1002

0.061*(0.032)-0.018(0.061)-0.131**(0.064)0.112(0.100)-2.516***(0.695)0.137
910

New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

Appendix 2-5: Subsample Analysis (Establishment Size and Single- or Multi-Unit Firms) - Full Results in Medical and Other Health Services
data
management
dumhq
univm
unive

Innovation 1
Small
1.670***(0.388)1.458***(0.361)-0.033(0.138)-0.000(0.038)0.066(0.063)Large
1.444***(0.427)1.986***(0.361)-0.239**(0.113)0.018(0.043)0.058(0.076)lemp
cmp
Q34_1score
Q34_2score
CIO
constant_term
Pseudo R2Ndatamanagement
dumhq
univm
unive

0.016
(0.041)-0.058(0.072)-0.161**(0.075)0.235(0.166)-1.407***(0.404)0.094
577
Innovation 4
Small
1.614***(0.382)
1.711***(0.363)0.146(0.138)-0.014(0.038)0.062(0.064)0.054(0.041)
0.102(0.070)-0.107(0.071)-0.007(0.140)-1.838***(0.399)
0.106
627
Large
1.840***(0.435)1.719***(0.362)-0.226**(0.114)-0.004(0.044)0.137*(0.078)

lemp
cmp

0.039
(0.041)
Q34_1score-0.086(0.072)
Q34_2score-0.172**(0.075)CIO
0.157(0.166)constant_term-1.372***(0.405)
0.102
Pseudo R2
N
575(Notes) See Tables 11 (a) and (b).

0.106**(0.042)0.023(0.070)-0.094(0.072)-0.032(0.142)-2.112***(0.408)0.116
621

Branch2.231***(0.595)1.760***(0.531)Hq
1.447***(0.330)1.731***(0.294)

Hq-sm
1.489***(0.426)1.440***(0.400)Hq-lg
1.244**(0.527)1.973***(0.438)0.057(0.059)-0.153(0.104)0.078(0.076)0.065(0.054)-0.160*(0.096)0.037(0.106)0.106(0.187)-1.909***(0.566)0.124
336-0.007(0.032)0.127**(0.056)-0.155***(0.054)0.024(0.035)0.092(0.059)-0.171***(0.059)0.089(0.132)-1.097***(0.349)
0.094
868

0.013
(0.041)0.078(0.070)-0.041(0.053)0.183**(0.092)-0.002(0.047)-0.013(0.082)-0.225***(0.083)0.125(0.186)-1.187***(0.419)
0.086
458

0.058
(0.052)0.207**(0.086)-0.128(0.085)0.033(0.189)-2.417***(0.473)0.107
410

Branch2.740***(0.611)1.658***(0.535)Hq
1.467***(0.328)1.719***(0.295)

Hq-sm
1.432***(0.419)1.653***(0.402)

Hq-lg
1.408***(0.531)1.746***(0.439)0.042(0.060)-0.145(0.105)0.105(0.077)0.032(0.055)-0.189*(0.098)0.084(0.106)-0.122(0.182)-1.894***(0.573)0.137
335-0.025(0.033)0.159***(0.057)-0.202***(0.054)0.088**(0.035)0.022(0.059)-0.180***(0.059)0.134(0.133)-0.967***(0.353)0.106
861-0.022(0.041)
0.096(0.071)

0.055(0.047)-0.045(0.082)-0.212**(0.084)0.066(0.187)-1.157***(0.422)
0.097
454

Innovation 2
Small
1.793***(0.384)1.620***(0.371)0.008(0.140)0.015(0.038)0.079(0.064)Large
1.400***(0.422)
1.614***(0.355)-0.222*(0.113)0.046(0.043)0.039(0.076)-0.033(0.053)0.242**(0.095)0.030(0.041)-0.052(0.073)-0.242***(0.076)0.307*(0.178)-1.315***(0.411)
0.118
574
Innovation 5
Small
2.052***(0.391)1.669***(0.374)0.331**(0.142)0.045(0.039)0.030(0.065)0.020(0.041)0.166**(0.070)-0.110(0.071)-0.149(0.139)-1.348***(0.394)
0.088
626
Large
1.698***(0.434)2.150***(0.367)-0.092(0.116)0.008(0.044)0.088(0.078)0.138***(0.053)0.096(0.086)-0.164*(0.085)0.199(0.192)-2.750***(0.489)
0.121
407

0.053
(0.042)0.029(0.074)-0.263***(0.077)0.216(0.175)-1.704***(0.417)
0.132
573

0.055
(0.042)0.124*(0.071)-0.075(0.072)-0.050(0.143)-1.991***(0.405)
0.118
627

0.015
(0.052)0.151*(0.091)

Innovation 3
Small
1.893***(0.395)1.681***(0.366)0.029(0.140)-0.017(0.038)0.066(0.065)Large
1.475***(0.423)
1.469***(0.354)-0.070(0.112)0.020(0.043)0.114(0.076)0.006(0.046)-0.027(0.082)-0.323***(0.084)0.248(0.199)-0.972**(0.423)
0.107
455

0.026
(0.051)0.233***(0.085)-0.118(0.084)0.016(0.187)-1.920***(0.464)
0.084
410

0.062
(0.042)-0.057(0.073)-0.125(0.076)0.175(0.169)-1.756***(0.419)
0.115
569

0.038
(0.041)0.101(0.070)-0.155**(0.071)-0.011(0.137)-1.790***(0.395)
0.083
624

Hq
1.695***(0.331)1.856***(0.299)Hq-sm
1.784***(0.426)
1.583***(0.411)Hq-lg
1.471***(0.530)2.169***(0.443)0.025(0.033)0.100*(0.057)-0.167***(0.055)0.058*(0.035)0.142**(0.060)-0.207***(0.060)0.120(0.138)-1.033***(0.357)
0.119
864

0.047
(0.042)0.020(0.072)-0.014(0.054)0.212**(0.094)0.051(0.047)0.105(0.083)-0.292***(0.085)0.175(0.197)-1.240***(0.429)
0.118
454

0.063
(0.053)0.187**(0.087)-0.122(0.087)0.047(0.197)-2.455***(0.484)0.131
410

Branch2.806***(0.615)2.015***(0.553)Hq
1.370***(0.325)1.492***(0.292)

Hq-sm
1.649***(0.424)
1.329***(0.407)

Hq-lg
0.829
(0.513)1.707***(0.427)0.089(0.062)-0.257**(0.109)0.061(0.079)0.066(0.056)-0.071(0.099)0.031(0.110)-0.225(0.185)-1.589***(0.579)
0.149
335

0.010
(0.032)0.145***(0.056)-0.175***(0.054)0.017(0.034)0.099*(0.059)-0.217***(0.059)0.140(0.134)-0.687**(0.347)
0.089
865

0.010
(0.041)0.120*(0.071)

Branch
2.743***(0.620)2.056***(0.547)0.039(0.061)-0.120(0.106)0.096(0.079)0.058(0.056)-0.107(0.099)0.008(0.108)-0.065(0.185)-2.124***(0.583)
0.157
336

52

Branch3.197***(0.637)2.021***(0.545)Hq
1.340***(0.330)1.517***(0.292)

Hq-sm
1.692***(0.432)
1.549***(0.402)

Hq-lg
0.779
(0.515)1.375***(0.428)0.080(0.061)-0.227**(0.107)0.005(0.078)0.103*(0.056)-0.243**(0.099)0.031(0.109)-0.114(0.183)-1.786***(0.591)0.168
334-0.022(0.032)0.169***(0.056)-0.140***(0.054)0.037(0.035)0.112*(0.059)-0.176***(0.059)0.130(0.131)-1.233***(0.351)0.088
859-0.011(0.042)0.113(0.071)-0.044(0.052)0.243***(0.092)0.020(0.047)0.028(0.083)-0.169**(0.084)0.046(0.188)-1.519***(0.432)
0.096
450

0.064
(0.052)0.201**(0.085)-0.200**(0.085)0.212(0.184)-2.147***(0.467)
0.085
409

New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

Appendix 2-6: Subsample Analysis (Degree of Decentralization in Decision-Making) - Full Results
Road Freight

Innovation1

Trandsport

Centralized

Decentralized

Centralized

Decentralized

Centralized

Decentralized

Centralized

Decentralized

Centralized

Decentralized

data

1.658***

2.091***

1.487**

1.385*

1.102*

1.604**

1.580***

1.629**

1.739***

0.910(0.634)(0.737)(0.606)(0.756)(0.614)(0.742)(0.610)(0.704)(0.617)(0.715)2.524***

2.145***

2.233***

3.441***

2.530***

2.373***

2.193***

1.526**

1.949***

2.630***(0.642)(0.656)(0.626)(0.715)(0.626)(0.679)(0.623)(0.637)(0.633)(0.689)management
univm
unive
lemp
cmp
Q34_1score
Q34_2score
CIO
constant_term

Innovation2

Innovation3

Innovation4

Innovation5-0.123**-0.114-0.024-0.136-0.063-0.100-0.073-0.053-0.022-0.044(0.063)(0.086)(0.061)(0.086)(0.061)(0.085)(0.061)(0.083)(0.063)(0.083)0.166*

0.093

0.071

-0.0780.146-0.181

0.050-0.111

0.038-0.110(0.098)(0.132)(0.096)(0.134)(0.099)(0.133)(0.096)(0.127)(0.100)(0.132)0.026-0.0430.083-0.1240.058-0.219-0.070-0.099-0.053-0.191(0.111)(0.144)(0.112)(0.154)(0.113)(0.149)(0.109)(0.141)(0.113)(0.148)0.111*

0.132*-0.0100.185**

0.099*

0.146**

0.109*

0.147**

0.037

0.128*(0.060)(0.072)(0.059)(0.075)(0.059)(0.072)(0.058)(0.069)(0.059)(0.071)

0.124-0.220*0.148-0.1740.143-0.0960.085-0.220**0.135-0.151(0.116)(0.114)(0.114)(0.118)(0.114)(0.115)(0.111)(0.111)(0.113)(0.116)-0.211*

0.055-0.199*0.080-0.1350.039-0.1470.048-0.1320.127(0.115)(0.123)(0.115)(0.128)(0.113)(0.123)(0.112)(0.118)(0.116)(0.124)0.321**

0.061

0.140

-0.0010.167

0.360*

0.205

0.204

0.166

0.270

(0.130)(0.165)(0.128)(0.179)(0.130)(0.184)(0.128)(0.165)(0.134)(0.183)-3.156***-2.252***-2.142***-1.595*-2.730***-1.021-1.993***-1.124-1.614**-0.979(0.689)(0.820)(0.648)(0.838)(0.672)(0.795)(0.627)(0.766)(0.639)(0.783)Pseudo R2

0.230

0.183

0.159

0.225

0.186

0.187

0.173

0.138

0.147

0.169

N

278

209

280

209

277

209

277

212

279

211

Wholesale

Innovation1

Trade

Centralized

Decentralized

Centralized

Decentralized

Centralized

Decentralized

Centralized

Decentralized

Centralized

Decentralized

data

1.348***

1.746***

1.167**

1.036*

0.850*

1.034**

1.092**

2.320***

1.350***

1.177**(0.488)(0.527)(0.499)(0.539)(0.481)(0.512)(0.487)(0.525)(0.516)(0.511)1.460***

1.252**

1.568***

1.634***

2.299***

0.874*

1.418***

1.249***

1.715***

1.481***(0.477)(0.490)(0.505)(0.507)(0.495)(0.482)(0.503)(0.484)(0.526)(0.492)

management
univm
unive
lemp
cmp
Q34_1score

Innovation2

Innovation3

Innovation4

Innovation5

0.003

0.049

0.053

0.063

-0.0130.060

0.104**

0.055

0.118**

0.066(0.050)(0.051)(0.051)(0.054)(0.051)(0.052)(0.050)(0.052)(0.053)(0.052)0.074-0.002-0.030-0.0470.152-0.1070.010-0.112-0.005-0.048(0.106)(0.105)(0.108)(0.111)(0.105)(0.107)(0.107)(0.107)(0.111)(0.105)-0.132-0.138-0.138-0.104-0.0620.045

0.188*

0.157

0.110

-0.046(0.093)(0.114)(0.096)(0.118)(0.095)(0.117)(0.104)(0.115)(0.111)(0.115)

0.067

0.029

0.097**

0.021

0.112**

0.032

0.027

0.005

-0.0180.041(0.043)(0.045)(0.045)(0.046)(0.044)(0.044)(0.046)(0.044)(0.048)(0.045)-0.104-0.175**-0.152*-0.025-0.100-0.027-0.051-0.148*-0.084-0.172**(0.082)(0.085)(0.086)(0.089)(0.083)(0.084)(0.085)(0.084)(0.090)(0.084)-0.134-0.136-0.073-0.167*-0.037-0.166*-0.014-0.058

0.114

0.012

(0.084)(0.088)(0.089)(0.092)(0.086)(0.088)(0.088)(0.087)(0.094)(0.087)0.217**

0.313**

0.149

0.395***

0.220**

0.430***

0.167*

0.213*

0.150

0.362***(0.097)(0.126)(0.102)(0.141)(0.098)(0.135)(0.101)(0.120)(0.109)(0.134)-0.9524.854***-0.8544.682***-1.672*4.492***-2.512**3.050-2.639**4.550***(0.865)(1.060)(0.876)(1.143)(0.876)(1.057)(1.082)(.)(1.102)(0.940)Pseudo R2

0.110

0.156

0.104

0.151

0.126

0.131

0.113

0.155

0.140

0.126

N

538

524

519

510

525

511

519

500

515

512

Medical andInnovation1

Q34_2score
CIO
constant_term

Other HealthServicesdatamanagement
univm
unive
lemp
cmp
Q34_1score
Q34_2score
CIO
constant_term

Centralized

Innovation2
Decentralized

Centralized

Innovation3
Decentralized

Centralized

Innovation4
Decentralized

Centralized

Innovation5
Decentralized

Centralized

Decentralized

2.773***

1.600*

2.820***

2.908***

3.297***

3.078***

2.332***

3.214***

2.946***

2.682***(0.839)(0.910)(0.826)(0.974)(0.891)(0.962)(0.802)(0.978)(0.850)(0.965)2.037***

1.663**

2.495***

1.669**

2.629***

1.516*

2.196***

1.371*

2.036***

2.288***(0.756)(0.781)(0.791)(0.812)(0.794)(0.784)(0.744)(0.811)(0.760)(0.836)0.075-0.0340.152*-0.047

0.103

0.001

0.066

-0.0280.121-0.120(0.081)(0.094)(0.085)(0.097)(0.085)(0.094)(0.082)(0.097)(0.084)(0.098)-0.094-0.145-0.280*-0.144-0.224-0.160-0.197-0.019-0.176-0.005(0.139)(0.172)(0.146)(0.178)(0.145)(0.171)(0.140)(0.175)(0.142)(0.176)

0.069

0.076

0.018

0.081

-0.057

0.092

0.099

0.066

0.183

-0.007(0.120)(0.122)(0.123)(0.129)(0.123)(0.121)(0.118)(0.126)(0.123)(0.129)

0.055

0.050

0.059

0.040

0.146*

0.034

0.004

0.057

0.068

-0.004(0.076)(0.083)(0.078)(0.086)(0.081)(0.083)(0.076)(0.086)(0.078)(0.088)-0.040-0.235-0.052-0.090-0.200-0.323**-0.086-0.256*-0.014-0.139(0.138)(0.148)(0.140)(0.153)(0.146)(0.150)(0.137)(0.154)(0.139)(0.157)

0.039

0.077

0.045

0.057

0.035

0.072

0.035

0.126

-0.1140.164(0.148)(0.170)(0.153)(0.176)(0.157)(0.170)(0.145)(0.172)(0.149)(0.175)

0.130

0.258

-0.170-0.189-0.1230.026-0.123-0.076-0.008-0.036(0.292)(0.294)(0.292)(0.272)(0.292)(0.264)(0.286)(0.268)(0.311)(0.269)-2.873***-1.166-1.961**-1.331-2.235***-1.700*-1.931**-1.912**-2.842***-1.429(0.845)(0.887)(0.824)(0.926)(0.865)(0.919)(0.805)(0.932)(0.867)(0.921)

Pseudo R2

0.164

0.093

0.186

0.124

0.214

0.143

0.140

0.145

0.198

0.143

N

173

156

173

155

172

155

172

154

173

156

(Notes) See Table 12.

53

New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

Appendix 3: Data Activities and Innovation Performance by the Ordered Probit Model with the interaction term between data and management Coefficients in All Independent Variables
Road Freight Transport

Wholesale Trade

Medical and Other Healthcare Services

Innovation

Innovation

Innovation

Innovation

Innovation

Innovation

Innovation

Innovation

Innovation

Innovation

Innovation

Innovation

Innovation

Innovation

Innovation

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

0.876

0.978

1.215

0.687

0.413

2.510***

2.303***

2.179***

1.705***

2.214***

1.261**

1.958***

1.558***

1.689***

2.078***(0.801)(0.711)(0.751)(0.741)(0.682)(0.476)(0.464)(0.48)(0.478)(0.477)(0.551)(0.533)(0.566)(0.554)(0.542)

Coefficients
data
management
data*manageme
nt
dumhq
univm
unive
lemp
cmp
Q34_1score
Q34_2score
CIO

0.715

0.87

1.252*

0.407

0.107

2.205***

1.939***

1.658***

1.076**

2.030***

0.982

1.352**

1.102*

1.312**

1.584**(0.764)(0.686)(0.719)(0.706)(0.657)(0.492)(0.479)(0.495)(0.495)(0.493)(0.627)(0.611)(0.642)(0.636)(0.619)

2.244

1.611

1.022

2.582*

3.036**-2.022**-1.457*-0.9520.435-0.8970.948-0.196

0.666

0.25

-0.066(1.21)(1.458)(1.325)(1.382)(1.371)(1.287)(0.877)(0.858)(0.884)(0.879)(0.879)(1.218)(1.188)(1.243)(1.232)0.001-0.043-0.076-0.107-0.140*-0.100**-0.122***-0.114**-0.181***-0.106**-0.162**-0.081-0.055-0.0820.027(0.09)(0.085)(0.086)(0.087)(0.084)(0.044)(0.044)(0.044)(0.045)(0.044)(0.076)(0.075)(0.077)(0.076)(0.075)-0.065*-0.038-0.043-0.042

0.016

0.026

0.047***

0.026-0.0060.01-0.0080.015-0.007-0.027-0.01(0.037)(0.034)(0.035)(0.035)(0.034)(0.017)(0.016)(0.017)(0.017)(0.016)(0.025)(0.024)(0.025)(0.025)(0.024)0.100*

0.097*

0.08

0.068

0.059

-0.004-0.028-0.0010-0.008

0.067

0.067

0.073*

0.088**

0.07(0.053)(0.05)(0.05)(0.051)(0.049)(0.032)(0.031)(0.031)(0.032)(0.031)(0.044)(0.042)(0.044)(0.044)(0.043)

0.004

0.026

0.009

-0.026-0.0060.039

0.054*

0.075**

0.089***

0.067**-0.066*-0.103***-0.093**-0.108***-0.108***(0.059)(0.056)(0.057)(0.058)(0.057)(0.031)(0.031)(0.031)(0.031)(0.031)(0.038)(0.037)(0.038)(0.038)(0.037)0.063**

0.055**

0.080***

0.099***

0.081***

0.038***

0.039***

0.055***

0.041***

0.024*

0.044*

0.051**

0.055**

0.079***

0.060**(0.029)(0.027)(0.028)(0.028)(0.027)(0.014)(0.014)(0.014)(0.014)(0.014)(0.026)(0.025)(0.026)(0.026)(0.025)-0.039-0.0080.015-0.0020.03-0.081***-0.081***-0.074***-0.068***-0.054**

0.016

0.062

0.044

0.024

0.116***(0.052)(0.049)(0.05)(0.051)(0.049)(0.026)(0.026)(0.026)(0.026)(0.026)(0.044)(0.043)(0.045)(0.044)(0.043)-0.078-0.039-0.055-0.0370.006-0.094***-0.146***-0.123***-0.104***-0.050*-0.148***-0.159***-0.127***-0.081*-0.142***(0.054)(0.051)(0.052)(0.052)(0.051)(0.027)(0.027)(0.027)(0.027)(0.027)(0.046)(0.044)(0.046)(0.045)(0.044)0.187***

0.113

0.160**

0.151**

0.140**

0.170***

0.186***

0.195***

0.155***

0.155***

0.076

0.083

0.117

0.102

0.132

(0.07)(0.069)(0.069)(0.069)(0.068)(0.038)(0.038)(0.038)(0.038)(0.038)(0.091)(0.089)(0.091)(0.09)(0.089)1.872***

1.626***

1.898***

1.584***

1.365***

2.236***

2.284***

2.372***

1.407***

1.884***

0.928***

1.003***

1.239***

1.249***

1.336***(0.495)(0.446)(0.465)(0.463)(0.433)(0.528)(0.529)(0.531)(0.525)(0.5)(0.357)(0.349)(0.368)(0.362)(0.351)2.807***

2.534***

2.764***

2.458***

2.271***

3.174***

3.183***

3.228***

2.568***

2.903***

1.992***

1.910***

2.098***

2.237***

2.214***(0.498)(0.448)(0.467)(0.466)(0.435)(0.529)(0.53)(0.531)(0.526)(0.501)(0.359)(0.35)(0.369)(0.364)(0.353)3.613***

3.314***

3.561***

3.220***

3.002***

3.692***

3.832***

3.885***

3.238***

3.622***

2.723***

2.731***

2.944***

3.041***

2.995***(0.501)(0.451)(0.47)(0.469)(0.438)(0.53)(0.531)(0.532)(0.526)(0.502)(0.363)(0.354)(0.373)(0.368)(0.355)

Cut
cut1
cut2
cut3
Pseudo R2

0.128

0.101

0.109

0.116

0.109

0.062

0.071

0.069

0.075

0.07

0.07

0.066

0.07

0.069

0.077

N

866

869

860

870

871

3047

3036

3027

3037

3043

1204

1200

1193

1196

1200

(Notes)data*management denotes the interaction term between data score 1 and management score.

54

New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

Appendix 4 The 2018 JP-MOPS Survey Sheet
内 閣 府

Management and Organizational Practices Survey
Survey Questionnaire(Road Freight Transport/Wholesale Trade/Medical and Other Health Services)

This survey questionnaire will be treated with absolute confidentiality.
Moreover, responses will be used for statistical purposes only.

1.

The reporting unit for this survey is an establishment. The aim of the survey is to understand theorganizational and management practices at establishments and to examine the links with productivity growth.
We estimate that this survey form will take an average of 30 minutes to complete.

2.

Once you have completed the survey form, please put it in the enclosed envelope and post it no later thanNovember 30, 2018.

3.

A summary of the results of this survey will be sent to your establishment.

○ About your establishment
Corporate
number ※１

Survey respondent

Establishment
name※２Affiliation/Position

IndustryYour name

Telephone
Address

EmailYears of serviceyears※ 1. If you know the 13-digit corporate number of the corporation to which your establishment belongs, please enter it here.
For details about the corporate number, please refer to “Notification of Corporate Number” and further information on thewebsite of the National Tax Agency (National Tax Agency Corporate Number Publication Site).
2. If your establishment belongs to a corporation, please enter the name of the corporation. If your establishment is a branch of acorporation, please enter the names of both the corporation and the establishment (such as the branch/store name).

55

New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

Section A: Management Practices
Question 1. In 2013 and 2018, what best describes what happened at your establishment when a problem in the
production process arose?
Examples: Finding a quality defect in service, problems with buying and stocking items, problems with serving
customers or with equipment, issues with the transportation of items※ Mark one box for each year
We fixed it but did not take further action
We fixed it and took action to make sure that it did not happen again
We fixed it and took action to make sure that it did not happen again,
and had a continuous improvement process to anticipate problems likethese in advanceNo action was taken

2013

2018

□
□

□
□

□

□

□

□

Question 2. In 2013 and 2018, how many key performance indicators (KPIs) were monitored at your establishment?
＊

KPIs are not the sales targets but indicators that show whether firm activities to achieve ultimate targets areproceeding smoothly, e.g., turnover, cost, waste, service quality, inventory, energy, on-time delivery, andcustomer satisfaction.

In the survey sheet in medical and other health services, KPIs are explained as follows:
KPIs are not the sales targets but indicators that show whether firm activities to achieve ultimate targets areproceeding smoothly, e.g., financial condition, number of patients, medical care quality such as procedure andoutcomes (e.g., fatality rate), hospital days, patient satisfaction.

※Mark one box for each year
1-2 key performance indicators
3-9 key performance indicators
10 or more key performance indicators
No key performance indicators (if no key performance indicators inboth years, SKIP to Question 5)

2013

2018

□
□
□

□
□
□

□

□

Proceed to
Question 3
Proceed to
Question 5

Question 3. During 2013 and 2018, how frequently were the key performance indicators reviewed by (a) managers*1and (b) non-managers*2 at your establishment?
Note: If in Question 2 your response is “No key performance indicators” either for 2013 or 2018, you do notneed to respond for that year.

(a) Managers＊１

A manager is someone who has employees directly reporting to them, with whom they meet on a regular basis, and whose
pay and promotion they may be involved with, e.g., Marketing Manager, Human Resource Manager, Sales Manager, FinanceManager.

※ Mark all that apply
YearlyQuarterlyMonthly
WeeklyDaily
Hourly or more frequently
Never

56

2013

2018

□
□
□
□
□
□
□

□
□
□
□
□
□
□

New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries(b) Non-managers＊2

Non-managers are all employees at the establishment who are not managers as defined in Question 3(a).

※ Mark all that apply
YearlyQuarterlyMonthly
WeeklyDaily
Hourly or more frequently
Never

2013

2018

□
□
□
□
□
□
□

□
□
□
□
□
□
□

Question 4. During 2013 and 2018, where were the display boards showing KPIs located at your establishment?
Note: If in Question 2 your response is “No key performance indicators” either for 2013 or 2018, you do notneed to respond for that year here.

※Mark one box for each year
All display boards were located in one place (e.g., at the sales place,the backyard, the office)Display boards were located in multiple places
We did not have any display boards

2013

2018

□

□

□
□

□
□

Question 5. In 2013 and 2018, what best describes the time frame of sales targets at your establishment?
In the survey sheet in medical and other health services, the term “targets (such as earnings, revenues, and costs)”is used instead of the term “sales of targets.”※ Mark one box for each year
Main focus was on short-term (less than one year) sales targets
Main focus was on long-term(more than one year) sales targets
Combination of short-term and long-term sales targets
No sales targets (If no sales targets in both years, SKIP to Question
10)

2013

2018

□
□
□

□
□
□

□

□

Proceed to
Question 6
Proceed to
Question 10

Question 6. How much effort was required (or is expected to be required) for your establishment to meet its sales
targets? Please choose the most appropriate answer below.
Note: If in Question 5 your response is “No production targets” either for 2013 or 2018, you do not need torespond for that year here.

※Mark one box for each year
Possible to achieve without much effort
Possible to achieve with some effort
Possible to achieve with normal amount of effort
Possible to achieve with more than normal effort
Only possible to achieve with extraordinary effort

57

2013

2018

□
□
□
□
□

□
□
□
□
□

New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries

Question 7. At your establishment, who was aware of the sales targets? Please choose the most appropriate answer
below.
Note: If in Question 5 your response is “No sales targets” either for 2013 or 2018, you do not need to respondfor that year here.

※ Mark one box for each year
Only senior managers (e.g., general managers, directors)
Most managers and some non-managers
Most managers and most non-managers
All managers and most non-managers

2013

2018

□
□
□
□

□
□
□
□

Question 8. In 2013 and 2018, what were (a) non-managers' and (b) managers’ performance bonuses usually basedon at your establishment?
Note: If in Question 5 your response is “No sales targets” either for 2013 or 2018, you do not need to respondfor that year here.

(a) Non-managers※ Mark all that apply
Their own performance as measured by sales targets
Their team or group performance as measured by sales targets
Their establishment's performance as measured by sales targets
Their company's performance as measured by sales targets
No performance bonuses

2013

2018

□
□
□
□
□

□
□
□
□
□

(b) Managers※ Mark all that apply
Their own performance as measured by sales targets
Their team or group performance as measured by sales targets
Their establishment's performance as measured by sales targets
Their company's performance as measured by sales targets
No performance bonuses (If no performance bonuses in both years,
SKIP to Question 10)

58

2013

2018

□
□
□
□

□
□
□
□

□

□

Proceed to
Question 9
Proceed to
Question 10

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The Impact of Data Activities on Innovation Performance in Service Industries

Question 9. In 2013 and 2018, when sales targets were achieved, what percentage of employees received (or is
expected to receive) a bonus based on the degree to which sales targets were achieved at your establishment?
Please choose the most appropriate answer for (a) non-managers and (b) managers.
Note: If in Question 5 your response is “No sales targets” either for 2013 or 2018, you do not need to respondfor that year here.

(a) Non-managers※ Mark one box for each year
0%
1-33%
34-66%
67-99%
100%
Sales targets not met

2013

2018

□
□
□
□
□
□

□
□
□
□
□
□

(b) Managers※ Mark one box for each year
0%
1-33%
34-66%
67-99%
100%
Sales targets not met

2013

2018

□
□
□
□
□
□

□
□
□
□
□
□

Question 10. In 2013 and 2018, what were (a) non-managers’ and (b) managers’ performance bonuses usually basedon at your establishment? At your establishment, when deciding whether to promote an employee, what is thedecision mainly based on? Please choose the most appropriate answer for (a) non-managers and (b) managers.
(a) Non-managers※Mark one box for each year
Promotions were based solely on performance and ability
Promotions were based partly on performance and ability, and partly onother factors (e.g., tenure or family connections)
Promotions were based mainly on factors other than performance andability (e.g., tenure or family connections)Non-managers are normally not promoted

2013

2018

□

□

□

□

□

□

□

□

(b) Managers※Mark one box for each year
Promotions were based solely on performance and ability
Promotions were based partly on performance and ability, and partly onother factors (e.g., tenure or family connections)
Promotions were based mainly on factors other than performance andability (e.g., tenure or family connections)Managers are normally not promoted

59

2013

2018

□

□

□

□

□

□

□

□

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The Impact of Data Activities on Innovation Performance in Service Industries

Question 11. At your establishment, if an employee is found to be doing a poor job, how long does it take for them
to be transferred to another position or be dismissed? Please choose the most appropriate answer for (a)non-managers and (b) managers.
(a) Non-managers※ Mark one box for each year
Less than six months
More than six months
There were few or no cases in which employees were transferred or
dismissed.

2013

2018

□
□

□
□

□

□

(b) Managers※Mark one box for each year
Less than six months
More than six months
There were few or no cases in which employees were transferred or
dismissed.

2013

2018

□
□

□
□

□

□

Section B: Organization
Question 12. Was the headquarters for this company at the same location as your establishment? Mark one box foreach year.
※Mark one box for each year
2013

2018

Yes (If yes in both years, SKIP to Question 19)

□

□

No

□

□

If no, what prefecture (if in Japan) or country (if abroad)?
2013:

2018:

Question 13. In 2013 and 2018, where were decisions on hiring permanent full-time employees made for yourestablishment? Mark one box for each year.

※Mark one box for each year
2013

2018

Only at your establishment□□

Only at headquarters□□

Both at your establishment and at headquarters

□

□

Other

□

□

(If other, please specify.)
2013:

2018:

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Question 14. In 2013 and 2018, where were decisions to give an employee a pay increase of at least 10% made foryour establishment? Mark one box for each year.

※ Mark one box for each year
2013

2018

Only at your establishment□□

Only at headquarters□□

Both at your establishment and at headquarters

□

□

Other

□

□

(If other, please specify.)
2013:

2018:

Question 15. In 2013 and 2018, where were decisions on new product/service introductions made for yourestablishment? Mark one box for each year.
※ Mark one box for each year
2013

2018

Only at your establishment□□

Only at headquarters□□

Both at your establishment and at headquarters

□

□

Other

□

□

(If other, please specify.)
2013:

2018:

Question 16. In 2013 and 2018, where were product/service pricing decisions made for your establishment? Markone box for each year.
※Mark one box for each year
2013

2018

Only at your establishment□□

Only at headquarters□□

Both at your establishment and at headquarters

□

□

Other

□

□

(If other, please specify.)
2013:

2018:

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Question 17. In 2013 and 2018, where were advertising decisions for products/services made for yourestablishment? Mark one box for each year.
※ Mark one box for each year
2013

2018

Only at your establishment□□

Only at headquarters□□

Both at your establishment and at headquarters

□

□

Other

□

□

(If other, please specify.)
2013:

2018:

Question 18. In 2013 and 2018, what was the yen amount that could be used to purchase a fixed/capital asset foryour establishment without prior authorization from headquarters? Mark one box for each year.
※Mark one box for each year
2013

2018

Under 100,000 yen□□

100,000 to 1 million yen□□

1 million to 10 million yen□□

10 million to 100 million yen□□

100 million yen or more□□

Section C: Data and Decision-Making●“Data” here refers to numerical values, characters, images, sounds, etc., represented in a formalized mannersuitable for communication, interpretation, or processing.

●“Decision-making” here includes all decisions necessary for establishment activities, ranging from strategic
decisions by management for the whole establishment to on-site decisions for the day-to-day activities of theestablishment.

Question 19-1. In 2013 and 2018, what best describes the availability of data to support decision making at yourestablishment?
※ Mark one box for each year
2013

2018

Data to support decision making are not available
A small amount of data to support decision making isavailable
A moderate amount of data to support decision making isavailable
A great deal of data to support decision making is available

□

□

□

□

□

□

□

□

All the data we need to support decision making is available

□

□

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Question 19-2. Following up on Question 19-1 regarding data to support decision-making: In 2018, how much isthe data collected as “systematically organized electronic data”＊? Mark one box for the data with thelongest accumulation period.
＊“Systematically organized electronic data” here refers to electronic data that is systematically collected andorganized to a certain extent, including information on individual customers and transaction data on
individual products or services (for example, sales to individual customers, data on sales and inventories ofindividual products, etc.). It does not refer to accounting data such as sales and profits aggregated at theestablishment level.

2018
Not being collected at all□

Has been collected for the past year□

Has been collected for the past 2 years□

Has been collected for the past 3 to 5 years□

Has been collected for the past 6 to 10 years□

Has been collected for more than 10 years□

Question 20. In 2013 and 2018, what best describes the use of data to support decision making at yourestablishment?
※ Mark one box for each year
2013

2018

Decision making does not use data□□

Decision making relies slightly on data□□

Decision making relies moderately on data□□

Decision making relies heavily on data□□

Decision making relies entirely on data□□

Question 21. Consider each of the following sources of data and rate how frequently each source is used in decision
making at your establishment in 2018 and was used in 2013.

(a) At present in 2018
Daily

WeeklyMonthly

YearlyNever

Basic Performance indicators (e.g., sales,
costs, inventories, etc.)

□

□

□

□

□

Formal or informal feedback from managers

□

□

□

□

□

Formal or informal feedback from nonmanagers

□

□

□

□

□

Data from outside the firm (suppliers,
customers, outside data providers)

□

□

□

□

□

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Daily

WeeklyMonthly

YearlyNever

Basic Performance indicators (e.g., sales,
costs, inventories, etc.)

□

□

□

□

□

Formal or informal feedback from managers

□

□

□

□

□

Formal or informal feedback from nonmanagers

□

□

□

□

□

Data from outside the firm (suppliers,
customers, outside data providers)

□

□

□

□

□

Question 22-1. How frequently is/was each of these activities influenced by data analysis at your establishment?
Mark one box for each year.

(a) At present in 2018
Never

Slightly

Moderately

Generally

Entirely

Demand forecasting

□

□

□

□

□

Investment decisions (e.g., regarding
capital equipment, new branches)

□

□

□

□

□

Design of new products or services

□

□

□

□

□

Advertising

□

□

□

□

□

Purchasing, shipping, inventory control,
distribution

□

□

□

□

□

Back office tasks (e.g., human resources,
accounting)

□

□

□

□

□

Never

Slightly

Moderately

Generally

Entirely

Demand forecasting

□

□

□

□

□

Investment decisions (e.g., regarding
capital equipment, new branches)

□

□

□

□

□

Design of new products or services

□

□

□

□

□

Advertising

□

□

□

□

□

Purchasing, shipping, inventory control,
distribution

□

□

□

□

□

Back office tasks (e.g., human resources,
accounting)

□

□

□

□

□

(b) In 201364

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Question 22-2. Following up on Question 22-1, what issues does your establishment currently face when using data
to support decision-making? Mark all that apply below.

2018
Not collecting useful data□

Not cost-effective□

Insufficient human resources to use data□

Legal uncertainties regarding the use of data (regarding personal data protection,
intellectual property rights, etc.)□

No issues in using data□

Do not need to use data

□

Other

□

(If other, please specify.)

Question 23-1. How frequently does your establishment typically rely on predictive analytics (statistical models
that provide forecasts in areas such as demand, production, or human resources)?
＊“Predictive analytics” refer to analytical methods that employ various kinds of data, including big data, as
well as statistical models to specify the likelihood of future outcomes based on historical data with regard tobusiness activities such as demand, sales, human resource management, etc.

※ Mark one box for each year
2013

2018

Daily

□

□

Weekly□□Monthly□□

Yearly

□

□

Never

□

□

Question 23-2. Following up on Question 23-1, is artificial intelligence (AI; for details, see Section D on thenext page) used for predictive analytics? Please choose the most appropriate answer below.
※ Mark one box for each year
2013

2018

Used

□

□

Not used□□65

New ESRI Working Paper No.53
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Section D: Artificial Intelligence (AI)● Artificial intelligence (AI) refers to technology that achieves intellectual work performed by the humanbrain using a computer. Specific examples of applications include image and speech recognition,interactive processing through the understanding of natural language, demand forecasting, machinelearning, inference making, and optimization.
＊

Examples of the use of AI (with examples of fields in parentheses)
The examples differ in business categories.
Road freight transport
・Proper inventory control through demand forecasting (corporate planning; products/services planning; research anddevelopment; purchasing, shipping, inventory control, distribution)
・Testing delivery route through inference and optimization (corporate planning; products/services planning; research anddevelopment; purchasing, shipping, inventory control, distribution)
・Shift management of staff at an establishment (human resources, workforce management, accounting)
Wholesale trade
・Purchasing and sales of goods through demand forecasting (corporate planning; products/services planning; research anddevelopment; purchasing, shipping, inventory control, distribution; advertising)・Responding to questions using chatbots (customer services such as sales, inquiries, after-sales services)
・Shift management of staff at an establishment (human resources, workforce management, accounting)
Medical and other health services
・Diagnosis support system through similar cases (corporate planning; products/services planning; research anddevelopment)
・Calculating patient risk through predictive model (corporate planning; products/services planning; research anddevelopment)
・Shift management of nurse at an establishment (human resources, workforce management, accounting)

Question 24. What best describes the use of AI in 2018? Mark one box for each of these activities.
Note: The use of AI includes not only the use of AI technology (and software incorporating AI technology) itself
but also that of machinery (hardware) such as automated robots operated by AI. It also includes the useof AI technology at the stage of demonstration experiments.
Proceed to Question 25-1

Corporate planning; products/services planning;
research and development
Purchasing, shipping, inventory control,
distribution
Advertising
Customer services (sales, inquiries, after-sales
services, etc.)
Human resources, workforce management,
accounting
Other

Proceed to Question 26 if all
your answers fall into thesecategories.

In useIn
considerationof useNo planto use

Does notapply

Do not
know

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

(If other, please specify.)

This question is for those who replied “In use” or “In consideration of use” in at least one of the rows in
Question 24 above.

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Question 25-1. How much effect is the use of AI expected to have? Mark one box for each of these activities.
Note: For fields where the response is “No plan to use” or “Does not apply” in Question 24, mark “No plan touse/Does not apply.”

Corporate planning; products/services planning;
research and development
Purchasing, shipping, inventory control,
distribution
Advertising
Customer services (sales, inquiries, after-sales
services, etc.)
Human resources, workforce management,
accounting
Other

Large

Some

LittleNo

No plan touse/Does not
apply

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

Question 25-2. It is assumed that as technology advances AI will make a lot of things possible. Under thisassumption, what is the role of AI expected to play at your establishment? Mark all that apply.
Create businesses with new values
Increase the value (in terms of quality, customer satisfaction, etc.) of, or
quantitatively expand the existing business
Increase efficiency and productivity of the existing business
Make up for labor shortages (due to tightness of the labor market, reduction of
overtime, shortage of skilled workers, etc.)
Promote business cooperation or rearrange business relationship with suppliers andclients
Do not know

□

Other

□

□
□
□
□
□

(If other, please specify.)

Question 25-3. In order for AI to play the role indicated in Question 25-2, what initiatives are your establishment
taking or planning to take in the future in terms of developing, securing, and allocating personnel. Pleasechoose all answers that apply.
Educate and train employees within the establishment□

Use external personnel familiar with AI (temporary workers, etc.)□

Recruit new graduates or mid-career personnel familiar with AI□

Reassign personnel (that can be replaced by AI) within the establishment□

Do not know

□

Other

□

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This question is for those who replied “No plan to use,” “Does not apply,” or “Do not know” in all the rows
in Question 24 above.
Question 26. What are the main reasons that your establishment is not considering or planning to use AI? Pleasechoose all answers that apply.

AI that could be used in the establishment’s activities does not currently exist□

Difficulties exist in judging the usefulness of AI due to a lack of knowledge or the high degreeof uncertainty with regard to AI□

Not cost-effective□

Low priority compared to other investment expenditure□

Insufficient personnel to handle AI□

Data necessary for the use of AI has not been collected□

Not used by others in the same industry□

Do not know

□

Other

□

(If other, please provide more details in the box below.)

Section E: Your establishment
Question 27. What was the number of regular employees＊ of your establishment as of June 1, 2018?
＊“Regular employees” refer to those employed indefinitely or for a month or more, including full-time andpart-time employees, etc. However, individual owner-managers, unpaid family employees, anddispatched workers should not be included.

Number of regular employees
As of June 1, 2018

Question 28. At your establishment, roughly what percentage of employees had a university degree? Please choose
the most appropriate answer for (a) managers and (b) non-managers.
(a) Managers※ Mark one box for each year
Less than 20%
20% or more, but less than 40%
40% or more, but less than 60%
60% or more, but less than 80%
80% or more

68

2013

2018

□
□
□
□
□

□
□
□
□
□

New ESRI Working Paper No.53
The Impact of Data Activities on Innovation Performance in Service Industries(b) Non-managers※ Mark one box for each year
0%
Less than 10%
10% or more, but less than 20%
20% or more

2013

2018

□
□
□
□

□
□
□
□

Question 29. At present in 2018, does the firm to which your establishment belongs have a CIO＊? Please choose
the most appropriate answer (one answer only).
＊

The term “chief information officer” (CIO) refers to a person that has ultimate responsibility for all
aspects related to information technology (IT) within the firm, ranging from the introduction to the use
and application of IT. Further, the CIO is responsible for devising and implementing an information
strategy that is in line with the management philosophy of the firm.

2018
Has a full-time CIO
Has a CIO who concurrently holds other positions
Does not have a CIO
Do not know

□
□
□
□

Question 30. Has the firm to which your establishment belongs used or applied for national or local government
subsidies for the introduction of IT between January 2018 and today? Please choose all answers that apply.
Between January 2018 and
today
□
□
□
□

Already received
Applying/Planning to apply
Not planning to apply
Do not know

Question 31. At present, in 2018, about how many establishments are directly competing with your establishment
to win customers, etc. Please choose the most appropriate answer (one answer only).
2018
0
1–2 establishments
3–5 establishments
6–10 establishments
More than 10 establishments

□
□
□
□
□

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Question 32. At your establishment, how does it train and use specialized personnel in order to resolve issues
straddling various specialized tasks＊? Please choose the most appropriate answer.

＊“Specialized tasks” refer to individual tasks that have been set up in the different fields within theestablishment such as corporate planning, distribution, advertising, customer services, etc.
※ Mark one box for each year
2013

2018

□
□

□
□

□

□

Train specialized personnel in each field and use them in teams
Train and use personnel capable of dealing with various fields
Train specialized personnel in each field and, in addition, train and use
specialized personnel responsible for communication between fields

Question 33. Has your establishment in the past five years (2013 to 2018) made any of the innovations listed
below? Please choose one answer for each row.
● Explanation of the “innovation” terminology below
1.

“New products/services” refer to products or services with characteristics and uses that differ substantially from thoseoffered/sold so far.

2.

“Improvement of existing products/services” refer to substantially strengthening or improving functions of existingproducts/services (within or outside your firm).

3.

“New combination of existing products/services” refer to producing new products/services by combining existingproducts/services without altering the performance or quality of those existing products/services.

4.

“Introduction of new processes” refer to the adoption of new methods in processes ranging from the development to theprovision of products/services.

5.

“Process improvements” refer to substantial functional strengthening or improvement of processes.

Development and introduction of
new products/services
Improvement of existingproducts/services
New combination of existingproducts/services
Introduction of new processes
Process improvements

No innovations

Once every few
years

Once a year

More than once a
year

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

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Question 34. In your own personal view, how can your establishment be described compared to otherestablishments in terms of the following aspects?
■ Specialization or coordination
Which of the following do you feel is more important for the performance of your establishment?
Referring to the example below, indicate the degree to which aspects are important by placing a mark on
the line as indicated.
・“Specialization in individual tasks” refers to a high degree of division of labor in tasks involving specialized knowledge,etc.
・“Coordination across individual tasks” refers to smooth communication across departments for the consistent
implementation of tasks, etc.

ExampleSpecialization is more important

Coordination is more important
Equally important■ Creativity or efficiency
Which do you feel is more important in your establishment, employees’ creativity or the efficiency of
performing tasks?
・“Creativity” refers to employees’ voluntary initiatives and ingenuity, etc.
・“Efficiency” refers to raising the efficiency of performing tasks through the standardization of tasks, etc.

Creativity is more important

Efficiency is more important
Equally importantThank you very much for your cooperation.
Please return the completed survey form no later than November 30, 2018.

71