Advances in Social Sciences Research Journal – Vol.4, No.5
Publication Date: Mar. 25, 2017
DoI:10.14738/assrj.45.2850.
Zhou, L., Borah, P. S., Chen, Y., & Nisar, N. (2017). Determinants of firm Performance In Knowledge-Intensive Industries: A
Comparative Study of Two Emerging Economies, China and Taiwan. Advances in Social Sciences Research Journal, 4(5) 53-71
Determinants of firm Performance In Knowledge-Intensive
Industries: A Comparative Study of Two Emerging Economies,
China and Taiwan
Lulin Zhou
School of Management, Jiangsu University
Zhenjiang, Jiangsu Province, P.R. China
Prasad Siba Borah
School of Management, Jiangsu University
Zhenjiang, Jiangsu Province, P.R. China
Yinghua Chen
School of Management, Jiangsu University
Zhenjiang, Jiangsu Province, P.R. China
Numair Nisar
School of Management, Jiangsu University
Zhenjiang, Jiangsu Province, P.R. China
ABSTRACT
In light of predictions and statistics on the increasing importance of knowledge-intensive
industries in China and Taiwan, this research utilizes these two emerging economies’
business context to examine whether a firm’s performance in knowledge-intensive
industries derives from industry effects or firm effects. In this study, the approach is to
use multiple measures of performance, including an intellectual capital measure of
performance (value added intellectual coefficient), an economic-based measure
(economic value added), and an accounting based measure (return on assets). Findings
suggest that firm effects greatly impact knowledge-intensive industries, in particular for
value-added intellectual coefficient (VAIC). In this sense, it is proposed that
organizational strategies which leverage human capital are critical to the continuous
development of firms in Chinese and Taiwanese knowledge-intensive industries.
Concurrently, industry effects also influence economic performance, suggesting overall
that industry membership is an important indicator of a knowledge-intensive firm's
capability in value added by capital invested. This paper contributes to a scarcity of
variance decomposition studies examining the sources of variation in firm performance
emphasizing on knowledge-intensive industries in developing countries.
Keywords: Knowledge-intensive industries; intellectual capital; industry and firm effects;
variance decomposition analysis; performance differences
INTRODUCTION
Firm performance has been commonly matched with profit margin or increased turnover [1].
One of the most significant strands of strategy research has been to compare the relative
importance of industry and firm effects on performance heterogeneity. These two effects are
confronted through two fundamental research streams which aim to explain differences in
firm performance: on one hand perspectives drawing from the economic tradition that
emphasize external market factors and firm characteristics as primary factors of firm
Copyright © Society for Science and Education, United Kingdom
53
Zhou, L., Borah, P. S., Chen, Y., & Nisar, N. (2017). Determinants of firm Performance In Knowledge-Intensive Industries: A Comparative Study
of Two Emerging Economies, China and Taiwan. Advances in Social Sciences Research Journal, 4(5) 53-71
performance; and on the other hand, building on the sociological and behavioural paradigm,
perspectives that emphasize on the impact of organizational factors and their environment.
Over the last decades, the world economy has witnessed a revolution in terms of the nature of
value creation. Towards this end, there has been a notable shift in regards to the source of
value, from tangible assets to intangible assets at a fast accelerating pace. Intangible assets,
also called ‘intellectual assets’ [2], are thought of as the lifeblood of creative and knowledgeintensive industries which have become the leading industries in the world through
international dealings of service flows [3]. ‘Knowledge-intensive firms’ refer to the firms that
undertake complex operations of an intellectual nature in which human capital is the dominant
factor [4] for promoting innovation [5]. The OECD has classified business services,
communication services, financial services, educational services, and healthcare services as
belonging to knowledge-intensive service industries [6]. One condition for knowledgeintensive industries is how there is no dedicated R&D team or innovation department in
knowledge-intensive industries, and the preferred way of operation involves teams working on
a rotation basis [7]. What can be claimed is that value creation from knowledge assets is a
cornerstone of firm strategy [8]. The latter refers to the conceptualization of value creation
found in the value-added by each activity, namely a firm’s value chain [9]. Based on this
argument, it can be claimed that knowledge-intensive industries contribute significantly to
economic competitiveness and growth. Concurrently, the majority of knowledge-intensive
companies have emerged from traditional, small-scale businesses to becoming enterprising
organizations [10].
In face of this development, it becomes meaningful to examine the sources of variation in firm
performance among these knowledge-intensive organizations since it can contribute to a
better understanding of the global economy. Benchmark research on the determinant of the
performance in 1985 ascribed the industry factors as the main determinants of firm
performance, whilst research from that point on until 2006 found that firm-specific factors
determine performance to a greater extent than that of the industry factors. Researchers
nevertheless are not conclusive on the primary source of firm performance in general. An
increasing body of research therefore continues to explore the issue yet there has been a
scarcity of research examining in particular the knowledge-intensive industries [11]. In this
respect, this study can contribute to the field by addressing both the debate of industry versus
firm effects on firm performance building on prior work by choosing to narrow the focus in the
Chinese and Taiwanese context as emerging economies with tremendous potential as it will be
explained in the following section. The internal and external environments of the firms in
China and Taiwan greatly different from those in developed countries and therefore it is
meaningful to examine the industry and firm effects utilizing data from different points of
reference for both countries.
The context
In this research, we examine two developing economies, China and Taiwan, which have been
characterized as ‘awaking giants’ following their transition from a labour-intensive to hightech industries engaging in knowledge-intensive services. The international trends of the
Organization for Economic Cooperation and Development (OECD) towards a more knowledgebased economy, have led to significant changes in Chinese society in recent years [10]. In fact,
it is expected that China will surpass the United States as the single largest economy by the
year 2050 [12] and it is anticipated that it will become the country with the largest proportion
of enterprises among the world’s top 500 companies [13]. In an attempt to sustain China’s
rapid economic growth, it has become key to enhance the capacity for innovation that will lead
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Advances in Social Sciences Research Journal (ASSRJ)
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to a solid industrial structure and a change to the economic growth pattern. In this respect,
knowledge-intensive industries in China have been booming denoting a considerable progress
among developing countries and it is expected that China will gradually continue to grow in
these industries [13]. In particular, the share of China’s high tech and knowledge-intensive
industry in GDP rose from 23,3% in 2007 to 25% in 2010 and it is estimated to reach 30% and
35% by 2020 and 2030, respectively [13]. The intention is that China further develops as a
knowledge-intensive, resource-intensive and ecologically-friendly industrial system to support
sustainable development and stimulate the reform of industrial and employment structures
[13].
The second country under scrutiny for this research was Taiwan. Taiwan is an interesting
example of a country whose rapid economic growth and industrialization after the early 1960s
and in particular following the new millennium has been remarkable. The development of
knowledge-intensive industries in Taiwan at a rapid race as the Council for Economic Planning
and Development suggests, has been a determining factor that triggered Taiwan’s economic
transformation. The percentage of the total GDP contributed by Taiwan’s knowledge-intensive
industries is 20.4% in 2012 [11]. Taiwan’s rather recent economic development would not
have been possible without the local government’s contribution. The Taiwanese government
prioritizes development deriving from intellectual capital, which relates to physical assets,
based on the belief that this can lead to develop its status as an emerging economy [14]. In
addition, firms in Taiwan, in contrast with those in China, possess tangible and intangible
knowledge, in particular when it comes to the high-technology industry. Taiwan is strong in
keeping ‘strategic assets’ such as human capital, brand reputation and managerial capabilities
to a high standard [15]. Capabilities, described as bundles of organizational processes in a
company, have been said to determine the firm-specific heterogeneity through different ways
[16]. Taiwanese knowledge-intensive firms have been systematically investing in innovation
through development of in-house R&D and absorbing foreign knowledge as a means to
effectively cope with international competition [17]. It has been empirically proven that firm’s
R&D capacities greatly influence firm performance regardless of whether knowledge sources
are external or internal.
This paper addresses the aforementioned dilemma first by reviewing relevant literature and
exploring the differences in various studies. Then, a discussion of performance measures is
analysed including the proposed approach for this study which is further explained in the data
and sampling and methodology section. These sections are followed by the empirical analysis
results and the discussion of implications from the differences arising between this study and
previous research in the field. Finally, the paper concludes with a discussion of the results and
final remarks.
Determinants of firm performance
As already mentioned in the introduction of this paper, researchers across the industrial
organization and strategic management fields have diachronically addressed the determinants
of firm performance. The industrial organization economics perspective adheres to a
theoretical framework known as the structure-concept performance (SCP) model. In this
perspective, it is proposed that the structural characteristics of an industry inevitably restrict
common patterns of behaviour of its component firms, which on the other hand leads to
industry-specific performance differences between firms [18-21]. In other words, the belief is
that industry structure is a central determinant of firm performance. In this conceptualization,
an important line of work involved examining the role of firm size as a factor to explain
differences in profitability [22], acknowledged as a source of competitive advantage.
Conversely, the strategic management view emphasizes on the firm itself to explain differences
Copyright © Society for Science and Education, United Kingdom
55
Zhou, L., Borah, P. S., Chen, Y., & Nisar, N. (2017). Determinants of firm Performance In Knowledge-Intensive Industries: A Comparative Study
of Two Emerging Economies, China and Taiwan. Advances in Social Sciences Research Journal, 4(5) 53-71
in performance and cohorts that firm-specific attributes drive performance outcomes [23-25].
This shift in reasoning was the result of inability of the industrial organization theory to offer
sustainable explanation for intra-industry differences in performance.
Schmalensee [26] and Rumelt [27] introduced the use of variance decomposition methodology
to study differences in performance derived from industry and firm effects. When comparing
findings from the two studies, contradictory findings emerge. Schmalensee found a significant
impact of the industry effects on performance, whereas Rumelt argued that his results
suggested firm effects have a larger impact on firm performance. Ever since the seminal work
of Schmalensee [26] and Rumelt [27], there have been over 25 further attempts to resolve this
controversy on the assessment of the relative importance of various drivers of heterogeneity in
firm performance. Empirical studies along the lines of Rumelt’s work included: Powell [28]
examined the influence of industry effects only; Roquebert, Phillips, and Westfall [29];
McGahan and Porter [30]; Cubbin and Geroski, [31]; Mauri and Michaels [32] looking into the
influence of industry and firm effects together; or the influence of industry and organisational
effects together [33].
A distinct difference in some of these studies is how for instance Roquebert, Phillips and
Westfall [29] and McGahan and Porter [30] followed a similar methodology however using
another database, the Compustat Business Segment Reports. This software covers a more
extensive array of data through analysis from all sectors of the American Economy allowing
service industries to be included in the analysis, whereas the FCT data set was based on
manufacturing industries alone. In addition, and distinct from the work of Schmalensee and
Rumelt, using the Compustat database reported the data at the level of the business segment
and not the business unit level. Hough [34], also employed analysis of business segment effects
and found twice as much variance in performance as corporate effects. In addition, findings
from Hough’s [34] study suggested that corporate effects had four times more variance in
business segment performance than industry effects and finally, that segments explained eight
times more variation when compared to industry effects. Brush, Bromiley, and Hendrick [35]
and Ruefli and Wiggins [36] utilized different approaches. The former use a two-stage least
squares model (2SLS) and the latter uses a non-parametric, ordinal variable approach. On the
other hand utilizing the business unit level found that firm factors overruled industry factors.
Mcnamara, Aime, and Vaaler [37] also cohort that firm effects have a significantly larger
influence on firm profitability compared to industry effects.
Regardless of whether these studies utilized business unit or business segment effects, all
confirmed that firm factors predominantly influence performance in contrast to the scarce
effect of industry factors. A significant proportion of these studies used ROA as the financial
performance measure. Nevertheless, it has been proposed that it is worthwhile to explore
alternative measures of firm performance and new data[38]. Towards this end, Hawawini,
Subramanian, and Verdia [39] and Chen and Lin [40] employed the U.S. Stern Stewart dataset
and Taiwan Economic Journal (TEJ) dataset, respectively, to revisit the question of the relative
importance of industry and firm effects on firm performance. In alignment with previous
findings, these studies further confirmed that industry factors have a minimal impact on firm
performance. Notably though, Hawawini et al. [39] concluded that industry in fact mattered for
firms with average managerial capabilities and performance.
McGahan and Porter’s [30, 38] study demonstrated that there is a difference of the importance
of various effects on profitability across sectors. The most critical strategic resource of
organizations, with direct impact on their market competition and their performance has been
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Advances in Social Sciences Research Journal (ASSRJ)
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intellectual capital [11]. It is therefore imperative to measure and value intellectual capital to
enable knowledge-intensive firms to reach their true potential. Pulic [41], taking this
consideration into account, developed the VAIC method, which explicitly considers intellectual
capital to measure the value creation efficiency of intellectual capital within a company. It is
important to note however that each measurement criterion measures firm performance from
different perspectives while no criterion dominates over others [42]. Iazzolino and Laise [42]
suggest that VAIC can be used to complement the traditional accounting or economic measures
rather than considering it separately. The VAIC method has been vastly used in academia and
business, becoming an important indicator in helping researchers to measure firms’
intellectual capital performance [43].
More recent studies have expanded this strand of research by incorporating new variables and
cross-country differences [44-51]. Despite the majority of these studies, in particular variance
decomposition indicate the dominance of firm effects over industry effects contributing to
determining effects in firm profitability, still it is meaningful to conduct a new study on the
impact of industry and firm effects, since it is possible that the results are influenced by the
specific performance measure used. More particular, it is not absolute that the performance
measure used in previous studies forms a reliable indicator of economic value. In addition, the
generic view that firm effects are relatively more important than industry effects does not
necessarily hold true for all firms. For instance, it is not definite that industry and firm effects
are the same among different classes of firms within the same industry. In addition, it is
meaningful to conduct a research which examines the knowledge-intensive service industries
sector which has seen unprecedented development over the last two decades and also a
sample from developing countries since most of the studies conducted so far are based on US
data and a significantly less number examines emerging economies. Among the few is a study
by Abdullahi, Abubakar, Aliyu, and Umar [52], who conducted an empirical review were
different variable or factors that influence firm performance in developing countries were
retrieved. The majority of these studies do not explicitly state their sample size, method of data
collection, and the theories that support or direct their studies. Importantly, the studies
examined did not focus specifically on knowledge-intensive service industries. However, it is
crucial to determine these variables as they are amongst the determinants of firm performance
in developing countries. With regards to China, there have been certain insufficiencies of
current Chinese domestic studies [53] examining the determinant of firm performance.
Taking the previous into consideration, this paper contributes to the debate of the relative
importance of firm versus industry effects, focusing in particular on knowledge-intensive
service industries performance in China and Taiwan as emerging economies by applying the
indicator of VAIC as an intellectual capital performance measure.
Performance measure
Until the early 2000s, most studies focused on return of total assets as a measure of
performance. Nevertheless, several disadvantages have been identified to arise from
accounting conventions such as the inability to measure cash flows, and how returns are not
adjusted for risk. In this sense, accounting rations were inaccurate in providing information on
either past economic profitability or the firm’s future profitability [39].
Taking the previous into consideration, this study drawing on Chi et al.’s [11] approach, utilizes
three different performance measures: the intellectual capital measure ‘value-added
intellectual coefficient’ (VAIC), the economic measure EVA, and the accounting measure ROA
(returns on assets). The former is estimated by measuring the operating income divided by
total assets. This ratio is considered to be an important indicator of financial performance [54].
Copyright © Society for Science and Education, United Kingdom
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Zhou, L., Borah, P. S., Chen, Y., & Nisar, N. (2017). Determinants of firm Performance In Knowledge-Intensive Industries: A Comparative Study
of Two Emerging Economies, China and Taiwan. Advances in Social Sciences Research Journal, 4(5) 53-71
Nevertheless, this approach has been judged as not measuring the cost of capital, while also
some conceptual problems arise from accounting conventions. In this sense, ROA, due to
shortcomings in capitalization of accounting practices, does not provide with any information
on either past economic profitability or the firm’s future profitability [39].
EVA and MVA have been developed as economic measures of performance already from 1991
by The Stern Stewart Co. Both types of measurement refer to residual income, which concerns
the way a firm achieves sustainable value creation only when its capital returns exceed its
capital costs. Instead of using ROA, it is considered best to address capital cost, risk and the
time value of money, to better reflect the economic value of a firm. It has been proposed that
the adoption of the EVA performance measure leads to the need to focus on strategies for
economic performance. In addition, as the EVA aims to offer improvements to the MVA
calculation [55], this paper will consider the role of EVA only in the valuation of firms.
Importantly, just as Hawawini et al. [39] argue that what drives ROA does not equal to what
drives economic performance, in the same vein what drives EVA does not equal what drives
intelligence capital performance. A firm’s market value is the result of capital employed and
intellectual capital, consisting of human capital and structural capital. Human capital should be
perceived as an investment and not a cost [43]. Rather than directly measuring firms’
intellectual capital, Pulic [41] developed the VAIC to efficiently monitor and evaluate the
efficiency of value added (VA) to a firm’s resources. This approach primarily measures the
efficiency of firms’ three types of inputs: physical capital employed (CA), human capital (HC),
and structural capital (SC), namely the Capital Employed Efficiency, the Human Capital
Efficiency, and the Structural Capital Efficiency. The sum of the three measures is the value of
VAIC. Within a conceptualization of intellectual capital as the knowledge and ability that
employees bring to their firms, it is derived that it increases their competitive advantage [56].
As such, an organization’s employees’ should be seen as a critical strategic source [11].
In this respect, key to the Pulic model is that labor expenses are considered an investment – a
value creating entity- and not a cost. VAIC demonstrates the value-creation efficiency of a
firm’s total resources, including its intellectual capital. The estimate is that the higher the VAIC
coefficient, the better management has made use of the firm’s value-creation potential. Hence,
the VAIC method elicits important information as to whether managers leverage their firms’
potential and maximize its value in the marketplace [41].
Due to the fact that VA is estimated based on how a certain business uses its total resources,
which include physical, human and structural capital, it becomes essential to enquire into the
relationship between VA and the aforementioned types of capital. Consequently, at first the
relationship between VA and physical capital employed (CA) were examined.
VA=CA ¼ VACA
(1)
VACA, namely the Value Added Capital Coefficient, represents the figure of new value which
has been created by one invested unit of capital employed. Following this procedure, the focus
is on the relationship between VA and employed human capital (HC), which gives insights into
the potential for HC to create value in a firm. Pulic argues that HC represents the amount of
human capital invested in knowledge workers.
VA=HC ¼ VAHC
(2)
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Advances in Social Sciences Research Journal (ASSRJ)
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In this equation, VAHC, represents the Value Added Human Capital Coefficient, and indicates
the proportion of VA created by one monetary unit invested in employees, In other words, this
coefficient concerns the productivity of knowledge workers in a firm. The next step entails to
retrieve the facts on the relationship between VA and employed structural capital (SC). Pulic
proposes that SC is the share of value added following deducting investment in HC.
SC=VA ¼ STVA
(3)
STVA, the Value Added Structural Capital Coefficient, aims to measure the share of SC in the
creation of value added. Finally, it is significant to calculate the intellectual ability of a firm,
which is the sum of the three components mentioned before.
VAIC ¼ VACA + VAHC + STVA
(4)
VAIC demonstrates the value-creation efficiency of a firm’s total resources. This includes its
intellectual capital. The higher the VAIC coefficient is, it suggest better management of the
firm’s value-creation potential. Hence, the VAIC method ultimately provides information on
how managers leverage their firm’s potential and maximize its value in the marketplace [41].
DATA AND SAMPLING
The source of data for this research, including all information essential to calculate VAIC, EVA,
and ROA, have been drawn from the CEIC China Premium Database and the datasets provided
by the National Science Board Science and Engineering Indicators – 2016 reports are used.
Regarding the Chinese context, the CEIC database provides with complete corporate-level
financial information owned by the enterprises listed and traded on the Shanghai Stock
Exchange (SSE), one of the two major stock exchanges in mainland China. The National Science
Board Science and Engineering Indicators on the other hand entail information on production,
value added, consumption, imports, and exports from 70 countries and represents 97% of
global economic activity [57].
The term ‘firm’ instead of the term ‘corporate’ was preferred to denote an autonomous
competitive unit within an industry. In this way, the term ‘firm effects’ comprises both business
unit and corporate effects. Concurrently, it covers both performance variety attributed to
differences within industries among firms and differences among firms which cannot be
explained by their patterns of industry activities [40, 39, 27].
The latter declared, the objective in this study has been to cross-check and compare the
relative importance of industry and firm effects on firm performance in two emerging
economies, in particular in the context of Chinese and Taiwanese knowledge-intensive
industries. In a similar research by Gu, Guan and Wu [58] on the listed companies in China, the
findings were different from previous researches: it seemed that the industry factors have
notable effect on short-term payoff of the listed companies. In contrast, firms' resource and
capacities appeared to be the dominant determinants of firm values and development potential
for performance leaders and losers in the industry. Our sample covers a 5 years period from
2009 – 2014 on Chinese and Taiwanese high-tech and service sectors. According to the
Guidelines for the Industry Classification of Listed Companies issued by the China Securities
Regulatory Commission (‘CSRC’), they divide the economic activities of listed companies into
two levels: categories and classes, with reference to the Industry classification for the National
Economy (GB T47542011). We use the four levels of the Industrial Classification for National
Economic Activities revised under the leadership of the National Bureau of Statistics of PRC
and organized by the relative departments of State Council. This research drew
Copyright © Society for Science and Education, United Kingdom
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Zhou, L., Borah, P. S., Chen, Y., & Nisar, N. (2017). Determinants of firm Performance In Knowledge-Intensive Industries: A Comparative Study
of Two Emerging Economies, China and Taiwan. Advances in Social Sciences Research Journal, 4(5) 53-71
9239preliminary records for firm data. We then excluded 137 records that reported results
with missing values. The final sample consisted of 4781 observations for 434 firms across
38industry classifications for China and 4321 observations for 425 firms across 31 industry
classifications for Taiwan.
Among the few research studies examining variance decomposition in regards to country and
region effect, Chen [46] examined found that country effects dominate industry effects as far as
industry performance around the world and in all regions examined (North America, South
America, West Europe, East Europe, and Asia) accounting for over three fifths of the overall
variation in industrial production worldwide ofknowledge-intensive service industries [46].
Nevertheless, Chen [46] also found that the effects of industry are important, accounting for
over one-third of performance differences among knowledge-intensive service industries. In
contrast, it appeared that region effects had a very small yet significant impact on knowledgeintensive service industry performance [46]. Finally, Chen’s systematic review indicated that
year effects have a scarce influence on the performance of knowledge-intensive service
industries. Table 1 and Table 2 show the descriptive statistics for the economic sector in China
and Taiwan respectively.
Table 1. Descriptive statistics of VAIC, EVA, and ROA by economic sector in China.
All
Hightechsector
EVA(inNTD
millions)
Average
VAIC ROA EVA(inNTD
(%) millions)
5,152,171.2 11.
8
16
VAIC ROA EVA(inNTD
(%) millions)
VA ROA
IC (%)
7.5 16,219,325. 4.
4
61
2
1
Standarddeviatio 31,959,272. 113 13. 17,166,734. 129 11. 42,452,211. 7.
n
23
.26 03 75
.37 02 63
4
Numberoffirms
434
287
147
5
Numberofindustr 38
ies
Totalnumberofo 4781
bservations
2009-2014
Yearincluded
7.6 826,725.21
0
Servicesector
14.
67
25
13
2760
2009-2014
2021
2009-2014
3.7
8
7.7
6
Note:VAIC,ValueAddedIntellectualCoefficient;EVA,economicvalueadded;ROA,returnonasset.
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Advances in Social Sciences Research Journal (ASSRJ)
Vol.4, Issue 5 Mar-2017
Table 2. Descriptive statistics of VAIC, EVA, and ROA by economic sector in Taiwan.
All
EVA(inNTD
millions)
Average
Hightechsector
VAIC ROA EVA(inNTD
(%) millions)
4,321,026.3 11.
4
88
Servicesector
VAIC ROA EVA(inNTD
(%) millions)
7.5 804,653.24 14.
7
72
VAI ROA
C
(%)
7.5 12,740,368. 5.2 4.8
6
45
4
7
Standarddeviat 32,897,332. 110 11. 14,539,714. 129 11. 44,378,208. 6.9 8.7
ion
22
.24 44 53
.16 53 67
5
8
Numberoffirms 425
279
146
Numberofindu 31
stries
Totalnumberof 4321
observations
2009-2014
Yearincluded
17
14
2439
2009-2014
1882
2009-2014
Note:VAIC,ValueAddedIntellectualCoefficient;EVA,economicvalueadded;ROA,returnonasset.
MODEL AND METHODOLOGY
In this research the analysis results from the descriptive model of firm performance similar to
that of Chi et al. [11] and Hawawini et al. [39]:
rikt ¼ m + ai + bk + gt + εikt
(5)
At the above, rikt represents the performance in year t for firm k in industry i, and
performance is measured as EVA, VAIC, and ROA, respectively. The first-right hand-side term is
m, which represents the average performance over the entire period for all firms. The next
three terms represent the random industry, firm, and year effects. Industry effects (ai) are the
result of the differences in the average performance to individual firm within each different
industry. These effects reflect industry specific factors, for instance differing competitive
behaviour, conditions of entry, and asset utilization rate impacts.
Firm effects (bk) represent both corporate and business unit effects, the result of the
differences in the average annual performance for each firm. These effects include
heterogeneity among firms in tangible and intangible assets reflecting the influence of firm
specific factors. Year effects (gt) is the sum of the differences in the average performance of
individual firms in each year and correspond to the in influence of factors sharing broad
economic trends. Finally, εikt, is a random error term. The model employed here makes use of
classes of effects as dummy variables, offering no casual inferences or structural explanation
for variation in performance across industries, firms, or years. However, it does allows a focus
on the existence and magnitude of differences in performance which relate to this categories. A
decision was made to follow Chi et al.’s [11] approach who did not included the ‘industry-year
interaction’ term used by Hawawini et al. [39] and Rumelt [27]. This transient industry effect,
does not include similar transient effect for firm-year interactions. McGahan and Porter [30]
posit that the use of the industry-year interaction term might replace the interactions between
the other types of effects and year effects. They allowed for a first order serial correlation on
Copyright © Society for Science and Education, United Kingdom
61
Zhou, L., Borah, P. S., Chen, Y., & Nisar, N. (2017). Determinants of firm Performance In Knowledge-Intensive Industries: A Comparative Study
of Two Emerging Economies, China and Taiwan. Advances in Social Sciences Research Journal, 4(5) 53-71
the errors term in their model and acknowledged that the results are comparable only with the
stable effects in Rumelt’s work. In this study, we do not utilize a general first-order
autoregressive process on the error term while we exclude the ‘interaction effect’ terms
thinking that the model would have been over specified if we equally represented transient
industry effects and transient firm effects.
Previous studies utilized two main statistical methods for variance decomposition of firm
performance: analysis of variance (ANOVA) and variance components analysis. Under the
ANOVA approach, first the intention is to estimate a null regression model without no
independent effect on the dependent variable. Next, the independent effects are added one by
one. The following step is to calculate the increment to the adjusted R2 of the regression and
regard it as an unbiased estimate of the fraction of the variance explained by each independent
variable. Importantly, the way of introducing the independent variables largely affects the
results. Traditionally, the first entries reflect a large proportion of the variance due to the fact
that ANOVA analysis shares a feature where it introduces all of the covariance to the first
introduced effect.
The second popular method is the variance components approach which is often called the
random-effects ANOVA due to the fact that there is a random-effects assumption. The former
means that the error term, as well as all other effects in the model, are derived in a random
order using an underlying population distribution with mean zero and no variance. Once this
effect is drawn, it is considered fixed. In this manner there is no need to include the whole
population in this model. It is still possible to draw an inference about a population of effects
when using data from a random sample [59]. In addition, it is considered that random
processes generate each effect in an independent way, so that each effect is not linked with
other effects. Hence, estimating these variance components can reflect the relative importance
of various effects. The equation utilized for the aforementioned estimation is based on Chi et
al.’s [11] conceptualizations using the descriptive statistical model of Equation (5) which was
decomposed as follows:
s2r ¼ s2a + s2b + s2 g + s2 ε
(6)
The total variance s2r of performance represents the sum of the population variances in
industry, firm, and year effects. Similar to Chi et al. [11], the preferred approach involved the
use of PROC VARCOM procedure in SAS software to estimate the different variance
components. Among the four estimation methods proposed for PROC VARCOMP in SAS,
following the example of Searle et al. [60], in this study we employed the restricted maximum
likelihood (REML) method, a strategy preferred for dealing with any sum of squares methods
for unbalanced data such as ours. There are sets of useful properties in the restricted
maximum likelihood such as consistency and asymptotic normality and the asymptotic
sampling dispersion matrix. On the other hand, the inherent disadvantage of the variance
component estimation used is the unreliability when it comes to testing the significance of the
independent effects [11]. In addressing this issue, Schmalensee [26], Rumelt [27], and
McGahan and Porter [30] used nested ANOVA techniques that consider the effects to be fixed.
This approach allows for F-statistics to be produced which delineate the presence of the
independent effects. Nevertheless, it was highlighted in the aforementioned studies that the
use of the ANOVA test for significance is not a prerequisite to variance components estimation.
The primary concern is to estimate the relative magnitude of each type of effect. In this
perspective, we adhere to variance component analysis as our main approach.
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EMPIRICAL RESULTS
The results in Tables 3 and 4 express the percentage of the total variance components
estimation of Equation (6) for each of the performance measures in China and Taiwan. Using
the nested ANOVA procedure, all figures were elevated at the 5% level for statistical
significance. It appears that the performance measures VAIC, EVAand ROA are respectively
68.11%, 79.75% and 35.71% of the total variance in firm profit for China (Table 3). In respect
to Taiwan, the performance measures VAIC, EVAand ROA are 68.28%, 79.87% and 30.38% of
the total variance in firm profit (Table 4). When comparing the figures across the two
countries, it appears that the first two variations for EVA and VAIC are quite high for both
countries. In contrast, the ROA is low and in fact it is almost about half of the total explained
variations in VAIC and EVA. It can be derived therefore that there is more variation in
accounting profit ROA than in VAIC and EVA.
Table 3. Variance components results for China.
Variancecomponent
VAIC
Estimate
EVA
%
Estimate
ROA
%
Estimate
%
Firmeffects
10,468.56 67.28
9.45E+13 39.34
42.12
33.04
Industryeffects
125.36
0.78
9.45E+ 13 38.43
3.34
2.43
Year effects
19.12
0.22
1.12E+12 1.54
3.36
1.89
Model
11,736.34 68.11
1.98E+14 79.75
49.89
35.71
Error
5012.41
4.47E+13 11.25
85.65
64.29
Total
21.89
16,748.75 100.00 2.34E+14 100.00 135.54
Numberofobservations 4781
4781
100.00
4781
Note:VAIC,Value-Added IntellectualCoefficient;EVA,economicvalueadded;ROA,
returnonasset.
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Zhou, L., Borah, P. S., Chen, Y., & Nisar, N. (2017). Determinants of firm Performance In Knowledge-Intensive Industries: A Comparative Study
of Two Emerging Economies, China and Taiwan. Advances in Social Sciences Research Journal, 4(5) 53-71
Table 4. Variance components results for Taiwan.
Variancecomponent VAIC
Estimate
EVA
%
Estimate
ROA
%
Estimate
%
Firmeffects
10,967.99 67.15
9.44E+14 39.97
42.25
30.71
Industryeffects
Year effects
125.32
13.87
9.44E+14 39.85
1.14E+13 0.46
4.57
3.24
3.32
2.12
Model
Error
Total
10,816.03 68.28
5435.41 31.71
16,251.44
100.00
1.89E+15 79.87
4.45E+14 20.13
2.33E+15
100.00
48.75
88.72
137.47
30.38
69.62
4321
4321
Numberofobservations 4321
1.11
0.19
100.00
Note:VAIC,ValueAddedIntellectualCoefficient;EVA,economicvalueadded;ROA,returnonasset.
According to Table 3, the estimated variance component of the firm effects for VAIC, EVA and
ROA are 67.28%, 39.34%, and 33.04%, respectively for Chinese firms, whereas in the case of
Taiwan these figures are 67.15%, 39.97%, and 30.71%, respectively for VAIC, EVAand ROA
(Table 4). The latter suggests similar results across China and Taiwan regarding the
importance of firm effects. Comparatively, the corresponding figures in China as far as industry
effects are 0.78%, 38.43%, and 2.43%, whilst for Taiwan the estimated variance component of
the industry effects are 1.11%, 39.85%, and 3.32%. The inference from this comparison across
the two countries and across effects is that for these developing countries, the effect of firm
effects is similar. In particular in both countries the highest score is found in VAIC.
Nevertheless, when comparing with industry effects, it is evident that firm effects both in China
and Taiwan have a far greater influence for VAIC and ROA, whilst they are similar as industry
effects for EVA. It is derived therefore that firm factors contribute to a far greater extent across
all three measures of performance for both Chinese and Taiwanese knowledge-intensive
industries. Conversely, the high estimated variance components observed for industry effects
in EVA (both in China and Taiwan), suggest that industry factors contribute significantly to a
firm’s economic performance. The above results differ from the majority of findings from
previous studies which focus on economy as a whole. Indeed, there was a noticeable difference
in importance of industry effects among the three performance measures. However, previous
research claimed that industry effects contribute less to variance across performance
measures. For instance, year effects for VAIC, EVA and ROA across the two countries are 0.22%,
1.54%, and 1.89% for China (Table 3), and 0.19%, 0.46%, and 2.12% for Taiwan (Table 4),
respectively. The inference is that year to year fluctuations in macroeconomic conditions do
not substantially influence overall movement of firm performance in Chinese and Taiwanese
knowledge-intensive industries.
The findings of the COV analysis are presented in Table 5 and 6 for the two countries. It
appears that firm effects significantly impact firm performance in both high-tech and service
sectors across all three measures of performance. In particular for high-tech sectors in China,
firm effects for VAIC, EVAand ROA are 67.35%, 38.12%, and 32.23%, respectively (Table 5). In
terms of the Taiwanese firms, VAIC, EVA and ROA form 66.76%, 39.16%, and 31.89% (Table
6). With regards to the service sectors, the corresponding figures for firm effects in China are
37.78%, 35.43%, and 28.14% (Table 5) and 39.12%, 36.63%, and 27.02% for Taiwan(Table 6).
In the case of high-tech sectors, firm factors dominate industry factors across performance
measures in both China and Taiwan. This prevalence of firm effects is more profound when
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Advances in Social Sciences Research Journal (ASSRJ)
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firm performance is measured by VAIC. However, notably firm effects do not dominate for EVA
in service sectors in either country. Looking at China, firm effects are more important in hightech sectors when comparing to service sectors to a significant degree. The same stands for
Taiwan although to a lesser degree.
Table 5. Variance components results by economic sector in China.
VAIC
Firmeffects
Industryeffects
Yeareffects
Model
Error
Total
Numberofobservations
ROA
VAIC
EVA
Estimate
Percent
EVA
Estimate
Percent
Estimate Percent
Estimate Percent
Estimate
Percent
Estimate Percent
15,134.20
169.66
38.14
16,694.45
8112.13
24,806.58
2760
67.35
0.78
0.21
68.15
31.85
100.00
101,189.32
2162.21
449.83
115,781.21
160,190.78
275,971.42
2760
38.12
0.82
0.15
37.63
62.37
100.00
52.26
2.23
4.28
62.53
104.83
167.36
2760
22.32
9.67
0.84
33.18
23.58
56.76
2021
9.67E+13
1.15E+14
1.12E+12
2.14E+14
4.49E+13
2.55E+14
2021
35.43
43.62
0.42
81.81
18.19
100.00
23.21
3.31
1.23
27.71
53.32
81.03
2021
Variancecomponent
32.23
1.56
3.12
35.87
64.13
100.00
High-techsector
37.78
15.87
1.43
56.45
43.55
100.00
ROA
28.14
3.87
1.42
33.61
66.39
100.00
Servicesector
Table 6. Variance components results by economic sector in Taiwan.
Variancecomponent
VAIC
Firmeffects
Industryeffects
Yeareffects
Model
Error
Total
Numberofobservations
High-techsector
EVA
Servicesector
ROA
VAIC
EVA
ROA
Estimate
Percent
Estimate
Percent
Estimate Percent
Estimate Percent
Estimate
Percent
Estimate Percent
16,146.11
189.62
35.27
16,943.67
8132.35
25,076.02
2439
66.76
0.78
0.20
67.12
32.88
100.00
111,179.30
2117.21
445.51
107,563.655
165,568.56
273,132.21
2439
39.16
0.75
0.16
37.89
62.11
100.00
53.17
2.31
5.17
60.65
105.64
166.29
2439
23.42
9.81
0.76
32.01
25.43
57.44
1882
9.78E+11
1.15E+14
1.12E+13
2.14E+14
4.46E+13
2.57E+14
1882
36.63
42.75
0.24
78.38
21.62
100.00
22.76
2.18
1.16
31.62
53.21
84.83
1882
31.89
1.26
3.21
35.17
64.83
100.00
39.12
17.43
1.47
58.15
41.85
100.00
27.12
3.87
1.47
35.79
64.21
100.00
One inference which is in common with Chi et al.’s study is a proportional variation in the
importance of industry effects between high-tech and service sectors. More specifically,
industry effects contribute more to total variance across the three measures of performance in
the case of service sectors than high-tech sectors. These effects are considerably larger for EVA
when the average EVA within an industry maintains abnormally high or low for sample period
of coverage. This phenomenon is explained because EVA corresponds to the value-added
ability of the firm on capital and it is possible to have a permanently low or high value due to
the capability of incumbents resulting from physical and financial capital. The inference is that
the dominance of industry effects over firm effects to EVA suggests that shareholders pertain
industry information when making their assessments about management strategies when
deciding on a service firm’s value creation of invested capital. Lastly, year effects in this study
are similar for China and Taiwan and do not take substantial portion in comparison to firm and
industry effects in both high-tech and service sectors across the three performance measures.
DISCUSSION
This research examined in particular the relative importance of industry and firm-level effects
on performance focusing primarily on China’s and Taiwan’s high-tech and service sectors. In
addition, the research focused on firm performance, using VAIC, a tool in addition to the
economic measure EVA and the accounting measure ROA. The analysis yielded some important
findings which are noted below.
To begin with, it appears with regards to this study that firm effects are significant across all
three measures of performance for both countries. Specifically, firm effects present much
greater value that industry effects for VAIC and ROA whilst they are almost identical for EVA.
The previous are complementary to the view that organizational resources and capabilities
greatly influence on performance in Chinese and Taiwanese knowledge-intensive industries.
Copyright © Society for Science and Education, United Kingdom
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Zhou, L., Borah, P. S., Chen, Y., & Nisar, N. (2017). Determinants of firm Performance In Knowledge-Intensive Industries: A Comparative Study
of Two Emerging Economies, China and Taiwan. Advances in Social Sciences Research Journal, 4(5) 53-71
This is the case in terms of measurement using VAIC in high-tech sectors where firm effects
seem to dominate over industry effects. In contrast, when it comes to service sectors, this
dominance of firm effects is not found for EVA. It can be claimed therefore that firm effects
prevail in high-tech sectors unlike the service sectors.
Secondly, it is evident from the findings in this research that industry effects have a substantial
impact on economic performance. Data derived from the analysis indicate a succinct variation
in the importance of industry effects across different performance measures for both China
and Taiwan. What was evident is that industry effects are at a lower rate when it comes to
VAIC and ROA, whereas for EVA, findings suggest they are comparable to firm effects. The
inference from the results is that industry effects are contributing to firm’s value creation by
capital invested, yet this is not the same as far as the firm’s value creation by intellectual capital
in knowledge-intensive industries. This research suggested that industry factors are of equal
importance to economic performance as firm factors in knowledge-intensive industries. It
appears that this finding does not comply with previous research suggesting that industry
effects contribute less to variance across different performance measurements. Another
important inference deriving from this research is that industry factors may vary in their
meaning for firm performance across economic sectors. For instance, while industry effects
account for less variation across all measures of performance, the figures are considerably
large in terms of the variation for EVA and VAIC in service sectors. In fact, when performance is
measured with EVA, industry effects overcome firm effects in service sectors. The
aforementioned could be explained by looking at the industry membership as an important
indicator of a service firm’s capability in value added by capital invested. In addition, it was
found that year effects minimally affect firm performance ascribing for a very small variation in
the high-tech and service sectors across the three performance measures.
Overall, the comparative findings derived suggested that for both China and Taiwan, industry
structural characteristics greatly influence China’s and Taiwan’s knowledge-intensive
industries. The indication was that firm effects were considerably more important to VAIC than
EVA and ROA in the high-tech sector. It is therefore assumed that organizational capabilities
which leverage human capital are incremental towards the learning and growth of high-tech
firms. Furthermore, it appears that industry effects prevail for EVA in the service sector, which
can lead to the inference that shareholders pay additional attention to industry structural
differences when making their assessments on the economic value of a service firm’s.
CONCLUSIONS
The intention in this study was to take deep insights into the determinants of firm performance
in knowledge-intensive industries across two emerging economies, that of China and Taiwan,
focusing on year, industry and firm effects. The study yielded data from a five year period
between 2009 and 2014. Given that developing human capital is a major input factor in
knowledge-intensive business services [61], it was considered best to implement an
alternative measure of performance, namely VAIC, as a means to supplement the analysis using
EVA and ROA. The overall findings suggested similar results for China and Taiwan in respect to
firm effects. It was found that for both countries, firm effects have substantial value across
performance measures, with an increased degree in Taiwanese knowledge-intensive
industries, although they are almost equally important as it appears for China as well,
especially for VAIC. Hence, it can be claimed that organizational capabilities that leverage
human capital play an important role in developing learning and growth in knowledgeintensive industries.
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Similar to other research findings, this study suggested that firm’s resources and capability
have greater impact than industry factors when it comes to industries with higher degree of
marketization. Different studies suggest that individual companies ‘utilize their own internal
knowledge resources in combination with that of their partners’ [61]. In a study by Chow and
Gong [62] examining the role of HRM in China's technology-intensive industries, it was found
that the firm's innovation capability was an important determinant of firm performance. The
latter finding points to the need for both Chinese and Taiwanese firms to invest in the
development of their resources and capability. Due to the fact that knowledge-intensive
industries have to make their way in a complex and competitive environment, it is essential for
organizations to exploit their human resources to produce innovation capabilities so that they
retain a competitive advantage and survive [62]. Human resources management (HRM)
strategies are critical when it comes to promoting creativity among employees [62, 63]. In fact,
it has been empirically proven that investing in R&D activities helps release innovation
potential [64, 65] since this process is closely depended to the capability of a firm to adopt
proper human resource management policies relevant to succeeding the company’s innovation
goals [66]. In addition, training of employees can potentially raise their feeling of ownership
and can lead to more personalized and accountable services for customers, which is not
possible without well-trained and knowledgeable employees [67]. In a study by Nguyen,
Truong and Buyens [68] examining training and firm performance in economies in transition,
the review results indicated that training is positively related to firm performance.
Taking the previous into consideration, establishing a systematic training mechanism can
improve the level of training courses provided to employees enhancing their professional
managerial skills. PCSC is such an example of a company utilizing this strategy successfully to
expand its marketing network in Taiwan, China, and other countries. As far as rewards policy,
Chien et al. [69] posit that it is meaningful to address performance-based salary as a means to
enhance R&D professionals’ job performance. In this way, it is important for high-tech
organizations to adopt performance-based pay, as a reward to motivate R&D professionals.
Already, this is taking place in Taiwan, as it was exhibited by the study of Lin et al. [70]
investigating the relationship between employees’ reward plans and firm performance among
Taiwanese firms. On the other hand, this combination of commitment and control HR practices
reflects the unique context and trajectories of Chinese business today [71]. Wei [72] examining
pay of performance (PFP) in China’s non-public sector knowledge-intensive industries, found
that it is a highly preferred practice widely adopted.
In addition, the results from this study indicate that industry factors have little importance for
VAIC both in the case of China and Taiwan. In Taiwan and China likewise, the unprecedented
economic growth and transformation of economic structures have led to increased demand for
skilled technical workforce [73]. Towards this end, the Taiwanese government has encouraged
private enterprises to offer more training opportunities to their employees seeking to step up
in terms of their overall knowledge and skills, a tactic to which the Taiwanese government
should pertain on a more systematic base [74]. In the case of China, both the government and
enterprises have emphasized on the job training as a possible solution to address the skill
shortage [75-77]. Nevertheless, it has been empirically proven that workplace-based training
in China is underperforming in terms of quality and quantity [78, 79, 77, 80]as a means to
avoid poaching when succeeding to train high-skilled professionals [77].
The findings from this study also suggested that industry effects are considerably larger when
it comes to EVA in the service sector, in accordance with McGahan and Porter’s [30], and Chi et
al.’s [11] findings. The latter suggests that whether in the United States or Taiwan and China,
industry effects do impact performance in service industries. There is a consensus that
Copyright © Society for Science and Education, United Kingdom
67
Zhou, L., Borah, P. S., Chen, Y., & Nisar, N. (2017). Determinants of firm Performance In Knowledge-Intensive Industries: A Comparative Study
of Two Emerging Economies, China and Taiwan. Advances in Social Sciences Research Journal, 4(5) 53-71
significant differences exist among the different types of service industries, such as in the case
of domestic service industries and export-oriented industries.
There were certain limitations in this research which should not go unnoticed. The model
excludes the transient effects terms in an attempt to avoid over specifying the model by equally
representing transient industry effects and transient firm effects. Most importantly, the
fieldwork for this study involved data from Chinese and Taiwanese listed companies which
represent only a small portion of companies in the respective countries. Secondly, although
based on the findings from this research it is possible to generalize for other countries, still
future work is needed in a number of developing countries to determine the extent to which
industry or firm effects influence firm performance in knowledge-intensive industries in these
emerging economies. On the other hand, it should be noted that the sample used was
considered substantial for the purposes of this study whilst the data for analysis were derived
from a rather recent and long period, focusing on the previous five years. This suggests that
one interesting strand to take future research would be however to focus on sources of
variance in firm performance focusing on identical specific industries and compare the findings
between developed and developing economies.
ACKNOWLEDGMENT
The authors would like to thank three anonymous reviewers for their valuable comments. We
are grateful to the financial support provided by the major project of the National Social
Science of China (no.15BGL200). Furthermore, the authors also thank The Overseas Education
College of Jiangsu University for their valuable support especially the Dean Prof. Jing Gao and
PhD supervisor Dr. Yong Cui.
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