3.3 Dependent Variables – The MCS
INDEX
ABSTRACT
The studies analyze the MCS Sophistication in High Tech Firms in China and the relationship with
CEO Characteristics and the firms’ shareholding structure. Data of 179 firms operating in the Beijing
Technology Z-Park have been collected in 453 one-to-one interviews with senior executives, comprising more than 800 hours of transcripts. An index representing the MCS Sophistication has been developed and testes, and in general a strong positive relationship has been found between the International
Exposure of the CEO and the Firm and the level of MCS Sophistication in the firm. Positive relationship has also been found between the CEO Education level and the MCS Sophistication index. At the contrary a significant negative relationship emerges between the concentration of the shareholding structure, sign of early stage or founder centric entities, and the MCS Sophistication index, revealing how more concentrated shareholding leads to less sophisticated MCS.
1.0 INTRODUCTION
The study of Management Control Systems and their use and usefulness (Chenhall, 2003) has been traditionally focused on stable and well developed organization (Davila, Foster, & Oyon, 2009) with few exceptions of studies focused on startups or early stage startups, and mostly focused on companies and enterprises outside Mainland China. Moreover the studies on the Chinese firms are more concentrated on the effects of Management Control Systems on profit or growth performances (Duh, Xiao, & Chow, 2009) or on how the external environment contextual variables influence the development of the MCSs
(O’Connor, Vera-Muñoz, & Chan, 2011). Little attention has been dedicated to how internal factors such as the CEO Characteristic and the Shareholder structure influence the adoption and development and the sophistication of the MCSs in Fast Growing companies in China.
This paper examines around 200 Fast Growing High Tech enterprises in the Beijing Technological
Park, analyzing 46 individual Management Control sub systems (Davila & Foster, 2007) grouped in 8
Management Control Systems and how key internal factors, as the CEO Education, the International
Exposure of the management and the company, the ownership structure of the company, influence their completeness, usage, extent of adoption, in a combined index referred as MCS sophistication
First of all China represents a very different environment to study the effects of CEO Education and
Experience and company ownership, compared to other western countries. Only in the past 20-30 years, recently in economic cultural terms, China has dismantled the centralized driven economic structure and
has permitted private initiative to develop. It has been studied that management appointed in the centralized and state driven economy did not and do not appreciate the importance of MCS or modern management accounting practices (Duh, Xiao, & Chow, 2009; Liu, 2006; Xu & Wang, 1999). At the same time different studies have demonstrated how the management culture differs between Chinese executives and managers and their western counterparts (Redding & Ng, 1982; Hofstede, 1984;
Ardichvili, Maurer, Li, Wentling, & Stuedemann, 2006) and that those differences imply different levels of Management Control System adoption and adoption timing (Chalos & O'Connor, 2004; Tang, Gao, &
Du, 2006; O’Connor, Chow, & Wu, 2004). It is therefore conceivable that foreign educated CEOs and
Managers, that have lived and trained in a more MCS oriented culture, would perceive differently the importance of MCSs in the company development, having some studies demonstrated how CEO beliefs and education can influence the adoption of MCS (Davila & Foster, 2005).
In the same context the Company and CEO International Exposure should have the same effect on
Management Control Systems. Not many studies have focused on the impact of the exposure to international market on the MCS of emerging economies firms. Among them Anderson and Lanen (1999) were among the first to analyze the influence of the competition on foreign markets by India companies followed by very few other studies among which O’Connor at al (2011) that specifically analyzed the effects of the International Exposure using archive data on a set of 154 Chinese listed firms. This paper aims to fill the gap analyzing non listed firms and the effect of the International exposure on the adoption and evolution of the MCS systems, hypothesizing the same correlation effects on non-listed companies.
Clarke (2003) epitomizes clearly that in China “ the special circumstances of state-sector enterprises ends up hijacking the entire Company Law, so that instead of state-sector enterprises being made more efficient by being forced to follow the rules for private-sector enterprises (the original ambition), potential private-sector enterprises are hamstrung by having to follow rules that make sense only in a heavily state-invested economy.” showing how also the corporate governance structure is peculiarly different from western practices (Tam, 2000; Qi, Wu, & Zhang, 2000). Different studies have shown how the company ownership structure has a specific influence in the management practices (Chen, Firth, Gaoa,
& Rui, 2006; Firth, Fung, & Rui, 2006), pointing out the possibility that different ownership structures imply a different adoption and sophistication of the management control system (Davila & Foster, 2005;
Davila & Foster, 2007), especially in the Chinese business environment. Chennal (2003) affirms that business environmental uncertainty and risk are associated with more open and flexible MCS (Gordon &
Narayanan, 2002) and that limited resources focus managers to tighter budget controls and therefore more emphasis on MCS (Henri, 2006; Asel & Wu, 2009). It is reasonable to think that the different levels of
uncertainty experienced by the CEOs related to the ownership structure have an impact on the MCS
Sophistication.
Fast Growing High Tech enterprises, as well as startups, face serious challenges in relation to management control, with low structured organization and the chaos deriving from a mixture of formal and informal controls. At the same time in contrast to large and established organizations that are characterized by diversified and complex operations (Auzair & Langfield-Smith, 2005; Chenhall, 2003), startups and fast growing enterprises typically show ‘organic’ organizational structures with short history and low stratification of causes and therefore with the possibility to easily establish causal links between internal factors and the MCS evolution. (Davila & Foster, 2007)
2.0 HYPOTHESIS DEVELOPMENT
Prior researches have been focused on the analysis and determination of the mediating and influential factors that affects the development (Chenhall, 2003) or the adoption timing (Sandino, 2004) of one or more MCS system or sub system (Davila & Foster, 2007). Analyzing the internal and structural factors that influence the development of the MCS, I decided to focus on the relationships between the influential factors and the development in general of the MCS as a whole more than focusing on a single specific system, this also due to the lack of specificity of most of the previous researches and under the consideration that as seen in previous researches, it is safe to assume “ that Chinese firms MAC [MCS] practices are primarily motivated by performance concerns ” (Duh, Xiao, & Chow, 2009).
CEO Influence
CEOs contribute both to the decision making procedure and organizational development, prior literature points out the most important four processes: first, CEOs affects market entry and customer selection. In the case of startups, the very first and perhaps the most important decision for the firm after its establishment is location choice because this decision largely determine the market the firm will face
(Deeds, Decarolis, & Coombs, 2000; Wright, Snell, & & Dyer, 2005; Kuo & Fang, 2009); second, CEOs help shape the firm’s culture or tone at the top, which in turn can influence the way in which MCS are deployed and used (Hanges, Lord, & Dickson, 2001; Schein, 2004; Tsui, Zhang, Wang, Xin, & Wu,
2006); third, CEOs can encourage R&D activities. Management characteristics can explain a significant proportion of the sample variance in firms’ R&D spending (Barker & Mueller, 2002; Davila, Foster, &
Oyon, 2009) and fourth, CEOs have a positive effect on firm growth and development. Indeed, CEOs’
distinctive characteristics have been found to be associated with changes in stock price and company valuation. (Coughlan & Schmidt, 1985; Daily & Schwenk, 1996; Palmon & Wald, 2002)
CEO Work Experience
CEO influence is extremely important especially in small and startup companies (Miller, DeVries, &
Toulouse, 1982), especially considering that in many cases the founder is the CEO of the company for an extended period of time. Davila & Foster (2005) have clearly demonstrated how the experience of the
CEO, measured in numbers of years of management, has an impact on the adoption timing of the MCS.
At the same time Baker & Mueller (2002) has related previous functional work experience to management decisions and in particular to R&D Spending, giving evidence that previous experience can influence the current management style of the CEO. Some studies have also suggested an inverse relationship between the CEO strength and experience and the size of the Top Management team
(Amason, 1996) and that it is possible to use the inverse relationship as a control measure of the CEO
Management experience. I therefore hypothesize:
H1: CEO Work Experience is positively associated with the MCS Sophistication
CEO Education
Different studies have associated CEO Demographic characteristics, among them education, to the choice and the adoption of MCS. It is clear since at least 50 years that higher education is linked to better information understanding and processing (Schroder, Driver, & Struefert, 1967) and to higher propensity to accept and understand innovation (Becker, 1970) presenting greater cognitive complexity (Hitt & Tyler,
1991). At the same time has been pointed out that higher education level of the CEO influence directly the level of business planning in the small and medium enterprises (Gibson & Cassar, 2002) where the
CEO has more direct influence on the business decisions (Tsui, Zhang, Wang, Xin, & Wu, 2006; Hanges,
Lord, & Dickson, 2001). Equally CEO distinctive demographic characteristics have been associated with a direct influence to stock price and enterprise value (Coughlan & Schmidt, 1985; Palmon & Wald, 2002).
In this direction Reheul & Jorissen (2010) has demonstrated that CEO Education level influence directly the planning, control and financial evaluation systems in the SMEs, and It is therefore logical to hypnotize that, extending the concept.
Has also been proved that MBA curriculums are particularly effective in providing CEO and entrepreneur tools and instruments to avoid big mistakes or losses in the execution of the business (Finkelstein &
Hambrick, 1996; Hambrick & & Mason, 1984). It is therefore logical to suppose therefore that CEOs that
have attained MBA studies have the potential to better grasp the advantages of implementing MCS in their firms.
H2: CEO Education level has a positive influence on the sophistication of the MCS
International Exposure
Recent studies on Chinese SMEs and High Tech firms have shown how returnee entrepreneurs perform better than local Chinese entrepreneurs in the same field and in the same science park or location (Wrigth,
Liu, Buck, & Filatotchev, 2008; Dai & X, 2009) . At the same time other studies show the direct relationship between the international management culture and the Chinese management culture impact on the management control systems (Chalos & O'Connor, 2004; Tang, Gao, & Du, 2006; O’Connor,
Chow, & Wu, 2004; Davila & Foster, 2005) and the relationship between international exposure of the firm and the adoption and timing of the MCS implementation (O’Connor, Vera-Muñoz, & Chan, 2011).
In those studies researchers has measured the international exposure of the firm, both in terms of the market and the competition (O’Connor, Vera-Muñoz, & Chan, 2011) or in terms of shareholder structure, affirming that JV with foreign enterprises have a positive effect on the implementation of the MCS systems (Chalos & O'Connor, 2004). Considering that previous management experiences influence the
CEO performance in the new company (Barker & Mueller, 2002; Davila & Foster, 2005) it is logical to extrapolate that managers that have previous experiences in foreign owned enterprises or in JVs with foreign owned companies will positively influence the MCS practices in the current firm, even if the current firm is a fully owned local company.
Conversely Davila & Foster (2005) have demonstrated the influence of CEO education and belief on the
MCS practices and many scholars have shown that knowledge level does not equal productivity on the workplace if the management, critical thinking and communication skills are not in place (McMullan &
Gillin, 1998; B, 1997; Cosh, 1998). Linked to this researchers have shown that the Chinese school system is mostly based and focused on the preparation of a knowledge-based assessment external examination with a low level of focus on critical thinking, communication skills and management when compared with western modern school practices (Lau, 1996; Sally, 1999; Watkins, 2000). It is therefore possible to conclude that CEOs with more international exposure both on the work experience and the education will have better understanding of the MCS importance and will influence directly the adoption of the MCS practices in the firms they manage.
H3: International work exposure of the CEO and the Firm a positive influence on the sophistication of the
MCS
In particular it is evident from the researches that we can distinguish the CEO Education and the Firm international exposure and its effects on MCS. It is possible to describe H3 as:
H3a : CEO that has worked abroad has a positive influence on the MCS Sophistication
H3b : The firm international exposure has a positive influence on the sophistication of MCS
Shareholder Structure Influence
Previous studies have demonstrated how the different shareholding structure in Joint Ventures can influence the adoption and the extent of use of the MCS (Chalos & O'Connor, 2004), hinting that not only management characteristics but ownership characteristics can influence the MCS. Conversely other studies have revealed how the CEO Ownership of control shares of the company can influence rewarding systems, MCS Choices and Adoption Strategies (Reheul & Jorissen, 2010; Reheul & Jorissen, 2008;
Villalonga & Amit, 2006). Family ownership of the firm has also been linked to different organizational structures and different implementation of the controls (Chu, 2009; Villalonga & Amit, 2006). Founder ownership and management have also been found linked to different firm performances, and control structure and MCS adoption (Davila & Foster, 2005; Jayaraman, Khorana, Nelling, & J., 2000). CEO
Ownership and Family ownership are usually reflected in concentrated ownership structure with majority shareholders having full control on the company. Conversely also the board of director choice will be influenced by the ownership structure, and has been demonstrated in previous researches that the board of director characteristics influence the controls in the firm and the MCS adoption and extent of use (K. &
Kiel, 2004; Westphal & Zajac, 2006).
It is possible to hypothesize that:
H4 : A more concentrated shareholder structure is negatively correlated with MCS Sophistication
3.0 RESEARCH METHOD
3.1 Target Definition
To satisfy the hypothesis testing, I decided to target a recognizable group of companies in the High
Tech Industry in China. In particular I focused in the Beijing Capital area being this area the one in China with the highest concentration of innovation and high technology firms, and being the area site of a large
science park that can allow an official classification of the companies in the specific area of the research.
Also the area allows for the study of the effects of a strong presence of Central Government influence and funding, much higher than in remote areas of the country.
To identify the target population I’ve chosen to limit the study to the companies and firms associated with any of the Science Parks in the Beijing Capital Area that belong to the general Zhongguancun
Science Park or Zhongguancun National Innovation Model Park 1 or easily Z-Park.
The Z-Park is divided in 11 sub parks distributed in the entire area of Beijing covering almost the entire population of High-Tech firms in different industries. The Z-Park comprise today a total of around
20,000 High-Tech companies of different sizes and ages.
Following other former papers on growth stage firms and on SME identification (Davila & Foster, 2007;
Huang, 2009) I applied a 200 Employee number as a threshold limit for the size of the company identifying a target population of 3,962 companies divided in different industries across the park.
3.2 Data Collection
There is no external or third party source available to be used as a source of detailed enough data regarding the MCS design and the reason behind the specific performances of the MCS and the firm characteristics. It has been therefore necessary to proceed with a detailed data collection exercises based on questionnaires and direct interviews with top executives.
I started the study with a series of interviews to top executives and experts in the High Tech Industry in
China and Hong Kong. The interviews were aimed to understand the usage and the characteristics of the
MCS systems and the challenges and typical issues that the management of start-ups and high tech firms encounter in china. I used the results of these interviews both to collect information and contacts regarding potential firms to include in the sample, and to design a questionnaire that would be able to collect all the relevant data needed to perform the research and to validate the hypothesis.
Using the academic sources and published literature (Davila & Foster, 2005; Sandino, 2004) and using the knowledge gained during the interviews, I therefore designed a questionnaire to collect the information. All the data have been collected through direct one-to-one interviews Each interview lasted an average of 2 and half hour with some lasting longer due to the complexity of the company or the interest that the executive had to discuss the topic.
1 The Z-Park founded as the first national science park in 1988 is a conglomerate of different parks in the Beijing Area has a total revenue in
2010 of 600 Billion Yuan equivalent to 1/7 th of the entire China science parks combined revenues put together. The Z-Park is therefore a very representative sample of the Chinese High Tech industry, and the best source of information for a MCS research in this field. Source http://www.zgc.gov.cn/english/AboutZPark/Z-ParkProfile/34441.htm
After the interviews and the data entry into the statistical system, I validated the questionnaire answers both for consistency and for errors. The validation used general data validation rules (i.e. Sum of shareholder distribution percentages should be 100%) and also qualitative data collected during the interview.
All the questionnaires with incomplete or not valid answers have been extracted from the database and went through a process of a second telephone or face-to-face completion with the firm executives, in order to minimize the risk of reducing the sample size.
I’ve chosen 230 firms as initial list for the questionnaires using random samples among the different industries composing the Science park and coupling the approach with the referral given by each of the firm that accepted the interview. After submitting the request for interviews I got the requested appointment with 220 firms, comprising 453 executives’ interviews.
For each of the target sample firms I interviewed at least the CEO or the Founder of the firm. When possible a second interview with the CFO has been scheduled. Being High Tech firms also the R&D
Department head has been interviewed in order to provide a complete picture on the core business of the firm. All the interviewed executives were C-Level roles in the sampled firms.
Requested Interviewed Average
Length
130 min CEOs of which:
- Founders
- Executives
CFOs
R&D Heads
Other C-Levels
230
187
43
135
170
54
Table 1 - Number of Interviews by Level
220
180
40
117
116
34
70 min
85 min
90 min
After conducting the interviews and completing the survey and after the follow up calls and interviews,
179 questionnaires have been completed and data validated.
Total Number of Requested Appointments
Total Number of Actual Interviews
Company Discarded because did not fit the scope
Questionnaire Number
230
220
-12
Questionnaire Discarded because largely incomplete
Questionnaire Discarded after data validation
-15
-14
Valid Questionnaire for the Analysis
Table 2 - Number of Valid Questionnaires
179
A goodness of fit test on the population distribution of the remaining questionnaire and the Z-Park industries distribution has been carried out in order to verify that the distribution of the industries in the sample taken matched the distribution in the technology park. The Chi-squared test resulted to be 𝜒
2
=
12.02
. Considering that the sample has 8 DOFs (9 industries in total) the test shows that ( 𝑃 > 𝜒 2 =
0.8496) the sample of answered questionnaires is not significantly different from the industry population.
3.3 Dependent Variables – The MCS Sophistication measurement
We have seen how many of the studies on MCS focus either on the time to adoption (Davila & Foster,
2005) or on the purpose or extent for using the MCS or single systems in the MCS portfolio (Chenhall,
2003; Duh, Xiao, & Chow, 2009). The interest and focus of this research was though to understand how the Sophistication of the MCS systems is affected by the CEO Demographic, the international exposure and the government intervention and influence.
It has been therefore made necessary to develop an index to measure the MCS Sophistication, based on the questionnaire responses collected and based on the different characteristics of each sub-system of the
MCS. Based on previous researches and literature (Davila & Foster, 2007; Langfield-Smith, 1997) we have taken into consideration 46 MCS sub-systems classified in 8 MCS Groups: Financial Planning, HR
Planning, Strategic Planning, PDM Systems, Sales and Marketing Management, Financial Evaluation,
HR Evaluation, Partnership Management.
Many studies have been carried out on the development of scales and indexes and on the measure of their reliability (Raubenheimer, 2004; Kline, 1998; Diamantopoulos & Winklhofer, 2001; Diamantopoulos,
Riefler, & Roth, 2008), in particular many researchers have focused the attention on the development of reliable indexes using a confirmatory or exploratory factor analysis (Lawley & Maxwell, 1971; Muthen &
Kaplan, 1985; Widaman, 1993; Kim & Mueller, 1978). Conversely it appears clear from many authors
(Hotelling, 1933; Jolliffe, 2002; Mardia & Kent, 1979) that in the case of a questionnaire with numerous variables, the Principal Component Analysis is a tool to obtain a reliable scale with a reduced number of variables that describe a series of uncorrelated linear combination of variables containing the largest possible variance (Rencher, 2002).
A confirmatory Principal Component Factor Analysis has been run on the question answers values of the questionnaire describing the characteristic, use, extent and sophistication of the MCS in the companies, in order to determine how to construct the scale for the MCS sophistication. In order to obtain a psychologically meaningful solution (Thurstone, 1935; Harman, 1976; Gorsuch, 1983) an oblimin rotation has been performed on the factors obtaining the final factorization of the questions. After excluding the factors with eigenvalue smaller than 1, 6 remaining principal factors have been identified and shown in the Table 3.
The 6 identified remaining factor explain 66% of the total variance of the measures, indication that the factors capture a very significant proportion of the variance of the variables. A Cronbach’s alpha analysis
(Chronback, 1951) has then been performed on the factors in order to assess the reliability of a summative rating scale (Likert, 1932) composed by the variables themselves (Bleda & Tobias, 2000).
Table 3: Confirmatory factor analysis of management control systems (
All Aggregated System
N = 179) 32%
9% 8% 7% 5% 5%
Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6
Aggregate Management Control Systems (19 items), (Alpha= 0.95)
Importance of Use (10 items), (Alpha = 0.95)
Describe the importance of the MCS System for the different levels of user within the organization and the ability of the data and results obtained to satisfy the needs of the data demand
Response anchors: 1 – Not at all; 5 – Critical
Response anchors for effort and data quality: 1 = Not At All; 5 = To a great extent
Importance to Functional Managers
Importance to Top Management Team
Importance to CEO
Importance in Product development decisions
Importance in fixed assets investment decisions
Importance in marketing and sale strategy determination
0.810
0.909
0.945
0.916
0.939
0.708
Level of effort put in designing each MCS System considering its importance 0.765
Value gained from the MCS System compared to the time spent
Value gained from the MCS System compared to the effort spent
Current level of Data Demand satisfacion
0.732
0.726
0.760
System Structure (6 items), (Alpha = 0.94)
Please rate the MCS Structure and the participation in the system usage
Response anchors for structure evaluation: 1 = Not At all; 5 = Very Much
Response anchors for participation: 1 = Not at All Involved; 5 = To a large extent
How Detailed is the MCS System
How Descriptive is the MCS System
How Customized is the MCS System
How Updated is the MCS System
C-Level Participation in the system
Department Managers Participation in the system
0.619
0.644
0.731
0.738
0.711
0.618
Data Aggregation (3 items), (Alpha = 0.90)
Describe what is the level of the information collected in terms of aggregation.
Response anchors: 1 – Individual; 2 – Department; 3 – Company; (Inverted Scale
Level of the data collected (Aggregation level)
Level of the data used (Aggregated view level)
Department Managers Participation in the system
0.942
0.947
0.793
System Operations (4 items), (Alpha = 0.85)
Does the Information System and the MCS present the real operational information of the company?
Response anchors for Level of checking: 1- Only information provider; 5 – Information user
Response anchors for the other questions: 1 – Negligible; 5 – Great Amount/Great Extent
Degree of participation of the information supplier
Degree of participation of the information user
Degree of flexibility of the system
Importance of the people doing the data checking in the process of adjusting the system
0.640
0.693
0.785
0.730
Completeness (3items), (Alpha = 0.77)
Degree of completeness of the management control systems, the frequency of usage and alignment with the operation
Multiple Sub-System for each Management Control System
Response anchors for completeness and usage frequency: 1 = Not At All; 5 = Very much
Response anchors for Frequency of align: 2 = Less than 1 Year; 5 = More than 5 Years (Inverted Likert scale)
Completeness of the system
Frequency of use of the system
Frequency of checking that the system is aligned with the operations
0.685
0.639
0.729
Level and Frequency of Information Collection (0 items), (Alpha = 0.79)
Describe the level and frequency of the information collection
Response anchors for levels : 1 – Individual Employee; 3 – Company (Inverted Likert scale)
Response anchors for frequency: 1 - Daily; 6 – annually or longer; (Inverted scale)
At what level the information is supplied ? (Information Supplier level)
At what level the information is used (Information User Level)
Frequency of Information Collection
0.762
0.573
0.653
All the Cronbach’s alpha coefficients and the alpha coefficient of the global summative scale are greater than 0.70 usually regarded as good indication of reliability, with the global summative alpha equal to 0.95 that Nunnally&Beirstein (1994) consider as a desirable high standard to reduce the standard error of measurement of the test scores even when considering the individual observations in the sample.
A MCS Sophistication Index has therefore been created using the standardized sum of all the components identified in the factor analysis. The same operation has been repeated for each of the single
MCS Systems in order to obtain a MCS Sophistication index for each of the single system in the MCS landscape.
Table 4: Descriptive statistics: Dependent (MCS) variables ( N = 179)
Standardized Values
Mean Std.Dev.
Financial Planning System
MCS Sophistication [Aggregate]
System Structure
Importance of Use
Data Aggregation
System Operations
Completeness
Level and frequency of info collection
HR Planning System
MCS Sophistication [Aggregate]
System Structure
Importance of Use
Data Aggregation
System Operations
Completeness
Level and frequency of info collection
Strategic Planning System
MCS Sophistication [Aggregate]
System Structure
Importance of Use
Data Aggregation
System Operations
Completeness
Level and frequency of info collection
Product Development System
MCS Sophistication [Aggregate]
System Structure
Importance of Use
Data Aggregation
System Operations
Completeness
Level and frequency of info collection
Sales and Marketing Management System
MCS Sophistication [Aggregate]
System Structure
Importance of Use
Data Aggregation
System Operations
Completeness
Level and frequency of info collection
11.71
2.17
2.37
1.24
2.02
2.22
1.69
11.63
2.07
2.40
1.24
1.90
2.03
2.00
12.14
2.37
2.01
1.24
2.09
2.28
2.15
11.99
2.21
2.26
1.26
1.90
2.29
2.07
11.80
2.11
2.38
1.23
1.89
2.26
1.93
2.86
0.71
0.68
1.40
0.52
0.59
0.46
2.97
0.75
0.70
1.45
0.52
0.67
0.53
2.77
0.77
0.55
1.43
0.56
0.52
0.53
2.61
0.65
0.66
1.39
0.48
0.45
0.44
2.66
0.69
0.67
1.38
0.52
0.54
0.41
Thoretical
Range
0-30
0-5
0-5
0-5
0-5
0-5
0-5
0-30
0-5
0-5
0-5
0-5
0-5
0-5
0-30
0-5
0-5
0-5
0-5
0-5
0-5
0-30
0-5
0-5
0-5
0-5
0-5
0-5
0-30
0-5
0-5
0-5
0-5
0-5
0-5
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Max
17.72
3.72
2.81
3.98
3.28
3.31
3.04
16.20
3.31
3.24
3.93
2.84
2.77
2.76
16.63
3.43
3.31
3.92
2.97
3.01
2.45
17.15
3.33
3.36
3.95
3.29
3.16
2.86
18.74
3.79
3.31
3.97
3.36
3.43
2.96
Financial Evaluation System
MCS Sophistication [Aggregate]
System Structure
Importance of Use
Data Aggregation
System Operations
Completeness
Level and frequency of info collection
HR Evaluation System
MCS Sophistication [Aggregate]
System Structure
Importance of Use
Data Aggregation
System Operations
Completeness
Level and frequency of info collection
Partnership Management System
MCS Sophistication [Aggregate]
System Structure
Importance of Use
Data Aggregation
System Operations
Completeness
Level and frequency of info collection
All Aggregated System
MCS Sophistication [Aggregate]
System Structure
Importance of Use
Data Aggregation
System Operations
Completeness
Level and frequency of info collection
0-30
0-5
0-5
0-5
0-5
0-5
0-5
0-30
0-5
0-5
0-5
0-5
0-5
0-5
0-30
0-5
0-5
0-5
0-5
0-5
0-5
0-30
0-5
0-5
0-5
0-5
0-5
0-5
3.00
0.72
0.55
1.40
0.53
0.70
0.84
2.68
0.66
0.60
1.40
0.48
0.49
0.42
2.67
0.67
0.62
1.40
0.52
0.57
0.47
2.81
0.70
0.68
1.49
0.54
0.52
0.51
12.28
2.32
1.90
1.22
1.81
2.43
2.60
11.91
2.21
2.22
1.25
1.95
2.23
2.06
11.90
2.20
2.24
1.24
1.98
2.24
1.99
12.34
2.22
2.42
1.31
2.03
2.19
2.18
A test for normality of the MCS Sophistication Index shows kurtosis = 5.9 and skeweness = -1.43 that are acceptable value to allow the use of the index in the linear multivariate regression.
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
18.14
3.66
2.75
3.87
2.81
3.33
3.87
17.06
3.43
3.02
3.80
2.74
2.99
2.93
17.17
3.40
3.05
4.29
3.46
3.27
2.84
17.86
3.46
3.49
4.00
3.48
3.01
3.14
3.4 Independent Variables Measures
All the independent and control variables has been collected directly from the questionnaires and from the responses of the interviewees. When possible, as for example for the number of employees, the answers of the questionnaires were checked against official records or documents of the company in order to verify the reliability of the answers.
CEO Total Management Experience ( CEOYTOTM): Following previous studies (Davila & Foster, 2005) the total management experience has been calculated as the number of years of management experience the CEO has, including the tenure in the current company. This measure is derived directly from the questionnaire and the data collected during the interview.
CEO Experience Outside Mainland China (CEOABR): Slater & Dixon-Fowler (2009) in their study on the CEO International Assignments use a dummy variable to identify if the CEO has or not a significant
international assignment experience. Similarly other studies (Sambharyam, 1996; Hermann, 2002;
Hermann & Datta, 2005; Reuber & Fischer, 1997) used dummy variables to identify whether the CEO or the Top Management Team has a prevalent Foreign or Domestic management experience. Following the latter examples CEOABR is a dummy variable that assumes the value of 1 if the CEO has a prevalent foreign management experience and 0 if the CEO has a prevalent Mainland China management experience.
CEO Previous Management Experience (CEOPREVMAN) : is a categorical variable assuming values from 0 to 6 depending on the previous management experience the CEO had before joining the current company. Using similar approach of former researches (Wasserman, 2003; Stuart & Abetti, 1990; Reuber
& Fischer, 1994) I assigned higher value on the scale for higher positions held in previous companies. In particular the variable assumes the following values: 0- No previous Management Experience, 1- Junior manager, 2- Managers, 3- Senior Management, 4- General Manager, 5- CEO, 6-Member of the
Board/Director.
CEO Educational Level (CEOEDULEVEL): Similarly to other previous studies (Barker & Mueller, 2002;
Daellenbach, McCarthy, & Schoenecker, 1999) the education level of the CEO has been measured on a 4 points scale where 0- High School, 1- University, 2- Master, 3-Phd. At the same time we have measured if the CEO Has obtained a MBA (CEOMBADUMMY) . This last one is a dummy variable that assumes the value 1 in case the CEO has obtained a MBA either in China or Abroad.
Company International Orientation Index (INTINDEX ): In order to measure the International Orientation index of a firm previous researches has determined that corresponds on how the firm depends on foreign markets for customers and production factors (O’Connor, Vera-Muñoz, & Chan, 2011; Hamel & Prahalad,
1994; Sullivan, 1994; Weick & VanOrden, 1990). The questionnaire collected 3 measures on the
International orientation that are consistent with previous researches. The Percentage of Export compared to the total revenues of the firm, The percentage of the International Customers within the 10 top customers of the firm and the importance that the Top Management attributes to the export, measured on a 5 points Likert scale. The INTINDEX is then calculated as the product of the 3 measures.
Company Years of International Exposure (INTCUSTSTART): Measures the number of years since the company has obtained/sold to its first international customer. The measure is calculated subtracting the year of first sale to an international customer from 2011, the year of the survey.
Shareholder concentration Index (SHAHHI) : Measure the Shareholder Concentration as the Herfindahl–
Hirschman Index on the shareholder structure under the consideration, also made in previous researches
(Wasserman, 2003) that, in the case of startup or early stage companies, more concentrated ownerships tend to be an indication of the correspondence of CEO to the Founder (Davila & Foster, 2007) and that a concentrated ownership will require less sophisticated controls and therefore less sophisticated MCS
(Chenhall, 2003). Many studies in different fields have used the Herfindahl–Hirschman Index in order to measure shareholder concentration and its effect on different aspects of the company with a broad spectrum of usage. (Anderson, Fornell, & Mazvancheryl, 2004; Cubbin & Leech, 1983; Barnea & Rubin,
2010; Dougherty & Herd, 2005; Small, Smith, & Yildrim, 2007)
3.5 Statistical Control Variables
In addition to the previous variables that are predicted to affect the MCS Sophistication some additional statistical control variables have been measured. Previous studies have correlated some of the statistical control variables either to the adoption or to the extensiveness of use of the MCS, and some of the control variables can represent proxies for the IVs. For example TOPMANRAT , The ratio of the Top Managers compared to the total number of employees have been used in previous studies as an inverse proxy of the
CEO Experience. Conversely the size of the organization, measured in terms of the total number of employees ( TOTNUMEMP ) has been correlated to the extent and adoption of MCS (Chenhall, 2003;
Davila & Foster, 2005) showing how larger enterprises have more means and infrastructure to support a wider use of MCS, with a positive correlation between the number of employees and the sophistication of the controls (Bruns & Waterhouse, 1975). Opposite the case for the R&D intensity index ( RDINTENS ), measured as the number of R&D employees over the total number of employees, that has been negatively correlated with the adoption and extent of use of MCS (Davila & Foster, 2005).
Following the example of other previous studies and researches, I also included the age of the company, measured as the number of years the company is in operation (COMPAGE) as a control variable.
3.6 Test for Normality and descriptive statistics of the Independent and Control Variables
A Test for normality has then been run on all the Independent Variable and Control Variables measures and a LN (Natural Logarithm) transformation has been applied to all the variable that were not normally distributed.
Table 5: Descriptive Statistics for Independent and Control Variables (N=179)
Mean Std.Dev Range Min Max Kurtosis Skew
Independent Variables
CEOYTOTM
CEOABR
CEOPREVMAN
CEOEDULEVEL
CEOMBADUMMY
INTINDEX
INTCUSTSTART
SHAHHI
Control Variables
TOPMANRAT
COMPAGE
TOTNUMEMP
RDINTENS
3.422 5.662 0-40
0.290 0.451
2.558
1.391 0.628
0.363 0.481
34.745 53.261 0-325
11.178 7.642 0-20
2.394 8.378 0-100
1.532
0.086 0.099
9.837 5.816 0-40
133.32 243.27 0
0.113 0.161
0-1
0-6
0-4
0-1
0-1
0-1
0
0
0
0
0
0
0
0
0
0
0
0
36
1
6
3
1
300
10.73
5.93
1.80
3.20
1.32
7.17
20 1.62
66.67 37.79
0.94
40
33.71
11.48
2140 52.91
0.59 3.22
It is evident in the Table 5 that TOPMANRAT, COMPAGE, TOTNUMEMP, CEOYTOTM and SHAHHI are not fitting the normal distribution. A log transformation has therefore been performed.
2.59
-2.22
0.04
0.68
0.56
2.15
-0.62
5.55
4.50
0.51
6.61
1.22
Table 6: Descriptive Statistics for Independent and Control Variables after LN transformation (N=179)
CEOYTOTM-LN
SHAHHI-LN
TOPMANRAT-LN
COMPAGE-LN
TOTNUMEMP-LN
Mean Std.Dev
0.754 0.973
0.349 0.876
-2.667
2.157
4.285
0.997
0.585
1.093
Min
0
0
-4.60
0
0
Max
3.58
4.19
0
3.68
7.66
Kurtosis
2.92
9.26
3.66
6.79
4.24
Skew
1.06
2.64
0.51
-1.28
-0.26
After the transformation, as reported in the Table 6, all the Independent and Control Variables fit with the criteria of a normal distribution.
The Table 7 describes the Pearson Correlation coefficient and the significance in a pairwise correlation of the independent and control variables with the MCS Sophistication Index values for the Single MCS system and the aggregate MCS Sophistication score. The tables show the correlation with the single MCS subsystems and the correlation with the single factors component of the aggregated MCS Sophistication
Index.
Table 7: Pairwise Pearson correlation statistics of the dependent (MCS) and independent variables ( N = 179)
SUB SYSTEM STANDARDIZED CORRELATIONS
All Aggregated Systems Management Control Variables Independent Variables Control Variables
Variables
1. Aggregated MCS Sophistication
2. Financial Planning
3. HR Planning
4. Strategic Planning
5. PDM Systems
6. Sales and Marketing
7. Financial Evaluation
8. HR Evaluation
9. Partnership Management
10. CEOYTOTM
11. CEOABR
12. CEOPREVMAN
13. CEOEDULEVEL
14. CEOMBADUMMY
15. INTINDEX
16. INTCUSTSTART
17. SHAHHI
18. TOPMANRAT
19. COMPAGE
20. TOTNUMEMP
21. RDINTENS
(1) (2) (3) (4) (5)
Significance levels: ‡ p < 0.01, † p < 0.05, * p < 0.10 (two-tailed).
(6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20)
0.96‡
0.98‡ 0.96‡
0.98‡ 0.96‡ 0.96‡
0.92‡ 0.83‡ 0.86‡ 0.87‡
0.97‡ 0.90‡ 0.92‡ 0.93‡ 0.94‡
0.98‡ 0.95‡ 0.96‡ 0.97‡ 0.86‡ 0.92‡
0.97‡ 0.94‡ 0.95‡ 0.95‡ 0.85‡ 0.92‡ 0.97‡
0.94‡ 0.91‡ 0.92‡ 0.92‡ 0.80‡ 0.87‡ 0.92‡ 0.93‡
0.10
0.11
0.07
0.10
0.10
0.09
0.09
0.12
0.09
0.10
0.15† 0.15† 0.15† -0.03
0.07
0.12
0.10
0.11
-0.05
-0.09
-0.09
-0.12
-0.10
-0.07
-0.07
-0.09
-0.06
-0.08
0.24‡
-0.14*
0.04
0.09
0.05
0.06
-0.03
0.00
0.05
0.06
0.05
-0.01
0.11
0.12
0.05
0.13* 0.11
0.08
-0.08
0.00
0.07
0.06
0.08
-0.09
0.34‡
0.01
0.42‡
0.14* 0.12
0.11
0.13* 0.16† 0.16† 0.12
0.12
0.13* -0.01
-0.04
-0.12
-0.01
-0.05
0.30‡ 0.26‡ 0.33‡ 0.31‡ 0.25‡ 0.30‡ 0.29‡ 0.28‡ 0.33‡ -0.28‡
-0.04
-0.15* -0.10
-0.07
0.17†
-0.42‡ -0.40‡ -0.42‡ -0.42‡ -0.33‡ -0.40‡ -0.41‡ -0.43‡ -0.45‡ -0.03
-0.05
-0.03
-0.01
-0.12
0.02
-0.23‡
-0.24‡ -0.21‡ -0.21‡ -0.24‡ -0.22‡ -0.24‡ -0.24‡ -0.27‡ -0.23‡ -0.20†
0.01
-0.12
-0.02
0.08
0.00
0.10
0.19†
0.01
0.01
0.01
-0.01
0.04
0.01
-0.01
0.01
-0.01
0.04
-0.15* -0.04
0.10
0.09
0.05
0.01
0.03
0.02
0.19† 0.17† 0.18† 0.16† 0.18† 0.19† 0.18† 0.19† 0.17†
0.02
0.05
-0.07
-0.06
0.02
0.13* 0.10
-0.05
-0.18†
0.09
-0.25‡ -0.21‡ -0.30‡ -0.24‡ -0.15† -0.22‡ -0.26‡ -0.25‡ -0.30‡ 0.31‡ -0.18† 0.22‡ 0.18† -0.13* 0.10
-0.45‡ 0.44‡ -0.03
0.07
-0.05
(21)
Table 8: Pairwise Pearson correlation statistics of the dependent (MCS) and independent variables ( N = 179)
STANDARDIZED ITEMS
All Aggregated Systems Management Control Variables Independent Variables Control Variables
Variables
1. System MCS Sophistication
2. Importance of Use
3. System Structure
4. Data Aggregation
5. System Operations
6. Completeness
7. Data Collection
8. CEOYTOTM
9. CEOABR
10. CEOPREVMAN
11. CEOEDULEVEL
12. CEOMBADUMMY
13. INTINDEX
14. INTCUSTSTART
15. SHAHHI
16. TOPMANRAT
17. COMPAGE
18. TOTNUMEMP
19. RDINTENS
(1) (2) (3) (4) (5)
Significance levels: ‡ p < 0.01, † p < 0.05, * p < 0.10 (two-tailed).
(6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18)
0.73‡
0.70‡ 0.64‡
0.68‡ 0.17† 0.20‡
0.65‡ 0.48‡ 0.36‡ 0.27‡
0.53‡ 0.53‡ 0.34‡ 0.04
0.34‡
0.59‡ 0.50‡ 0.49‡
0.07
0.48‡ 0.39‡
0.10
0.06
0.01
0.13* 0.02
0.13* -0.07
0.10
-0.10
-0.02
0.25‡
0.08
-0.08
0.00
-0.05
-0.09
-0.05
-0.04
-0.13* -0.06
0.14* -0.09
0.24‡ -0.14*
0.04
-0.01
-0.02
0.04
0.14* 0.06
-0.08
-0.01
0.11
0.05
-0.02
0.04
0.09
0.12
0.08
-0.07
0.00
-0.09
0.34‡ 0.01
0.42‡
0.14* 0.13* 0.09
0.08
0.09
0.04
0.14* -0.01
-0.04
-0.12
-0.01
-0.05
0.30‡ 0.30‡ 0.31‡ 0.10
0.23‡ 0.15† 0.23‡ -0.28‡ -0.04
-0.15* -0.10
-0.07
0.17†
-0.42‡ -0.38‡ -0.40‡
-0.06
-0.32‡ -0.46‡ -0.41‡
-0.03
-0.05
-0.03
-0.01
-0.12
0.02
-0.23‡
-0.24‡ -0.21‡ -0.16† -0.11
-0.14* -0.25‡ -0.15† -0.20† 0.01
-0.12
-0.02
0.08
0.00
0.10
0.19†
0.01
-0.02
0.03
0.05
0.00
0.00
-0.14* 0.04
-0.15* -0.04
0.10
0.09
0.05
0.01
0.03
0.02
0.19† 0.07
0.09
0.16† 0.13* 0.07
0.16† 0.02
0.05
-0.07
-0.06
0.02
0.13* 0.10
-0.05
-0.18† 0.09
-0.25‡ -0.22‡ -0.24‡
0.00
-0.23‡ -0.20† -0.38‡ 0.31‡ -0.18† 0.22‡ 0.18†
-0.13* 0.10
-0.45‡ 0.44‡
-0.03
0.07
-0.05
(19) (20)
3.7 Factor Analysis of the Independent Variables
In order to group the independent variable and to verify the hypothesis, similarly to the creation of the
MCS Sophistication index, a confirmatory Principal Component Factor Analysis has been run on the IVs, with the scope of supporting the hypothesis definition.
Table 9: Factor Analysis on the Independent Variables ( N=179 )
Factor 1 Factor 2 Factor 3 Factor 4 Factor 5
H1: CEO Work Experience is positively associated with the MCS Sophistication
CEOYTOTM 0.813
CEOPREVMAN 0.667
H2: CEO Education level has a positive influence on the sophistication of the MCS
CEOEDULEVEL 0.776
CEOMBADUMMY 0.846
H3: The firm and the CEO international exposure has a positive influence on the sophistication of MCS
CEOABR
INTINDEX
INTCUSTSTART
0.852
0.388
0.593
H4: A more concentrated shareholder structure is negatively correlated with MCS Sophistication
SHAHHI 0.757
Because the factor analysis has confirmed that the variable groups according to the hypothesis, it is possible to create additive indexes to represent each of the hypotheses in the linear regression. The variables H1, H2, H3, H4, has been created as additive and standardized sum of the variables included in the factor analysis, substituting the variables with their Natural Logarithms in the cases indicated above in the table 6.
In order to test the regression also separating the CEO Interational working experience ( CEOABR ) with the Firm International Exposure and test the Hypothesis H3a and H3b another variable describing the
Firm International Exposure has been created. We will define therefore also H3a = CEOABR and H3b as the standardized additive sum of INTINDEX and INTCUSTSTART
Table 10: Descriptive Statistics for Additive Independent Variable (H1-H4) ( N=179 )
Mean Std.Dev Min Max Kurtosis Skew
H1 1.656 0.992 0 3.99 2.27 0.31
H2
H3
H4
1.240
2.577
0.349
0.899
1.038
0.876
0
0
0
3.00
4.50
4.19
1.61
3.42
9.26
0.54
-0.92
2.64
All the additive standardized variables H1, H2, H3 and H4 are compatible with the assumption of a normal distribution.
4. EMPIRICAL MODEL AND RESULTS
I estimated the following OLS regression model to test the hypothesis described:
TMCSOPH i,s
= β
0
+ β
1
H1 i,s
+ β
2
H2 i,s
+ β
3
H3 i,s
+ β
4
H4 i,s
+
Β
5
COMPAGE-LN
i
+ β
6
RDINTENS
i
+ β
7
SHAHHI-LN
i
+ β
8
TOPMANRAT-LN
i
+ β
9
TOTNUMEMP-LN
i
+
i,s
Where TMCSOPH
i,s
represent the standardized value of the MCS Sophistication Index for the item ( i) for the MCS Group ( s) considering the 8 MCS Groups: Financial Planning, HR Planning, Strategic Planning,
PDM Systems, Sales and Marketing Management, Financial Evaluation, HR Evaluation, Partnership
Management. (Davila & Foster, 2005; Davila & Foster, 2007; Langfield-Smith, 1997)
TMCSOPH i
= β
0
+ β
1
H1 i
+ β
2
H2 i
+ β
3
H3 i
+
β
4
H4 i
+
Β
5
COMPAGE-LN
i
+ β
6
RDINTENS
i
+ β
7
SHAHHI-LN
i
+ β
8
TOPMANRAT-LN
i
+ β
9
TOTNUMEMP-LN
i
+
i,s
For the aggregated value of the MCS Sophistication Index for all the MCS System aggregated together.
In order to separate the CEO International Working Experience and the Firm International working experience we will also test the following model (2)
TMCSOPH i
= β
0
+ β
1
H1 i
+ β
2
H2 i
+ β
3
CEOABR i
+ β
4
H3b i
+ β
5
H4 i
+
Β
5
COMPAGE-LN
i
+ β
6
RDINTENS
i
+ β
7
SHAHHI-LN
i
+ β
8
TOPMANRAT-LN
i
+ β
9
TOTNUMEMP-LN
i
+
i,s
Table 11: Ordinary least squares (OLS) regressions (
N
= 1432) - Standardized Factors
ALL SYSTEMS AGGREGATED
TMCSOPH i
= β
0
+ β
1
H1 i
`
+ β
2
H2 i
+ β
3
H3a
i
+ β
4
H3b
i
+ β
5
H3c
i
+ β
6
H4
i
+
Β
7
COMPAGE-LN
i
+ β
8
RDINTENS
i
+ β
9
SHAHHI-LN
i
+ β
10
TOPMANRAT-LN
i
+ β
11
TOTNUMEMP-LN
i
+
i,s
Standardized
Values
ALL SYSTEMS
Model (1)
Aggregate MCS
Model (2)
Aggregate MCS
Coefficient t-statistic Coefficient t-statistic
H1
H2
H3
CEOABR
H3b
H4
COMPAGE-LN
+
+
0.074
0.210
1.07
2.97
‡
13.49
‡
0.051
0.200
0.73
2.82
‡
+
+
+
-
0.894
-0.708 -8.09
‡
0.615
0.748
-0.685
2.05
†
7.71
‡
-7.77
‡
0.042 0.40 0.051 0.48
RDINTENS
TOPMANRAT-LN
TOTNUMEMP-LN
INTERCEPT
-0.695
-0.450
0.002
-1.41
-6.56
7.64
‡
‡
-0.676
-0.430
0.001
-1.37
-6.21
7.43
‡
‡
8.057 21.33 7.979 21.04
Model F-Statistic
R 2
Adjusted R
2
71.53
‡
0.2868
64.19
‡
0.2889
0.2828 0.2844 a
Significance levels: ‡ p
< 0.01, † p < 0.05, * p < 0.10 (two-tailed). Significance tests are conducted using Huber-
White robust standard errors that are adjusted for heteroskedasticity as well as industry-specific clustering. The variance inflation factors ( VIF ) is equal to 1.27 in both regressions with single variable VIF ranging from 1.03 to
1.62 in the first regression and 1.04 to 1.65 in the second regression.
The Table 11 shows the regression for the TMCSOPH of the aggregated systems, considering two possible OLS regression, the first column with he interaction of the H3 terms altogether, and the second column with the separate interaction of the logical component of H3 : The CEO International working exposure and the Firm International Exposure and Orientation . As can be seen from the table the model in which we consider the H3, instead of the single terms, is better specified with a F statistics much higher than the model in which the single terms are specified separately. Further investigation on the single subsystems will be done using the first model (1). Studying the multicollinearity between the variables shows
a VIF (Variance Inflation Factor) in both cases ranging from 1.03 to 1.65 far below the limits threshold
(Belsley, 1991) concluding that the two models are not affected by multicollinearity problems. Residuals in both models range from -9.29 to 5.55 with Kurtosis of 3.9 and Skewness of respectively -0.56 and -
0.63. Residuals are normally distributed. A calculation of the Cook’s Distance of the data points shows a mean Cook’s Distance equal to 0.00097 with a standard deviation of 0.0030 with data points having a max D<0.056 in the first model and D<0.057 in the second model, far below the threshold of 1, indicating that there is no actual outliner in the data points of the regression model (Cook & Weisbler, 1982; Belsley,
Khu, & Welsch, 1980).
H1 predicts that in the context of Small and Medium Enterprises the CEO work experience is positively associated with the MCS Sophistication. As shown in the Table 11, H1 is positive in both regression models with the terms of H3 considered together ( t=1.07; p>0.1) and with the terms considered separately (t=0.73; p>0.1) . In both cases, even if the coefficients show a positive relationship, the statistical results are not significant. The results therefore suggest a positive relationship but the statistical analysis DO NOT support H1.
H2 predicts that the CEO education level has a positive influence on the MCS Sophistication. As shown in the Table 11 the term is positively associated with the MCS Sophistication in both regressions models, specifically in the first model ( t=2.97, p<0.01
) and with the Government Influence term (t=2.82, p<0.01) .
In both cases the t-statistics show a strong significance. These results support H2.
H3 predicts that the firm and the CEO international exposure have a positive influence on the MCS
Sophistication in the examined context of firms. The two models test H3 together and with separate terms for the CEO International working exposure and the Firm International Exposure and orientation. In Both model the relationship are significant, and the sign of the relation is positive in both models. In particular the first model present a very high t-statistics ( t=13.49, p<0.01
) while the second model present for both variables a significant relationship ( CEOABR t=2.05, p<0.05
and H3b t=7.71, p<0.01
). It is significant to notice that the effect of the CEO Experience Abroad and the Firm International Exposure are intensified when the variables are considered together. The empirical model supports H3.
H4 Predicts that more concentrated shareholding structures have a negative impact on the MCS
Sophistication. In both regression the relationship is statistically significant, in particular in the first model, with the H3 terms considered together, we have ( t=-8.09, p<0.01
) and in the second we have ( t=-
7.77, p<0.01
). The results support H4.
5. DISCUSSION AND CONCLUSIONS
5.1 Limitations of this study
As mentioned during the description of the Research Methods, this study followed best practices in the preparation, development and pre-test and small sample review of the survey questionnaire, as well as in the process used to collect the information and to select the executives and the respondent to each of the interviews. For example, responses from different senior managers within each firm has been collected and compared in order to reduce common method bias, and the survey has been conducted during a formal interview where the extensiveness of the answer allowed the interviewed to clarify questions and to filter out biases. Nevertheless, this study is obviously subject to the common limitations of survey-based research, including the validity and reliability of items and tests. In particular we have pose specific attention in constructing the focal measure of the dependent variable (MCS Sophistication) based on a number of questions that required subjective answers from the interviewees, and we have tried to clearly explain the meaning of each of the terms in the questions. Nevertheless terms such as
“Completeness” or “Descriptive” or “Customized” are based on the perception of the respondent, and the divergence between the meaning for the respondent and the interviewee, could have weakened the operationalization of the dependent variable, and thus, represents an internal validity threat to this study.
Other limitations of this study point the path for further investigations and researches to be carried out in order to get a better understanding of the underlining relationship between some of the hypothesis and the MCS Sophistication. In particular we have put in evidence the intensifying effect in the empirical model of considering the CEO Working Experience abroad and the Firm international exposure together. Further studies should concentrate in understanding if a direct relationship exist between the firms with an extensive international exposure and the CEO previous working experience abroad. In particular should be clarified if firms that have international exposure and orientation attract
CEO with previous foreign working experience, or vice-versa if CEO with previous working experience affects the orientation of the firms, and how these two factors combine together in affecting the MCS.
Conversely further investigation should be carried out on the apparently non significant relationship between previous management experience and the MCS Sophistication. We feel that further measure should be carried out to better understand the classification of previous experience based on different factors, among which it could be interesting to investigate if the ownership structure of the previous firms have an impact on the CEO influence on the MCSs. A scale should be developed to measure the validity of the classification of the firm by ownership structure. A better classification of the previous management experiences can derive from similar studies taking into account functional experiences
(Barker & Mueller, 2002) or firm size (Chenhall, 2003) or other key characteristics of the firms where the
CEO has worked before.
Another theme for further studies should be the analysis on how the top management structure and especially the Top Management ratio is affected and related to the CEO Characteristics and can be considered as an inverse proxy to measure the CEO Experience. We have noticed that heavy top management structure correspond to a reduced MCS Sophistication. Further researches should be conducted to verify if the impact is due to the relationship with the CEO Experience or to the fact that large top management structure tend to compensate formal systems with informal communications between the managers.
In addition following the example of other studies (Davila & Foster, 2007) it may also be interesting to investigate the relationship of the hypothesis with the single MCS sub-system, and if the relationships are maintained at the single sub-system and MCS group.
Also further studies should be focused in understanding the impact of the Government
Intervention on MCS Systems and how the government intervention in the firms considered affects the
MCS and if there is any relationship between the CEO Characteristics and the access to Government
Intervention and how these two factors combine to influence the MCS Sophistication in the firm.
This study moreover focuses on firms in the Beijing technological park, that we have considered to be a good representative sample of the High Tech industry in China. It should be focus of further research also the extension of the study to other Chinese areas in order to understand if local characteristic will influence the relationships between the hypothesis and the MCS Sophistication. We have conducted a cluster analysis on the different industries in the sample chosen and we have deducted that there are not significant difference in the industries considered in the Technological Park, but the Technological park provides an infrastructure for homogeneity that can justify the absence of differences among the different industries. Further studies should focus on the understanding if different firms operating in different industries outside the Technological Park present more clusterization and significant difference in how the hypothesis affect the MCS Sophistication.
5.2 Conclusions
Considering the previous explained limitation, using a set of around 200 questionnaires collected in direct interviews with Top Executives in High-Tech firms in the Beijing Industrial Park, the study has identified some of CEO Demographic and Firm Ownership structure factors that influence the level of
Sophistication of the MCS. In particular we have put in evidence how apparently previous CEO
management experience has no statistical significant impact on the MCS Sophistication, while the CEO working experience abroad has a strong influence on the level of sophistication of the MCS. In general the International Exposure either of the firm and or the CEO seems to have a significant effect on the
MCS Sophistication. This is supported from previous researches, especially focused on the Chinese environment (Chalos & O'Connor, 2004; O’Connor, Vera-Muñoz, & Chan, 2011; Duh, Xiao, & Chow,
2009) and in some of the interviews conducted before and during the study. The Appendix A offer some illustrative quotes from the interviews supporting the empirical findings. Also supported from some of the interviews, we have investigated if an heavy top management structure is somehow linked to the CEO
Experience and to the MCS Sophistication. Using the Top Management ratio as a control variable the study shows a significant inverse relationship between the top management ratio and the MCS
Sophistication.
Also supported by previous studies, we have demonstrated how the CEO Education level has a positive impact on the MCS Sophistication. At the same time we have investigated how the shareholding concentration reflects on the MCS Sophistication, under the hypothesis that concentrated shareholding structure correspond to more founder-entrepreneurial initiatives and to simpler reporting and control requirements on the MCS. We have found empirical support for this hypothesis showing how the concentration of the shareholding structure negatively impacts the MCS Sophistication index.
APPENDIX A : SELECTED QUOTES FROM THE INTERVIEWS
1
N.
2
3
4
5
6
Area/Hypothesis
International Exposure and link to
Firm Orientation
International Exposure and
Education
International Exposure and MCS
Impact
Previous work experience
Founder influence
Management Ratio and MCS
Quote
“Returnees are not in touch with the local situation, so they are not suitable for the local market, but they are best suitable for dealing with overseas [‘International’] markets... Returnees are good because they have the sense of entrepreneurship: the US ecosystem is very entrepreneurial / clarity on ethical issues / understands the value of smart money / understanding the value of
MCS and controls”
“The Chinese education systems is very much different from that of the US, it is much more memorization and spoon-feeding way of education. The Chinese education system does not encourage problem solving, so the people that come from this systems may be dealing with the old problems well but don’t know how to solve new problems.”
For instance, in Germany, the development of SME is a very complicated concept. German has three systems to help SME financing their business and support their development…. their entrepreneurial business pass is very clear. They have a group of people following the development of an entrepreneur and evaluating their integrity and reputation. People are used to reporting and control, and take advantages from these practices.
Here in China there is no short term practical advantage”
“Interviewee: If somebody that has worked in startups before could come in and point out the problem under the current situation, which would be really good.”
“In mature firms, the career pass is clearer; here in a small firm the shareholders are crucial; The Founder previous working experience [Travel Business] has been essential. He controls everything and he is the man in charge”
“First, the staffs have to be able to see the problems and;
Then it is their way of fixing the problem. Some of the managers here put “a big bandage” on it and they do not actually go inside and fix the problem from its root. The same with reporting. We have many managers and Not everybody have to be excellent because their responsibilities are different.”
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