This study used quantitative approach as a means to measure the effect of independent variables towards dependent variables. This chapter focused on the research methodology which comprised of five sections, specifically research framework, research procedure, data collection method, instrumentation, and data analysis method.
Research Framework
Based on literature review, a model was proposed. The model was based on Team Effectiveness Model by Robbins and Judge (2013) and Survival and Competitiveness of the Enterprise Intellectual Capital Model by Lin (2009). Team Effectiveness, Organizational Performance, Organizational Survival and Competitiveness (TEPS) Model as shown in figure 3.1 was developed by Cheng-Ping Shih and Dian Utami Putri. TEPS model served as the research framework for this study.
Figure 3.1 TEPS model, developed by Cheng-Ping Shih and Dian Utami Putri.
Team
- Employees with key knowledge - Employees learning and update speed
Organization - Innovation capability
- Flexible organizational structure - Dynamic organization
- Excellent and distinctive culture and value system
Environment - Adapt to environmental capacity - Good working environment - Good relationships of consumers,
suppliers and partners
H1 H2
H3
H4
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Based on research questions, literature review, and research framework, the following null-hypotheses were formulated as follows:
H1: Team characteristics have no effect on team effectiveness.
H2: Team effectiveness has no effect on organizational performance.
H3: Team effectiveness has no effect on organizational survival and competitiveness.
H4: Organizational performance has no effect on organizational survival and competitiveness.
Research Procedure
This study was conducted by following the subsequent research process:
Figure 3.2 Research process.
Conduct Pilot Study Review of Instrument
Data Collection
Data Analysis Data Coding
Conclusion and Suggestions Proposal Meeting
Identify Research Questions and Hypothesis Review of Literature
Develop Theoretical Framework Research Motivation
Develop Instrument Identify Problems
Develop Research Method
Translation and Expert Review of Instrument
•
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Data Collection Method
As this study aimed to observe the relationship and the effects of team effectiveness on organizational performance as well as on organizational survival and competitiveness, a quantitative approach was perceived as the appropriate methodology. Compare to other social research techniques, experimental research offered the strongest tests of causal relationships (Neuman, 2011). Using empirical data and analyzing it statistically, experiments provided focused tests of hypotheses, that in one study, it might examine many variables simultaneously in a diverse range of social settings. Thus, it was seen as an appropriate approach to utilize quantitative method for the purposes of this study.
Participant
The target population of this study was employees of YCAB Foundation. Consent and permission to conduct data gathering and analysis were granted by YCAB Foundation for this study. The instrument was initially undergone pilot test on a group of 30 employees who have worked actively within teams in YCAB Foundation. Once the instrument was revised and validated, it was utilized to collect data for the main investigation.
Population
It was reported in 2012 in their website that YCAB Foundation had 229 staff members on payroll. Researcher sent the questionnaires to every member. Paper questionnaire was used to gather data from staffs in Jakarta. Respondents were then being notified and personally approached by the researcher, with the help of Human Capital Department, to fill out the questionnaires. This method was chosen as due to the certainty of high return rate. On other hand, for staffs outside Jakarta, online questionnaire was utilized.
Respondents were also personally contacted with the help of Human Capital Department to follow them up. This method was chosen as it was convenient. All ethical guidelines as well as the confidentiality of all respondents were strictly upheld during the course of this research.
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Instrumentation Measures
The instrument consisted of 4 variables with total amount of 94 questions. After the pilot test, a statistical analysis was initially conducted to see the validity of the instrument.
Some items were dropped as either the items could not pass the minimum requirement of reliability test or factor analysis test.
The questions were basically divided into five distinct parts; which were: part I) team characteristics; part II) team effectiveness; part III) organizational performance; part IV) survival and competitiveness; and part V) demographics. For part I to part IV, respondents were asked to rate each item that described identified dimensions using a 5-point Likert Scale with scale anchors ranging from “strongly agree” (1) to “strongly disagree” (5). For part V, respondents were asked to choose one of different available options.
Part I team characteristics, the 60 questions were adopted from various resources.
They were Davis & Davis (2008); Goodstein & Pfeiffer (1985); Hammond (2008); Hobman, Bordia, & Gallois (2004); Katzenbach & Smith (1993); Kwak (2004); Lencioni (2002);
Maxwell’s Team Effectiveness Questionnaire (TEQ) 2.0; Ryan’s Dimensions of Teamwork Survey; Watkins & Marsick (1999); Yu (2005); and Zoogah (2006), refer to appendix A. This was because there was no exact valid questionnaire that used the same model like this study;
therefore items were adopted from various resources for the purpose of this study. The validity and reliability of the items were then tested in the pilot test.
Part II team effectiveness, the 10 questions were adopted from Gladstein (1984);
Hammond (2008); and Kwak (2004). The validity and reliability of the 10 items were also being tested in the pilot test.
Part III organizational performance, the 9 questions were adopted from Balanced Scorecard (BSC) and Duque-Zuluaga and Schneider (2008). The validity and reliability of the 9 items were also being tested in the pilot test.
Part IV survival and competitiveness, the 9 questions were adopted from Lin (2009) in the Knowledge Management textbook. The questions were based primarily on the literature that affects the survival and competitiveness of an organization. The validity and reliability of the 9 items were also being tested in the pilot test.
Moreover, peer reviews and expert review were utilized to preserve the validity of the instrument. Pilot test was initially conducted to ensure the validity of each item, before gathering the whole data for the study.
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Validity and Reliability of Instrument
Reliability of instrument is the external and internal consistency of measurement. On the other hand, validity of instrument is the degree to which scales measure what researchers claim they measure (Williams & Monge, 2001).
Table 3.1
Reliability of Instrument Reliability
Type Theoretical Meaning Reliability of Instrument Reliability
Analysis
It is the dependability or consistency of the measure of a variable (Neuman, 2011).
Cronbach’s Alpha is computed for each variable.
Above .70 is generally considered acceptable (Nunnally, 1978).
Average variance extracted (AVE) is also computed. A cut-off value above or equal to .50 or higher is considered acceptable (Chin, 1998).
Table 3.2
Validity of Instrument Validity
Type Theoretical Meaning Validity of Instrument
Face Validity
It is a type of measurement validity in which an indicator “makes sense” as a measure of a construct in the judgment of others,
especially in the scientific community (Neuman, 2011).
The questionnaire is examined and translated by native speaker. It is also examined by two bilingual experts and two Indonesian academic professionals.
Content Validity
It is a type of measurement validity that requires that a measure
represent all aspects of the
conceptual definition of a construct (Neuman, 2011).
Every questionnaire items refer back to the definition. It is also examined by two peer reviews and expert reviews.
Criterion Validity
It is a type of measurement validity that relies on some independent, outside verification (Neuman, 2011).
The questionnaire was also initially undergone pilot tests.
Construct Validity
It is a type of measurement validity that uses multiple indicators and has two subtypes: how well the indicators of one construct
converge or how well the indicators of different constructs diverge (Neuman, 2011).
Convergent validity is established by using Explanatory Factor Analysis (EFA). Before the EFA test, KMO and Bartlett’s test for Sphericity are conducted. If the items are confirmed then EFA can be conducted.
Divergent validity is established by using Confirmatory Factor Analysis (CFA).
36 Face validity.
For the convenience of respondents, the questionnaire was available in English and Indonesian Language. Originally the instrument was gathered in English. It was translated from English to Indonesian language by the researcher. The translation was then revised by two bilingual experts, English – Indonesian Language translators. The questionnaire was also undergone a back translation to ensure the meaning of each items were preserved. The questionnaire was again reviewed by two Indonesian academic professionals from psychology department.
Construct validity.
There are two types of construct validity which are convergent validity and divergent validity, or it is also usually called as discriminant validity (Neuman, 2011). Before testing these factor analyses, there are two analyses that needed to be performed in advance.
First is the Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy value. This value indicates the appropriateness of using factor analysis on data. KMO value has to be greater than .80 can be considered excellent, as it is an indication that component or factor analysis will be useful for these variables. The closer the KMO value is to 1.00, the better the factors extracted from the data will account for the variance in the data (Friel, 2010).
However, if the value is above .70, it can also be considered as acceptable. When KMO less than .50, there is a need for remedial action, either by deleting the offending variables or by including other variables related to the offenders. KMO value was tested by using SPSS 17.0 software.
Second is the Bartlett’s test for Sphericity. The value tells whether or not the correlations between variables happened by chance. In Bartlett’s test, the small p-value, which is less than .05, confirmed that a factor analysis may be useful with the data gathered.
After these tests are confirmed, the data can then pass the minimum standard and factor analyses can be conducted. Bartlett’s test for Sphericity value was tested by using SPSS 17.0 software.
After the data can pass requirement for KMO test and Bartlett’s test for Sphericity, the data can then undergo the construct validity or factor analyses tests. The convergent validity is established by conducting EFA (factor loadings). EFA is used to test if the factors of each scale are consistent with those of previous studies. The correlations between item’s score with the total score of the dimension are used. If the item-to-total correlations are less than .40, the item is then dropped from further analysis (Kerlinger, 1986). This is because the
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item may not be correlated with other items that measure the same construct. EFA value was tested by using SPSS 17.0 software.
The divergent validity is established by conducting CFA. CFA is also used to test the hypothesis. Factor loadings of the items are observed. Structural equation modeling software is typically used for performing CFA. In this study, CFA value was tested by using SmartPLS 2.0 software.
Data Analysis Method
The empirical data collected from the sample were analyzed by looking at the following statistical analysis.
Descriptive Statistics
Descriptive statistics are used to describe the main features of a collection of data quantitatively without employing a probabilistic formulation (Mann, 1995). This holistic overview provides a simple summary about the sample in order to have a better understanding of where the data comes from. There are two purposes in using this descriptive statistics. First, it is to identify whether the sample collected in the main investigation can represent the case study. Second, it is to identify the main aspects of each variable that seems to be most influential within population under study. Mean and standard deviation are used in main study. Descriptive statistics are tested by using SPSS 17.0 software.
Correlation Analysis
A correlation analysis is performed in order to investigate the direction and strength of linear relationship between the independent variables and dependent variables. In the finding, we can conclude the relationship between dependent variable is positively or negatively related to independent variable. It is important to take note that a high correlation coefficient may not necessarily imply multicollinearity existed between the variables. Since the data has been confirmed by CFA, the constructs investigated in the study are also confirmed as distinct constructs. Therefore, if CFA is confirmed and the correlation coefficient value is still high, it means that the variables are highly related, and the value of regression can still be accepted. Correlations analysis is tested by using SPSS 17.0 software.
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Coefficient of Determination (R
2)
Coefficient of determination or regression is used to explain the endogenous latent variables to the total variance. This is to show the percentage of how much the data gathered can explain the real situation. Therefore, the higher the percentage, the data is closer to explain the situation in real. Regression value is tested by using SmartPLS 2.0 software.
Structural Equation Modeling (SEM)
SEM technique is usually used to determine if a given model can be applied to explain a given set of data or if a model is applicable to a given situation. This technique is suitable for testing whether the model in this research is valid within the context of YCAB Foundation. There are two core SEM techniques that applicable in this study. First is to perform CFA. Second is to perform causal modeling, or path analysis. Partial Least Square (PLS) is used to measure the path coefficients. The two values of SEM are used to offer evidence whether support or reject the hypothesis proposed in the beginning.
Partial least square (PLS).
PLS path-modeling algorithm is a type of SEM technique. It was developed by Herman Wold in 1975. PLS estimates path models using latent variables. It allows the simultaneous modeling of relationships among multiple constructs. In other words, it permits the analysis of a system of constructs. It is used in many scientific disciplines as an easy, yet powerful, estimation technique for SEM. Since the models we used for each construct identifies formative models, it is appropriate to use PLS techniques for this analysis (Chin, 1998). Moreover, it has the ability to handle model complexity which based on exploratory research. It is stated that if correctly applied, PLS can indeed be a “silver bullet” for estimating causal models in many model and data situations (Hair, Ringle, & Sarstedt, 2011).
Hair et al. (2011) also indicated that the individual path coefficients in the PLS structural model can be interpreted as the standardized beta coefficient in ordinary regressions. Path coefficients values are used to judge the relationship between variables, determine the direction of the relationships and its significance. Path coefficients are then tested by using SmartPLS 2.0 software.
39 Bootstrapping.
Bootstrapping is a way to duplicate the sample and retrieve the t-value to test whether the sample would be significant. The bootstrapping assumes that the sample is reasonably repeated of the assumed population distribution, and therefore the estimated coefficient in the PLS-SEM can be tested for their significance (Henseler, Ringle, & Sinkovics, 2009). The PLS algorithm uses the boostrapping samples to estimate the result, such as the t-value and the path coefficient. Hair et al. (2011) also stated that critical t-values for the two-tailed test are 1.65, 1.96 and 2.58, which represented weak, moderate, and strong relations. The significance of the relationship between variables can thus be assessed.
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