International Journal of Business and Economy (IJBEC) eISSN: 2682-8359 | Vol. 5 No. 2 [June 2023]
Journal website: http://myjms.mohe.gov.my/index.php/ijbec
AN ANALYSIS OF HYBRID PROJECT MANAGEMENT ADOPTION FOR QUALITY 4.0 AND SUSTAINABILITY:
EVIDENCE FROM FINTECH IN MALAYSIA CONTEXT
Tan Chi Xiang1*, Zunirah Mohd Talib2 and Md Gapar Md Johar3
1 2 School of Graduate Studies, Management and Science University, Shah Alam, MALAYSIA
3 Information Technology Innovation Centre, Management and Science University, Shah Alam, MALAYSIA
*Corresponding author: [email protected]
Article Information:
Article history:
Received date : 28 March 2023 Revised date : 2 May 2023 Accepted date : 25 May 2023 Published date : 5 June 2023 To cite this document:
Tan, C. X., Mohd Talib, Z., & Md Johar, M. G. (2023). AN ANALYSIS OF HYBRID PROJECT
MANAGEMENT ADOPTION FOR QUALITY 4.0 AND
SUSTAINABILITY: EVIDENCE FROM FINTECH IN MALAYSIA CONTEXT. International Journal of Business and Economy, 5(2), 9-22.
Abstract: In this paper, the authors aim to analyse FinTech organization intention to adopt hybrid project management (HPM) methodology in system software development. It is important to find out the factors that affect organization decision makers' intention to adopt HPM. This study aims at applying theoretical approach that integrates Technology-Organization-Environment (TOE), which examine the factors that impact FinTech organization’s decision to adopt HPM into their software development projects. It examines those factors that form organization decision makers' readiness for HPM and cause their sustainable intention to implement it. When forming the independent and dependent variables into research framework, the results show that the effect of organization size and environmental uncertainty is significant with regard to HPM adoption in FinTech Malaysia and sustainability in Quality 4.0. In contrast, the effect of complexity is insignificant towards innovation adoption. The outcomes of this study provide valuable insights for the practitioners and contribute to Quality 4.0 and FinTech literature.
Keywords: FinTech, Quality 4.0, HPM, TOE, Intention to adopt.
1. Introduction
The term “FinTech” defined as the firm combination of financial services with innovative technologies offered to improve and automate traditional finance forms for financial service providers and consumers alike (Dorfleitner et al., 2017). Quality 4.0 technologies and innovations has becoming more important and more visible among organizations and financial industrial sectors (Jayashree et al., 2022; Luthra and Mangla, 2018). Financial industry dealing with the frequent changing condition such as sustainable business requirements change to fulfil market needs. Influential forces factors such as complexity, environmental uncertainty, digital revolution, and customers' needs are leading financial industry to follow the pace to deal with changes to stay competitive. Hence, the challenges making financial institutions to change their project management methodology, technologies, project status tracking and reporting method.
This study is focusing to find out the key factors leading the large financial institutions intention to adopt and apply HPM in their software development with taking considerations of constraints from technological, organizational, and environmental these financial institutions are having (Kilu et al., 2019).
This study superficially focuses on the software development institutions intention to adopt HPM in Malaysia FinTech industry. HPM is a new concept and emerging stage within Malaysia FinTech industry. The aim of HPM is to create the optimal results along the project implementation (Hisham Alasad, 2020). This paper targets the population of Malaysia FinTech industry from different managerial level and explore the factors leading their intention to adopt HPM during software project implementation. An exploration will be conducted by using these subjects, and questionnaires about these cited dimensions were carried out. The scope of this study is only limited to the Malaysia FinTech organization’s decision makers’ intention to adopt HPM and the implementation of the HPM itself does not include in this study. The participants were limits to FinTech organization’s management or lead level, FinTech project manager and FinTech project stakeholders who had a role in their organizations to allow them to influence the adoption decision process.
This paper is structured as follows. Section 2 discusses the relevant concepts from the existing literatures related to HPM and TOE. Section 3 explains the framework, hypotheses, and measures used in this study to analyse the data. Section 4 forms discussion on the measurement model, structural model and mediation results along with the model strength and quality.
Results shown that hypothesis H1 is insignificant whereas hypotheses H2 and H3 are significant with regard to HPM adoption in FinTech Malaysia and sustainability in Quality 4.0. Section 5 provides conclusions with limitations of this study and proposed future research and further elaborates the implications and applications of the results of this study and presents relevant suggestions for FinTech decision makers and practitioners.
2. Literature Review
The challenges surrounding the need exploring the relationship between the Malaysia FinTech organizations decision maker’s intention to adopt HPM, and the technological, organizational, and environmental factors.
2.1 Hybrid Project Management (HPM)
HPM is defined as the combination of predictive and adaptive approaches (PMBOK® Guide, 2021). Besides, HPM also defined as “the methods that combine planning strategies from the traditional project manager environment with the Agile approach’s flexible approach.” (Johann Strasser, 2020). HPM enables project stakeholders to adapt business requirements regular changes and the requirements are defined iteratively (PMBOK® Guide, 2021). HPM employs
“thoroughness of Work Breakdown Structure (WBS) with speed and lean benefits of Agile for a new project management method which is both detailed and fast.” (Sandra Idjoski, 2021).
Scrum master and project manager are both are sharing the same responsibility relates with various project segments in HPM implementation. However, scrum master has to provide adequate supports and updates to project manager during sprint execution (Robert G. Cooper
& Anita Friis Sommer, 2018).
2.2 Technology-Organization-Environment (TOE)
There are three context aspects can affect the technological innovation adoption. The theoretical model emphasizes the impacts of multiple levels of technology application contexts:
application scenarios, the degree of organizational adapt with technology applications, and organizational needs on the influences of technology applications (Wang, S. et al., 2022;
Tornatzky & Klein, 1982):
1. Technological Context 2. Organizational Context 3. Environmental Context
The technical context refers to the current and new technologies in an organization and the components consists of relative advantage, compatibility, complexity, and observability, which affect the particular consequences of technology within the organization (Wang, S. et al., 2022;
Sin Tan et al., 2009; Low et al., 2011). The organizational context comprises elements including the top management support, organization size, and organization readiness (Oliveira et al., 2014; Malik et al., 2021; Melo et al.; 2021; Setiyani & Yeny Rostiani, 2021; You Fu &
Lee, 2021). Industry pressure, environmental uncertainty, and business partner quality are part of elements in environmental context (You Fu & Lee, 2021; Pacheco-Bernal et al., 2020;
Effendi et al., 2020; Athambawa Haleem, 2021).
3. Materials and Methods
3.1 Hypotheses and Research Framework
Integrating the literature and hypotheses described as below, the research framework shown in Figure 1 is adapted from (Pateli et al., 2020; Piaralal et al., 2015; Püschel et al., 2010).
Figure 1: Conceptual Model
Complexity refers to the difficulty level of understanding in using a new technology (Sonnenwald et al., 2001). Complexity is also defined as the time consumed to execute the tasks, system features and functionalities, and system’s third parties’ interfaces design (Gangwar et al., 2015). The possibilities of new innovation adoption lowed if the innovation is difficult to be performed (Rogers, 2003). The technologies should be easier to manage, easier to use, and user friendly (Berman et al., 2012). Complexity is the technological factor which the level of the innovation is viewed as hard to understand and implement (Salah OH et al., 2021).
Melo et al. (2021) explained that organization size is the main determinants of technological adoption in an organization. Organizational size is identified as one of the factors affecting organization to adopt new information system innovations (Jeyaraj et al., 2006). The larger of organizational size, the the higher intention to adopt more information technology innovations from the aspects of risks and flexibilities; the smaller size of organization shows lower intention to adopt new technology innovations (Setiyani & Yeny Rostiani (2021). However, Goode &
Stevens (2000) study indicates that organization size is not significantly impacting an organization to adopt new information system innovations.
Environmental uncertainty can weaken the adoption of new technology or innovation (Effendi et al., 2020). Environmental uncertainty happens when there are transformations are quick and complicated. New technologies adoption may not be happened in the organization undergoes high uncertainty without clearer picture on operating standards. This environmental uncertainty influences organization to have lower intention to adopt new innovations.
Hypothesis 1 (H1): Complexity will significantly affect the hybrid project management adoption decision in the FinTech Industry Malaysia.
Hypothesis 2 (H2): Organization size will significantly affect the hybrid project management adoption decision in the FinTech Industry Malaysia.
Hypothesis 3 (H3): Environmental uncertainty will significantly affect the hybrid project management adoption decision in the FinTech Industry Malaysia.
H2
Intention of Hybrid Project Management Adoption in the FinTech Industry
Malaysia (ADPT) Complexity
(CX) Organization Size
(OS) Environmental Uncertainty (EU)
H3
H1
3.2 Methodology
Quantitative approach with correlational design flow is chosen as this study research method to measure the correlation between the technological, organizational, and environmental factors and FinTech organization’s decision makers’ intention to adopt HPM methodology into their software project development. Quantitative research method is used to evaluate the opinions, behaviors, attitudes, and other variables by using the method of recapitulate results from produced numeric statistic data based on a defined area of population samples (Mohajan, Haradhan, 2020). Quantitative approach is also defined as a set of assumptions, strategies, and techniques which using in researches of examining social, psychology, and economy processes by exploring the numeric statistic data designs (Bosnjak et al., 2020). Questionnaires, experiments, or observations are methods to be used in quantitative research to study groups of peoples or populations and researchers will perform complicated statistic data analysis based on the series of quantitative information (Mohajan, Haradhan, 2020). Quantitative method is the preferable method to be used in identifying the relationship between variables and this can be done through gathering numeric data and analyse the data by using statistic demonstration (Allen Rubin & Earl R Babbie., 2017).
In this study, participants with FinTech software development related working experiences were invited as this study participants and only respondents. The target population are employees of FinTech organizations based in Malaysia. There was 233 FinTech companies and 27 numbers of banks in Malaysia at year of 2021 (Fintech News Malaysia, 2021). The construct indicator reliability, and validity will be examined by the analysis of FinTech firms’
experiences and information which involving in software development services and project management expertise in projects. The participants were professionals who working in the FinTech organizations and working as a managerial role in providing decision of adopting new project management methodology, project planning and innovations in their respective organization.
The questionnaires invitation was sent and questionnaires were accessed by survey participants via online google form. After the online questionnaires were responded, the data were gathered and recorded in excel format. All survey participants were having an understanding of predictive, adaptive, and HPM methodology in delivering FinTech software solutions. The aspects affecting HPM adoption were measured by using well-labeled seven-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). SmartPLS 3.3.3 software will be used to analyze the data and Partial Least Square (PLS) was used for data analysis in this study.
Structural Equation Modeling (SEM) was used to validate instruments and evaluate the correlation between constructs. PLS procedure is using by many researchers as it has the capability to build hidden built of small and medium sample data sizes and connections between the conceptual contexts and the gauge of each construct. The logical analysis was running by using SmartPLS software which adopting SEM approach and hypotheses will be evaluated. Multiple variables were analyzed and the variables indicated the measurement extracted from surveys which are typically employed as primary data collection. PLS-SEM is the option as it offers approximation of complex models with many item variables and constructs, and allows the flexibility of relationships specification and data requirements (Lundin, 2020).
Table 1: Demographics and Descriptive Statistics
Freq. %
Gender
Male 135 60.54
Female 88 39.46
Age Group of Respondents
20 - 30 52 23.32
31 - 39 88 39.46
40 - 49 69 30.94
50 o above 14 6.28
Highest Education Level
High School 3 1.35
Diploma 3 1.35
Bachelor 166 74.44
Master 50 22.42
PhD 1 0.45
Respondent Category
Director of Organization 9 4.04
Head of Department 5 2.24
Project Manager 66 19.60
Scrum Master 16 7.17
Business Lead 31 13.90
Technical Lead 80 35.87
Testing Lead 16 7.17
Years of Working Experience
Below 3 22 9.87
3 – 5 7 3.14
5 – 10 60 26.94
10 – 15 47 21.08
15 – 20 44 19.74
Above 20 43 19.28
Organization’s Number of Employees
< 50 38 17.04
50 – 100 20 8.97
101 – 500 69 30.94
501 – 1000 21 9.42
> 1000 75 33.63
A total of 415 questionnaires invitation were sent out and 223 were answered completely, thereby yielding a total response rate of 53.74%. After checking data for any missing values, unusable responses or outliers, at last, 223 responses were remained usable as this is because the questions were set compulsory option to answer in google form and no optional question is provided. Sample size of 223 in this study is appropriate as it is aligning with the widespread application of "10 times rule of thumb" which has suggested minimum sampling size more than 10 times from total number of independent variables. This study has maximum of 4 arrows pointing at a latent variable and this in line with the requirement of minimal sampling size of 65 (Hair et al., 2011; Chin & Marcoulides, 1998). The sampling size of 223 in this study has met all the criteria above. All participants can choose option to receive study’s findings at the ending of survey questionnaire to encourage more professionals to take part in this study.
3.3 Measurement
Measurement tools from several previous literatures were adapted into this study to assure that the tools and instruments being used do not have any validity or reliability problems. The current questionnaires in this study are adapted and modified from several different studies.
The questionnaire used for data collection was divided into two sections: demographic questions and followed by section of inclusive of questions related to each of the constructs in the model. To assess in which process stage of the organization is currently in, the participants will be chosen one from six selections measuring the dependent variable of HPM adoption:
1. My organization or the organization I work for not considering adoption of HPM.
2. Currently my organization or the organization I work for in progress of evaluating adoption of HPM.
3. My organization or the organization I work for has evaluated HPM, but do not plan to adopt it.
4. My organization or the organization I work for has evaluated HPM and intends to adopt it.
5. It is likely that my organization or the organization I work for will take steps to adopt HPM in the future.
6. My organization or the organization I work for has already adopted HPM.
4. Results and Discussion
The collected survey data will be analysed using multivariate analysis method which is Partial Least Squares (PLS) approach in this case and it is based on Structural Equation Modelling (SEM). The study model was assessed using SmartPLS 3.3.3 software tool which is using PLS- SEM approach and it provides rough complex models with allowing the agility of relationships specification and data requirements (Lundin, 2020). According to (Rick H. Hoyle., 1995), the ideal starting point to conduct path modelling is suggested between value from 100 to 200. The dataset size in this study is n which is more than the suggested value and hence PLS-SEM approach is a good option for this research. Bootstrapping is defined as a method to create multiple datasets out of one dataset. PLS-SEM approach leveraging on bootstrap procedure to examine the various results whether it is significant (Davison & Hinkley, 1997). In addition, according to (Lai et al., 2012), PLS is believed is the preferrable approach for decision-making, management-oriented problems, and prediction studies. Hence, PLS is the better option in situations where other methods unable to cover or when developed solutions are inadmissible.
Table 2: Item Loadings Item
Constructs CX OS EU IA
Complexity (CX)
CX1 0.935
CX2 0.637
CX3 0.845
Organization Size (OS)
OS1 0.908
OS2 0.733
OS3 0.852
Environmental Uncertainty (EU)
EU1 0.832
EU2 0.687
EU3 0.908
Intention to Adopt (IA)
IA1 0.803
IA2 0.617
IA3 0.706
IA4 0.674
Item
Constructs CX OS EU IA
IA5 0.780
IA6 0.713
Construct validity is assessed by examining both the convergent and discriminant validity, and if the value of 0.5 or higher was set as the acceptable value of average variance extracted (AVE). Table 2 and table 3 shows that constructs in this study have AVE values greater than 0.5 and ranged between 0.516 and 0.696, thus confirming convergent validity. The discriminant reliability was followed examined using both Fornell–Larcker's and Heterotrait-Monotrait Ratio (HTMT).
Table 3: Reliability and Convergent Validity Cronbach’s
Alpha
RhoA Composite Reliability
(CR)
AVE
CX 0.756 0.912 0.853 0.664
OS 0.780 0.780 0.872 0.663
EU 0.743 0.831 0.853 0.516
IA 0.813 0.811 0.864 0.696
Fornell-Larcker criterion compares the square root of AVE with the latent variable correlations.
The measurement model convergent validity is assessed by AVE and composite reliability. The square root of construct CX, EU, IA and OS are greater than its highest correlation with any other construct and this assessment the discriminant validity can be achieved.
Table 4: Discriminant Validity: Fornell–Larcker Criterion
CX EU IA OS
CX 0.815
EU 0.184 0.814
IA 0.183 0.758 0.718
OS 0.170 0.499 0.669 0.834
HTMT is the measurement of similarity between latent variables. If the HTMT value is less than value 1, discriminant validity can be regarded as established. A threshold of 0.85 reliably distinguishes between the pairs of latent variables that are discriminant valid and those that are not (Franke, G. and Sarstedt, M., 2019). As shown in Table 5, all of the variables displayed acceptable discriminant validity by using the HTMT test and bearing values are mostly below thresholds. Result below shows that all the values are less than value of 1.
Table 5: Discriminant Validity: Heterotrait–Monotrait Ratio (HTMT)
CX EU IA OS
CX
EU 0.213
IA 0.224 0.941
OS 0.217 0.635 0.796
4.1 Structural Model Assessment
Figure 2 below shows the structural model for this study.
Figure 2: Structural Model
From Path Coefficients statistic in Table 6 below, t-value of construct EU and OS are larger than the critical values (1.96 and 2.58) and these two constructs are considered significant with the levels of 5% and 1% respectively. Besides, the P value of construct EU and OS are less than value 0.05 and this shows that construct EU and OS are significant. However, the t-value and P value of construct CX is shown insignificant.
Table 6: Path Coefficients and Hypotheses Testing Results Path Original
Sample (O)
Sample Mean (M)
Standard Deviation (STDEV)
T Statistics (|O/STDEV|)
P Values Results
H1: CX -> IA 0.014 0.021 0.039 0.365 0.715 Not
Supported
H2: EU -> IA 0.563 0.564 0.044 12.926 0.000 Supported
H3: OS -> IA 0.386 0.385 0.039 9.918 0.000 Supported
Based on the path analysis is shows that:
H1: CX (β=0.014, t=0.365, p>0.05) does not has direct influences IA.
H2: EU (β=0.563, t=12.926, p<0.05) directly influences IA.
H3: OS (β=0.386, t=9.918, p<0.05) directly influences IA.
As a result, hypothesis H1 is not supported, and hypothesis H2 and H3 are supported.
R Square (R2) is defined as the strength of the least-squares fit to the training set activities. An R2 value of 0.9 is explained as the model accounts for 90% of the variance in the observed activities for the training set. The value gets closer and closer to 1 (100%) as more PLS factors are incorporated into the fit. R2 is also refers to the proportion of the variance in the response variable which can be explained by predictor variable. R2 value is ranged from 0 to 1. Value 0 indicates that response variable is unable explained by the predictor variable and value 1 indicates that response variable can be perfectly explained without error by the predictor
variable (Chin & Marcoulides, 1998). In this study, the bootstrapping generated 5000 samples from 223 cases. Referring to Figure 2 above and Table 7 below, the structural model 68.8% of the variation in IA is explained by the CX, OS, and EU constructs.
Table 7: R2 Result Path Original
Sample (O)
Sample Mean (M)
Standard Deviation (STDEV)
T Statistics (|O/STDEV|)
P Values
IA 0.688 0.696 0.033 20.696 0.000
F Square (F2) is defined as effect size (>=0.02 is small; >=0.13 is medium; >=0.26 is large). F2 measures variance illustrate each exogenous variable in the model (Chin & Marcoulides, 1998).
From Table 8 below, it shows that:
Construct CX effect size is 0.936, and this indicates that the effect size is large.
Construct EU effect size is 0.000, and this indicate that the effect size is small.
Construct OS effect size is 0.000, and this indicate that the effect size is small.
Table 8: F2 Result Path Original
Sample (O)
Sample Mean (M)
Standard Deviation (STDEV)
T Statistics (|O/STDEV|)
P Values
CX -> IA 0.001 0.006 0.008 0.081 0.936
EU -> IA 0.750 0.782 0.199 3.768 0.000
OS -> IA 0.355 0.365 0.096 3.710 0.000
Q Square (Q2) refers to predictive validity and relevance, and it measure the test model whether it has predicted validity or nor (>0 is good). For Q2 value, value>=0.02 is small; >=0.15 is medium; >=0.35 is large (Chin & Marcoulides, 1998). Whereas, that Q2 value for construct IA has value of 0.335 which this considered this construct is medium and it has predicted validity.
Table 9: Construct Crossvalidated Redundancy
SSO SSE Q2 (=1-SSE/SSo)
CX 669.000 669.000
EU 669.000 669.000
IA 1338.000 890.123 0.335
OS 669.000 669.000
5. Conclusion
The significance of this study is to reveal the relationship between the intention of Malaysia FinTech organization’s decision makers, project stakeholders and project managers to adopt HPM methodology into their software development project and some of the technological, organizational, and environmental elements that they are encountering. The new information revealed from this study might helping FinTech and Quality 4.0 during assessing the factors which requires to be taken into consideration before and during adopting HPM methodology during implementing software development project especially financial software systems. The outcomes of this study provide the advantages related to HPM adoption in FinTech Malaysia which beneficial to FinTech organizations before their project planning.
Technology, organizational, environmental factors play a significant role and provide positive perceptions about the factors of intention adoption of HPM such as complexity, organization size, and environmental uncertainty. There is one hypothesis is not supported i.e., CX -> IA, this indicates that complexity has negative influence towards organization’s intention to adopt HPM. This is contrary to our proposed hypothesis. This study also concluded that OS and EU factors have positive influencing organization’s intention to adopt HPM. This study scope is focusing in Malaysia only, for future study, a larger sample size with a more diverse geographical range of respondents from another country of multiple countries shall further improve the statistical power to achieve more generalizable results. Secondly, this study examines the complexity, organization size, and environmental uncertainty factor as antecedents of TOE. However, these are not the only sole determinants for decision makers in FinTech organization intends to adopt HPM, the observability is one such example of another measurement.
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