mustafa degerli - 2012 - master of science thesis - presentation - identifying factors influencing...

Presentation for the Master’s Thesis

May 2, 2012; Wednesday | 02.00 pm – 03.00 pm | B116

Information Systems, Informatics Institute, METU

Outline

Introduction

Literature Review

Research Model and Methodology

Data Analysis and Results

Conclusions

Questions & Comments

2 Mustafa Değerli - 2012 - METU Informatics Institute

Outline

Introduction

Literature Review



Conclusions



Introduction

Background and Statement of the Problem (1)

Systems are going to be useless or meaningless unless they are accepted by the intended users for intended purposes.

Develop and implement effective interventions with the aim of taking full advantage of employees’ acceptance and use for the designated systems.

Acceptance matters, since it is to govern the success of the systems.

Organizations are interested in standards and models based on processes


Introduction (Cont’d)


To get the best out of the productivity of people and to maximize the use of technology a focus on process (process-focus) delivers the expected groundwork (Chrissis, Konrad, & Shrum, 2006)

People, procedures and methods, and tools and equipment are the three critical dimensions that organizations typically manage to improve their businesses. Yet, these three core and critical dimensions are kept together by means of processes (Garcia & Turner, 2006; Chrissis, et al., 2006).



Taken from CMMI-DEV V1.3 (CMMI Product Team, 2010)




The process management principle “the quality of a system or product is highly influenced by the quality of the process used to develop and maintain it,” has been taken by the SEI of CMU.

Processes matter!

There is a need to determine the factors influencing the acceptance of processes.



Research Question

What are the factors influencing the acceptance of processes?

Addressed Issues

The factors influencing the acceptance of processes

The accompanying items ensuring the factors

The interactions of the factors

The inclusive appearance of the acceptance model

The descriptive and inferential findings

The statistically significant relationships

The outcomes of quantitative and qualitative analyses



Overall Evolution of the Study (1)



Overall Evolution of the Study (2)


Introduction (Cont’d) Literature

Review •Constructs, Hypotheses, Model

Instrument Development

•Content Validity, Ethical Permission

Pilot Study •Deployment, Data Collection and Analysis

Main Study

•Deployment, Data Collection, Descriptive Statistics, Data Screening, Reliability,

•EFA, CFA, Est. & Eva. of Initial Model

•Modification, CFA, Est. & Eva. of Final Model

•Descriptive Statistics and t-Testing

•Qualitative Analysis

Documenting Conclusions •Conclusions


Outline

Introduction

Literature Review



Conclusions



Literature Review

Pertinent literature was reviewed with respect to three dimensions the technology acceptance models and theories

the processes, and fascinated standards and model

the previous related researches and studies


Literature Review (Cont’d)

The technology acceptance models and theories (1)

Acceptance is the demonstrable willingness of the users to employ targeted systems, and

Acceptance can be modeled (Dillon & Morris, 1996)

Ground works for acceptance

Technology Acceptance Model (TAM) of Davis, Bagozzi, and Warshaw (1989),

Theory of Planned Behavior (TPB) of Ajzen (1991),

TAM 2 of Venkatesh and Davis (2000),

Unified Theory of Acceptance and Use of Technology (UTAUT) of Venkatesh, Morris, F. Davis, and G. Davis (2003), and

TAM 3 of Venkatesh and Bala (2008)



The technology acceptance models and theories (2)

The most usually cited is the TAM of Davis et al.

Perceived Ease of Use - perception that using a system will be free of effort

Perceived Usefulness - perception that using a system will enhance job performance

Attitude Behavioral

Intention to Use (Acceptance)

Actual Use

Perceived Usefulness

Perceived Ease of Use



The processes, and fascinated standards and model (1)

Process is a set of interrelated activities that transform inputs into outputs, in order to achieve a prearranged purpose

Process has inputs, entry criteria, activities, roles, measures, verification steps, outputs, and exit criteria (CMMI Product Team, 2010)

Input(s) Activities Output(s)



The processes, and fascinated standards and model (2)

Process-focused fascinated standards and model ISO 9001 (Quality Management Systems - Requirements)

AS 9100 (Quality Management System Requirements for Design and/or Manufacture of Aerospace Products)

ISO 27001 (Information Security Management Systems -Requirements)

AQAP 160 (NATO Integrated Quality Requirements for Software throughout the Life Cycle)

AQAP 2110 (NATO Quality Assurance Requirements for Design, Development and Production)

ISO 15504 (Information Technology - Process Assessment)

CMMI (Capability Maturity Model Integration)



The previous related researches and studies (1)

Riemenschneider, Hardgrave, and Davis’s Work Why individual developers accept or resist development

methodologies

Organizational mandate, compatibility of the methodology, opinions of developers’ coworkers and supervisors

Green, Hevner, and Collins’s Work What establishes evident achievement of a SPI to developers

SPIs ought to reveal positive impacts on quality and/or productivity, and the SPI must be perceived as useful for developers



The previous related researches and studies (2)

Dingsoyr and Moe’s Work How the participation in process workshops influences

The users should be involved in developing, as this promotes use behavior


Outline

Introduction

Literature Review



Conclusions




Defining Constructs and Formulating Hypotheses (1)

Used some constructs from selected technology acceptance models, and added its own special and contextual constructs

In total 19 constructs defined (3 of them from the TAM, 1 of them from the UTAUT, 5 of them from the TAM 3, and 10 of them defined as new constructs.

Defining

Constructs

Formulating

Hypotheses

Defining Items

for Constructs


Res. Model & Methodology. (Cont’d)

Defining Constructs and Formulating Hypotheses (2)

70 items defined for 19 factors (constructs)

As there are three core elements (PU, PEOU, and BI) in the TAM, the researcher formulated his hypotheses based on these core elements.

To formulate hypotheses, linked each remaining constructs (FC, SN, OQ, RD, JR, OU, OC, AUD, TLR, OM, STB, GRN, PD, TRN, MED and MDL) to these three constructs to decide on which construct(s) promotes which construct(s).

As a result, 51 hypotheses formed.


Perceived

Usefulness

Perceived Ease

of Use

Behavioral

Intention for

Acceptance of

Processes

Subjective

Norm

Organizational

Culture

Tailoring

Results

Demonstrability

Job Relevance

Objective

Usability

Facilitating

Conditions

Operations &

Maintenance

Granularity

Training

Modelling

Medium

Audit

Participation in

Development

Stability

Output Quality



Study Setting and Sample Selection

Probability sampling (random samples) applied Every member of the target population has an equal probability of

being picked

target population includes people who have relation with CMMI Level 5, ISO 15504 Level 5, CMMI Level 3, ISO 15504 Level 3, ISO 9001, NATO-AQAP 160, NATO-AQAP 2110, ISO 27001, and/or AS 9100.



Instrument Development

Survey instrument (questionnaire) designed based on the defined constructs and items for these constructs

23 changes applied after RaR cycle

Initial Design of

the

Questionnaire

Review and

Refinement (RaR)

with the Advisor

RaR with the

Process Experts

RaR with the

Social Science

Experts

RaR with Two

Participants

RaR with the

METU AWC

Personnel

Refined

Questionnaire



Pilot Study (1)

To re-check and re-evaluate the appropriateness of the developed instrument

Applied to a total of 250 participants for one-week period.

60 responses were collected (response rate: 24%).

Reliability Statistics for the Pilot Study

Cronbach's

Alpha

N of Items

,949 70



Pilot Study (2)

After analyzing descriptive statistics, individually analyzed the answers of the participants for the questions to detect if there is something to improve or not

Nothing found to change or improve probably owing to the review and refinement steps



Main Study (1)

Applied the questionnaire to nearly 4000 participants for one-month period.

368 responses are collected (response rate: ~9,2%)

77 of printed form, 291 of online.

~200 printed-form distributed to people from six different organizations.

~1700 e-mails sent to the people on the LinkedIn. From six different continents and from different disciplines and departments having relation with the processes.



Main Study (2)

~300 e-mails sent to the people from the SEI Partner Organizations

~100 e-mails sent to the people from the Turkish Standards Institute

~300 e-mails forwarded by the friends

~300 e-mails sent by the researcher to friends

~1100 e-mails sent to the people on the related Yahoo Groups. From six different continents and from different disciplines and departments having relation with the processes.



Applied Exploratory Factor Analysis (EFA) (1)

To explore and summarize the underlying correlational structures for the data set

11 steps were tailed to apply EFA 1. Sample size adequacy was checked.

2. Anti-image correlation matrix was analyzed.

3. Kaiser-Meier-Olkin (KMO) and Bartlett’s test was applied.

4. Extracted communalities were checked and addressed.

5. Factor analysis extraction method was defined and applied.

6. Rotation method was defined and applied.



Applied EFA (2) 7. Item main loadings (coefficients) were checked.

8. Rotated component matrix was created.

9. Number of factors was determined.

10. Total variance explained was evaluated and analyzed.

11. Factors and items per factors were defined and analyzed.



Applied Confirmatory Factor Analysis (CFA)

To determine whether the factors and the loadings of measured variables (items) on them conform to what is expected based on the pre-established theories

7 steps were tailed to apply CFA 1. Model was drawn with SmartPLS

2. PLS (Partial Least Squares) algorithm was run.

3. Factor loadings were checked.

4. Composite reliabilities (CR) were checked.

5. Average variance extracted (AVE) values were checked.

6. Convergent validity was ensured.

7. Discriminant validity was checked and ensured.



Applied Structural Equation Modeling (SEM) (1)

Hybrid of factor analysis and path analysis

To estimate and test the relationships among constructs/latent variables

Two types of SEM: covariance based SEM and partial least squares (PLS) SEM

Objective of PLS SEM is to show high R square and significant t-values, thus rejecting the null hypothesis of no-effect

Objective of covariance-based SEM is to show that the operationalization of the theory being examined is corroborated and not disconfirmed by the data



Applied SEM (2)

Decided to utilize PLS SEM over covariance based SEM, owing to the fact that PLS SEM requires only the very limited distributional assumptions (Chin, Peterson, & Brown,

2008)

bootstrapping is used to empirically estimate standard errors for its parameter estimates, which safes escape from constricting distributional assumptions (Gefen, Rigdon, & Straub, 2011)

does not necessarily necessitate sound theory base. That is to say, PLS supports both exploratory and confirmatory research (Gefen, Rigdon, & Straub, 2011)



Applied SEM (2) is a tool for situations that are “data-rich but theory-primitive.” (Gefen et al.,

2011)

should be chosen provided that the research is exploratory or an extension of an existing structural theory (Hair, Ringle, & Sarstedt, 2011).

should be selected, if there is many constructs and many indicators in the structural model, that is the structural model is complex (Hair et al., 2011).

Steps for SEM 1 - Data Collection

2 - Model Specification

3 - Model Identification

4 - Estimation

5 - Evaluation

6 - Modification



Applied SEM (3)

Model

Modification

Model Estimation

and Evaluation 1

Exploratory Factor

Analysis

Confirmatory

Factor Analysis

Model Estimation

and Evaluation 2

Confirmatory

Factor Analysis 2

Data Collection



Applied Descriptive Statistics and t Testing

to get more information about collected data and to reach more conclusions and interpretations (22 findings) Descriptive statistics used to describe samples of subjects in terms of

items (Tabachnick & Fidell, 2001, p. 7).

t-test used to evaluate the statistically significantly differences in means between two groups (Trochim, 2006)

Analyzing

Descriptive

Statistics

Documenting

Meaningful

Findings

t Testing

Raw Data



Applied Qualitative Data Analysis

To describe phenomena, classify it, and see how concepts interconnect and related, regarding meanings (Dey, 1993)

Used the quasi-statistics to determine the possible additional factors

This part optional, yet 158 (131 usable) responses collected.

Raw DataCreating Quantitative

Data Table (QDT)

Analyzing QDT

and Assigning

Codes

Interpreting

Assigned Codes

and Frequencies

Reporting Results of

Quantitative Data

Analysis


Outline

Introduction

Literature Review



Conclusions




Descriptive Statistics Results (1)

Frequency Percent Valid Percent Cumulative

Percent

Valid

26-33 146 39,7 39,7 39,7

34-41 83 22,6 22,6 62,2

50 or Over 65 17,7 17,7 79,9

42-49 48 13,0 13,0 92,9

18-25 26 7,1 7,1 100,0

Total 368 100,0 100,0


Percent

Valid

Asia 210 57,1 57,1 57,1

Europe 111 30,2 30,2 87,2

America 42 11,4 11,4 98,6

Australia 4 1,1 1,1 99,7

Africa 1 ,3 ,3 100,0

Total 368 100,0 100,0


Data Analysis and Results (Cont’d)

Descriptive Statistics Results (2) Frequency Percent Valid Percent Cumulative

Percent

Valid

Male 227 61,7 61,7 61,7

Female 140 38,0 38,0 99,7

Other 1 ,3 ,3 100,0

Total 368 100,0 100,0


Percent

Valid

12 Years or More 158 42,9 42,9 42,9

3-6 Years 66 17,9 17,9 60,9

6-9 Years 60 16,3 16,3 77,2

9-12 Years 48 13,0 13,0 90,2

0-3 Years 36 9,8 9,8 100,0

Total 368 100,0 100,0




Percent

Valid

Engineering 227 61,7 61,7 61,7

Science 41 11,1 11,1 72,8

Informatics 34 9,2 9,2 82,1

Economics and

Administrative Sciences 25 6,8 6,8 88,9

Other 24 6,5 6,5 95,4

Social Sciences 17 4,6 4,6 100,0

Total 368 100,0 100,0


Percent

Valid

Mature 231 62,8 62,8 62,8

High

Mature 137 37,2 37,2 100,0

Total 368 100,0 100,0




Percent

Valid

Engineering 144 39,1 39,1 39,1

Quality Assurance or

Process 90 24,5 24,5 63,6

Consultancy 43 11,7 11,7 75,3

Management 40 10,9 10,9 86,1

Independent Auditing and

Certification 13 3,5 3,5 89,7

Human Resources or

Training 11 3,0 3,0 92,7

Other 10 2,7 2,7 95,4

Acquisition/Contract/Purcha

sing 7 1,9 1,9 97,3

Administrative Affairs 4 1,1 1,1 98,4

Marketing 4 1,1 1,1 99,5

Finance 2 ,5 ,5 100,0

Total 368 100,0 100,0




Percent

Valid

Graduate 204 55,4 55,4 55,4

Undergraduate 110 29,9 29,9 85,3

Doctorate 43 11,7 11,7 97,0

High School 7 1,9 1,9 98,9

Associate Degree 4 1,1 1,1 100,0

Total 368 100,0 100,0


Percent

Valid

Private Sector Organization 316 85,9 85,9 85,9

Public Organization 52 14,1 14,1 100,0

Total 368 100,0 100,0



Data Screening

Checking for Incorrectly Entered Data there was no incorrectly entered data

Checking for Missing Data there was no missing value / data for all of the must items

Checking for Outliers

in the scope of this study outliers do not have adverse, extreme and pervasive influence on the means of the variables

Checking for Normality the normality assumption was not extremely violated



Reliability of the Instrument

The Cronbach’s alpha value was found as ,947, which is greater than the required minimum reliability of ,70

The Cronbach’s alpha if item deleted values for each item were analyzed to see each item’s weight on the reliability of the instrument

Validity of the Instrument

Content validity was ensured identified the general content to be characterized

items chosen from the content to correctly represent the information

obtained a group of items that is descriptive of the content

review of the instrument by the professionals



Results of Applied EFA (1)

Step 1: Checking Sample Size Adequacy subjects to variables (cases) ratio for this research was 19,4,

remarkably greater than the suggested 10

sample size was 368, remarkably greater than the suggested 200

Step 2: Analyzing Anti-image Correlation Matrix MSA values on the diagonals of AIC were used

the AIC – MSA values for the items in this research varied between ,665 and ,954, greater than the recommended ,50




Step 3: Applying KMO and Bartlett’s Test KMO of sampling adequacy was ,906, superb (excellent) criterion was

satisfied for sampling adequacy and factorability

Bartlett’s Test of Sphericity value for this study was ,000, less than recommended ,05

Step 4: Checking and Addressing Extracted Communalities should be greater than ,50

excluded the MDL5 item of the questionnaire, whose extracted communality value was ,496, less than ,50

After removal of MDL5 item, communalities values for the remaining 69 items were recalculated, final extracted communalities varied between ,519 and ,918 range




Step 5: Defining and Applying Factor Analysis Extraction Method Principal components method was used to reduce data to a set of

factor scores for use in data analyses.

Step 6: Defining and Applying Rotation Method To relate the calculated factors to theoretical entities, varimax

(oblique) rotation applied

Step 7: Checking Item Main Loadings (Coefficients) Values below ,4 were suppressed in the composition of factor

structure




Step 8: Creating Rotated Component Matrix

Step 9: Determining Number of Factors Eigenvalues of numbers greater than one was decided, and number of

factors was decided

Number of factors was determined as 18

Step 10: Evaluating and Analyzing Total Variance Explained generally happy with 50-75% of the total variance explained

was 71,583 % for this study.




Step 11: Defining and Analyzing Factors and Items per Factors Bare minimum for number of items per factor is two, and

recommended minimum is three

combined OR construct was defined to include items for RD and OQ

Number of factors was determined as 18 having at least 2 items for each.



Results of Applied CFA (1)

Step 1: Drawing Model with SmartPLS

Step 2: Running PLS Algorithm

Step 3: Checking Factor Loadings should be greater than ,6

all factor loadings were greater than the recommended ,6

Step 4: Checking Composite Reliabilities (CR) CR values larger than ,6 are normally judged as satisfactory

all CR values were larger than the recommended ,7



Results of Applied CFA (2)

Step 5: Checking Average Variance Extracted (AVE) Values AVE values less than ,5 are considered unsatisfactory

all AVE values were higher than the recommended ,5

Step 6: Ensuring Convergent Validity Factor loadings, CR values, and AVE values were calculated and

evaluated to check and ensure convergent validity

Step 7: Checking and Ensuring Discriminant The square roots of the AVE values for each construct should be

greater than the correlations among constructs

All the square roots of the AVE values for each construct were greater than the correlations among constructs



Estimation and Evaluation of the Initial Model (1)

Bootstrap approach was applied to estimate the significance (t-values) of the paths with 5000 bootstrap samples value and 368 cases value

Model fit was tested with significant path coefficients, high R square values, and composite reliabilities

R square values for the initial model varied between ,25 and ,75 values. Therefore, the model fit can be treated as moderate level with respect to R square values

CR values were all above recommended ,7 value



Estimation and Evaluation of the Initial Model (2)

there were some insignificant path loadings for some constructs, and some of the initially set hypotheses were not verified as results of hypotheses testing

model modification applied by freeing (estimating) or setting (not estimating) parameters to achieve the best-fitting model, as it is also explicitly required by the applied SEM

Modification applied, and CFA repeated via 7 steps, and all requirements for the steps were again met.



Estimation and Evaluation of the Modified Final Model

Bootstrap approach was applied

Model fit was tested with significant path coefficients, high R square values, and composite reliabilities

R square values for the initial model varied between ,25 and ,75 values. Therefore, the model fit can be treated as moderate level with respect to R square values

CR values were all above recommended ,7 value

As results of hypotheses testing 24 hypotheses were verified



Additional Findings based on Descriptive Statistics and t Testing

Answered the following 22 questions Is there a need of an easily accessible guideline about processes?

Is there a need for tools regarding processes?

What should be the language for documenting processes?

Is just text is enough for process definitions?

Is there a need to compose a devoted-group for process activities?

Is there a need of consultants for processes?

How much information or details should a process include?

Does gender make any difference for preference regarding the modeling type of processes based on the purposes?

Does gender make any difference for preference regarding outputs of processes?


Data Analysis and Results (Cont’d) Does gender make any difference for preference regarding deployment and

stability of processes?

Does gender make any difference for preference regarding stability of processes?

Does gender make any difference for preference regarding how much text there should be in the processes?

Does gender make any difference for preference regarding how much complex the interaction with the processes should be?

Does maturity level make any difference for preference regarding the modeling type of processes?

Is there a statistically significant relationship between maturity level and process modeling type?

Does maturity level make any difference for preference regarding outputs or results of processes?


Data Analysis and Results (Cont’d) Is there a statistically significant relationship between maturity level and

tailoring of processes?

Does continent (Asia or Europe) make any difference for preference regarding that commitments of people about processes?

Does continent (Asia or Europe) make any difference for preference regarding the organizational culture about processes?

Does continent (Asia or Europe) make any difference for preference regarding the tailoring of processes?

Is there a statistically significant difference between undergraduate and graduate people regarding trainings?

Is there a statistically significant difference between engineering and quality assurance/process people for preference regarding the constructs?



Additional Findings based on Qualitative Data Analysis and Results

158 of 368 participants (42,9%) provided answers

27 of 158 (17,1%) answers were somehow irrelevant or lack with respect to quality, content or context

131 usable answers out of 368 total sample size (35,6%)


Outline

Introduction

Literature Review



Conclusions



Perceived

Usefulness

Perceived Ease

of Use

Behavioral

Intention for the

Acceptance of

Processes

Subjective

Norm

Organizational

Culture

Tailoring

Outputs &

Results

Job Relevance

Objective

Usability

Facilitating

Conditions

Operations &

Maintenance

Granularity

Training

Modeling

Medium

Audit

Participation in

DevelopmentStability


Management

Commitment &

Sponsorship

Acceptance of

Processes

Role in

Performance

Evaluation

Measurement

Others *

Others *

Automation

Feedback by Users

National Culture

Punishment

Agility

Competition

Incremental Deployment

Low Coupling

Regulatory Requirements

Size

Team Factors

Type of Project

Personal Factors

Process Ownership

Customer Expectations


Conclusions

Conclusions based on Descriptive Statistics and t Testing (1) An easily accessible guideline about process system, structure, and

interactions ought to be available to the personnel

There should be tools to access processes easily and to use

Processes should be documented in users’ native language or a language in which users are proficient

There should be well-refined and meaningful visual elements, flows, and diagrams in the processes

A group should be composed for the activities for deployment, operations, and maintenance of processes, and this group should be composed of competent people who are directly responsible for their work, and have adequate theoretical and practical knowledge in the field


Conclusions (Cont’d)

Conclusions based on Descriptive Statistics and t Testing (2) There should be active, competent, and professional consultants

Processes should neither be high-level nor include many details, they should just include required and enough information

When compared to females, males more rigorously think that the type of process modeling should be shaped and determined by the purposes of the processes

When compared to males, females more strictly think that outputs or results of processes should be applicable to use for certain purposes

When compared to males, females more firmly think that processes should be deployed once they are mature enough, and by this way, more stable and mature processes should be generated



Conclusions based on Descriptive Statistics and t Testing (3) When compared to males, females more decisively think that processes

should be stable, and stability should be provided, rather than frequent changes in processes.

When compared to males, females more resolutely think that processes should not be composed of only texts.

When compared to males, females more steadily think that the interaction with the processes should not require too much mental effort, and it should not be too complex.

When compared to mature (organization) members, high-mature (organization) members more severely think that processes should be defined so that they just direct what to do information



Conclusions based on Descriptive Statistics and t Testing (4) When compared to high-mature (organization) members, mature

(organization) members more severely think that in addition to what to do information, processes should be defined so that they also direct how to do information.

When compared to mature (organization) members, high-mature (organization) members more firmly think that for processes, there should be defined and meaningful outputs or results, and these should be easily recognizable and noticeable

When compared to mature (organization) members, high-mature (organization) members more decisively think that some processes should be tailored in accordance with defined rules, with respect to project realities, rather than using them as they are



Conclusions based on Descriptive Statistics and t Testing (5) When compared to mature (organization) members, high-mature

(organization) members more decisively think that as the projects’ realities or priorities may differ from each other, not every project should be expected to implement all organizational processes as they are, tailoring should be permitted

When compared to people from Asia, people from Europe more strictly think that commitments of especially people to directly implement the processes, of processes owners, and of management representatives should be ensured during definition and before deployment of processes

When compared to people from Asia, people from Europe more firmly think that organizational culture has main contribution for providing the use of processes



Conclusions based on Descriptive Statistics and t Testing (6) When compared to people from Asia, people from Europe more

decisively consider that processes should be tailored for specific needs or realities of projects

When compared to undergraduate participants, graduate participants less rigorously think that trainings regarding processes are to provide more productive implementation of processes

When compared to people working in engineering department, people working in quality assurance/process department more conclusively consider that participation in development, organizational culture, outputs & results, tailoring, audit, training, operations and maintenance, and behavioral intention are significant and imperative regarding the acceptance of processes



Limitations

more participants may have been included from America continent

qualitative analyses and results part of the study may have been enhanced

Recommended Future Works

utilizing the developed PAM and findings, and appreciating or repudiating the promised benefits of the PAM in some organizations and reporting the results

improving the quantitatively developed model by adding the new constructs, discovered during qualitative analyses



Contribution of the Study A wholesome, quantitatively developed, acceptance model for process content

and context (process acceptance model [PAM]) comprising 18 factors covering different dimensions of process acceptance content and context.

Additional qualitatively determined factors (3 factors) defined as results of the qualitative analyses and interpretations to address the process acceptance meant for supplementary dimensions.

A checklist, quantitatively and qualitatively developed, to test and/or ensure the acceptance of processes.

The accompanying revised and refined items ensuring the factors influencing the acceptance of processes.

The interactions of the factors influencing the acceptance of processes.

The descriptive and inferential findings, and statistically significant relationships discovered in the course of the development of the PAM aimed at determining factors influencing the acceptance of processes.


Mustafa Değerli

[email protected]

www.mustafadegerli.com


mustafa degerli - 2012 - master of science thesis - presentation - identifying factors influencing...

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