Technology Acceptance - Science topic

Gives a good result

Which is you sample size? CB-SEM only works with big samples, the requirements for the size depend on the number of variables included in your model. As Marius Ole Johansen said you should also check the distribution of the data.

With SUS it very much depends on project and user-group size and on what outcome you’re looking for, areas to act on, or areas to investigate further.

SUS, for most commercial concerns, a participant size of 40-50 will provide actionable results with a good level of confidence. For most small workplace projects, a sample of 5 can provided results that effective enough to indicate areas for investigation.

TAM is a very different measurement and can vary significantly on which variation of TAM you’re using. TAM, is usually good enough for small user groups, but because of it’s disconnect from external influences can provide false positive and negative results.

TAM can be effective between 5 and 10 participants, due it’s general targeting of ethnographically-similar, smaller, user groups.

TAM2, and the evolving TAM3, with their greater recognition of external influencing factors, provide stronger, more nuanced results and are transferable to larger projects.

In most studies I’ve done or have seen, PU (Perceived Usability) and PE (Perceived Ease of use) correlations garner higher degrees of confidence between 40 and 70 participants.

Unified Theory of Acceptance and Use of Technology (UTAUT) can be used as I myself is planning to use in my new research.

Extending the Technology Acceptance Model (TAM) with an additional antecedent involves several steps:

1. **Literature Review:** Conduct a thorough literature review to identify relevant antecedents that have been studied in the context of technology acceptance. Choose an antecedent that aligns with your research objectives and theoretical framework.

2. **Theoretical Framework:** Clearly define your theoretical framework by integrating the selected antecedent with the TAM. Understand how the new antecedent interacts with the core TAM constructs (perceived usefulness and perceived ease of use).

3. **Hypothesis Formulation:** Develop hypotheses that articulate the relationships between the new antecedent, perceived usefulness, perceived ease of use, and behavioral intentions to use the technology. These hypotheses will guide your research and provide a basis for testing your model.

4. **Data Collection:** Gather relevant data through surveys, interviews, or experiments. Ensure that your data collection methods align with your research design and allow you to measure the constructs accurately.

5. **Data Analysis:** Use appropriate statistical techniques, such as regression analysis or structural equation modeling (SEM), to analyze the collected data. Evaluate the relationships between the antecedent, TAM constructs, and behavioral intentions. Assess the significance and strength of these relationships.

6. **Interpretation:** Interpret the results of your analysis. Determine whether the new antecedent has a significant impact on perceived usefulness, perceived ease of use, and behavioral intentions. Discuss the implications of your findings in the context of existing literature and theoretical frameworks.

7. **Discussion and Conclusion:** Summarize your findings, discussing their theoretical and practical implications. Address the limitations of your study and suggest directions for future research.

8. **Documentation:** Document your research methodology, data analysis procedures, results, and conclusions in a clear and concise manner. Consider publishing your work in academic journals or conferences to contribute to the existing body of knowledge.

Remember that each research study is unique, so adapt these steps according to your specific research context and objectives.

Your questionnaire is too long and the questions are repetitive - But the two questions never asked - made me stop. -- I never was asked if I want to engage with robots. Or, even if past experience on any level was satisfactory.

.....

Hi Egon. I would agree with you if you take the whole context into consideration. Then, yes, PEOU and Usability can also be seen as what the user experience is (UX) is like. However, UX, PEOU and usability could also have bad outcomes, or side affects. For example in relation to TAM, the experience might be good and the software might be easy to use but they might not want to use it as they don't perceive it useful.

You can also see the effect from the lens of each country as moderator/MGA. It would be interesting to see how tourist from each country perceives your model. Thanks

There two was that I know of in SPSS:

To calculate the average score for each scale/construct, click the following in SPSS:

Transform / Compute variable / in “Function group” select “Statistical” /

in “Functions and special variables” double click on “Mean” /

in the “Numerical expression” the “Mean” function appears in the box, replace question mark with variable names with commas by “blocking” the ? and double click on the variable names (in the “Type and label” section) /

Double click one by one and type comma in between.

Type label in “Target variable”, e.g. "Actual Use" / OK

A new variable will appear in the "data view" as per you label Actual Use

Analyse / Dimension reduction / Factor /

Move all construct variables (i.e the four items) across into “Variables” /

Scores / Save as variables

Options / Supress small coefficients / e.g. 0.40 (whatever value decide upon)/ OK

At least one new variable will appear in SPSS (depending if the 4 items were sufficiently inter-correlated) called FAC1_1 or more more if they were not!

If there was more than one variable, then it is possible to force the items together into one variable by (in the steps the steps above) to also select Extraction / Fixed number of factors (Factor to extract) / e.g. 1 (in your case) / OK

You can publish your academic articles in the Journal of Metaverse. It is a great opportunity. It is open-access, fully free and international double-blind peer-reviewed. You can visit the journal on https://dergipark.org.tr/en/pub/jmv

Dear Aws,

To analyze the adoption of this type of innovation, you can use traditional models:

- TAM (Technology acceptance model) (Davis, 1989)

- TAM 2 (Venkatesh & Davis 2000 & Venkatesh 2000)

- TAM 3 (Venkatesh & Bala 2008)

- UTAUT (Unified theory of acceptance and use of technology) (Venkatesh et al. 2003)

However, you can also use other models such as:

- TRA (theory of reasoned action) (Ajzen & Fishbein, 1980)

- TRI (Technology Readiness Index) (Parasuraman, 2000).

- TRI 2.0 (Parasuraman & Colby, 2015)

For more recent updates, it is recommended that you participate in specialized conferences where these topics are addressed, such as AIRSI2020; AIRSI2021 or the future edition of

AIRSI2022 http://airsi2022.unizar.es/

Good luck with this interesting topic.

Rerences:

- Ajzen, I.; Fishbein, M (1980), Understanding attitudes and predicting social behavior, Englewood Cliffs, NJ: Prentice-Hall

- Davis, F. D. (1989), "Perceived usefulness, perceived ease of use, and user acceptance of information technology", MIS Quarterly, 13 (3): 319–340, doi:10.2307/249008

- Davis, F. D.; Bagozzi, R. P.; Warshaw, P. R. (1989), "User acceptance of computer technology: A comparison of two theoretical models", Management Science, 35 (8): 982–1003, doi:10.1287/mnsc.35.8.982.

- Parasuraman, A. & Colby, C.L (2015). “An Updated and Streamlined Technology Readiness: TRI 2.0”, Journal of Service Research, Volume 18:1, pages 59-74

- Parasuraman, A. (2000). "Technology Readiness Index (TRI) a multiple-item scale to measure readiness to embrace new technologies". Journal of Service Research. 2 (4): 307–320. doi:10.1177/109467050024001.

- Venkatesh, V. (2000), "Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model", Information Systems Research, 11, pp. 342–365

- Venkatesh, V. (2000), "Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model", Information Systems Research, 11, pp. 342–365

- Venkatesh, V.; Bala, H. (2008), "Technology Acceptance Model 3 and a Research Agenda on Interventions", Decision Sciences, 39 (2): 273–315, doi:10.1111/j.1540-5915.2008.00192.x

- Venkatesh, V.; Davis, F. D. (2000), "A theoretical extension of the technology acceptance model: Four longitudinal field studies", Management Science, 46 (2): 186–204, doi:10.1287/mnsc.46.2.186.11926

- Venkatesh, V.; Morris, M. G.; Davis, G. B.; Davis, F. D. (2003), "User acceptance of information technology: Toward a unified view" (PDF), MIS Quarterly, 27 (3): 425–478, doi:10.2307/30036540, JSTOR 30036540

Dear Dominique Heintjes,

I suggest you to see links on topic.

Best regards

I'm not sure I fully understand what you are asking so forgive me if my answer is off topic. I'm assuming you are asking whether your exogenous factors on the left hand side of your path diagram should be allowed to correlate, that is, whether their covariances should be estimated as free parameters. The answer to this is that your model is probably misspecified if you assume no covariances / implicitly constrain/fix all of those covariances to zero (because at least some of them may be correlated). As a result, your structural regression coefficients may be biased towards significance. I would check to see whether the model without covariances fits significantly worse than the model that does allow the covariances. If it does, the model with covariances freely estimated should be preferred.

The covariances are important because they account for the partial redundancy of the exogenous factors in predicting your dependent factor. If you (incorrectly) fix them to zero, the results of the structural regression may be incorrect also.

The questionnaire of Technology Acceptance Model (TAM) Questionnaire developed by Davis (1989) is below

about modified by Nair and Das (2011) Could you please provide the full reference of Nair and Das (2011) article

Perceived Usefulness

Using CHART-MASTER in my job would enable me to accomplish tasks more quickly.

Using CHART-MASTER would improve my job performance.

Using CHART-MASTER in my job would increase my productivity.

Using CHART-MASTER would enhance my effectiveness on the job.

Using CHART-MASTER would make it easier to do my job.

I would find CHART-MASTER useful in my job.

Perceived Ease of Use

Learning to operate CHART-MASTER would be easy for me.

I would find it easy to get CHART-MASTER to do what I want it to do.

My interaction with CHART-MASTER would be clear and understandable.

I would find CHART-MASTER to be flexible to interact with.

It would be easy for me to become skillful at using CHART-MASTER.

I would find CHART-MASTER easy to use.

Hello Jawad Harb. Please forgive me for commenting on something other than the question you raised. In the original question, you wrote:

"After checking the indirect effect (mediation effect) I have found that perceived usefulness is a mediator between the external variable and attitude."

By definition, a mediator is on a causal pathway between two other variables. So your statement is tantamount to saying that X causes M and M causes Y. This way of describing the results of mediation analyses is very common, but IMO, it is rather careless, especially when one has observational data (as opposed to experimental data). For alternative and more careful ways of describing your results, see section 6.6 in the paper by Fiedler et al. (2018). I think this article should be required reading for all mediation analysts. HTH.

Fiedler, K., Harris, C., & Schott, M. (2018). Unwarranted inferences from statistical mediation tests–An analysis of articles published in 2015. Journal of Experimental Social Psychology, 75, 95-102.

Thanks for your answers!

How would you have a discussion about the TAM and blended learning without IT significance? Blended learning requires IT and users need to accept the IT. The IT in this sense is all the tools and software that users need to 'onboard' onto, not to mention the IT infrastructure required to support 'virtual classrooms'

I have found this work on it. https://searchdatacenter.techtarget.com/es/chronic/Casos-de-uso-y-beneficios-del-computo-de-alto-rendimiento-en-los-negocios

I hope it serves you, kind regards,

it depends based on your sample characteristics!

Thank you all for your kind support!

So, as I can understand, the sample population must include also potential users.

I think that I will apply a revised TAM, both on actual and potential smartwatch users: it will help me to better understand the differences between these two groups of consumers, as I can evaluate not only users' perceptions but also their actual use of the technology.

Many many congratulations. Really a very good topic of research.

It is not a matter of new or old models, but because of the suitability of the research (research question) with the model, with the variables, with limited research time, or other selected limitations ...

Yes, that is a useful approach to mixed methods. Here is paper that advocates the model you are following.

Dear Mr. Pascucci!

You raised an important issue, so I searched for articles on this topic:

1) Elliot Mbunge et al. (2020). A critical review of emerging technologies for tackling COVID‐19 pandemic, Human behavior and Emerging Technologies, 01 December 2020, Open Access:

2) Christine Manta et al. (2020). An Evaluation of Biometric Monitoring Technologies for Vital Signs in the Era of COVID‐19, CTS, Clinical and Translational Science, Volume 13, Issue 6 November 2020, Open Access:

3) Yushan Siriwardhana et al. (2020). The role of 5G for digital healthcare against COVID-19 pandemic: Opportunities and challenges, ICT Express, 4 November 2020, In Press, Corrected Proof, Open Access;

4) A case-study: Ameer Alhasan et al. (2020). A case-study to examine doctors’ intentions to use IoT healthcare devices in Iraq during COVID-19 pandemic, International Journal of Pervasive Computing and Communications, 9 November 2020, Available at:

Yours sincerely, Bulcsu Szekely

Akwesi Assensoh-Kodua Your answer builds more confidence. Tx

In the past, design was a downstream step in the product development process, aiming to enhance the appeal of an existing product. However, design acquires great importance when function is taken for granted and no longer helps stakeholders differentiate. Nowadays, organizations ask designers to build upstream entire systems that optimize customer experience and satisfaction. And so, it would be useful to research how design might promote technology acceptance across cultures, as Rodney Duffett suggests: design thinking is a protocol that can serve this intent.

Dear Nino Setyo Utomo

You can adopt the TAM theory in the case of adopting most of the previous studies UTAUT, which distinguishes your study as a new scientific contribution in addition to that the field of study plays a large role and it is possible to use the two theories together

Regards

Dear Pereira,

There isn't any problem for doing it. You can use PLS method on small sample due to the non parametric characteristic. I attach a paper where has been used PLS path modeling on a sample of size n= 6 with success.

good luck

Thanks a lot, professor for your valuable suggestions. I will go through them and I hope it will increase my knowledge.

The TAM model is used to explain and predict user acceptance of computer technologies. Basically, if an information system can help people do their jobs better, it is perceived as useful. Accordingly, the basic TAM model defines two variables: perceived usefulness and perceived ease of use, in order to quantify users' attitudes towards information technology. In this context, you can check as a TAM example on this link: https://commons.erau.edu/cgi/viewcontent.cgi?article=1354&context=publication

It is about TAM of the Information Systems Security that can help you to respond to your question.

Also, to be confident about the problem, you can use the independent decision variables but they should have significative ponderations. To determine the ponderation, I suggest using an experimental method like "Delphi" as a methodology for constructing your final model.

Following

You can investigate any kinds of technology and develop a questionnaire based on your case. UTAUT 2 is recommended to use as the latest version of technology acceptance models.

@ Bayes Ahme: could you please share a reference which mentions that nagelkerke R2 value should be between (0.05-0.25) ?

TIA

Affective technologies like social robots must answer appropriately according to context. For example, if the goal is build empathy (towards human acceptance), social robot must imitate the affect state of humans. In any way, affective technologies need recognize humans emotions first. In this context, we development this paper:

I hope it will be useful

Wonderful Rajkumar Rajendram sir

The answer is yes. It applies well in org setting. Look at its recent extensions by tracking Venkatesh's works http://www.vvenkatesh.com/ . Stay away of TAM and TAM2, it's a guaranteed way to get a rejection. They have been over-used in IS research, and much has been written on this in CAIS. Good luck!

As the names states, the Technology Acceptance Model is a model, not a measure.

I advise that you adequately study the UTAUT2 model and see how it fits into your study. I am not sure of the constructs you are using. However, In addition to the construct relationships proposed in the original UTAUT, hedonic motivation and price value can determine behavioral intention. Habit can influence both behavioral intention and usage behavior. Again, facilitating conditions can affect behavioral intention.

So, with regards to whether the model can be used to determine the intention of use (for mobile banking, for example), you can make the hypothesis:

Read this article and adopt:

Regards

Timira Shukla thank you for the answer, but your examples are very irrelevant to my research, unfortunately :/

Mahmoud Abdelrahman Kamel thank you, I will have a look at that!!

Excellent, If you need an example: Take a mobile banking app

PU means the degree of belief that the app would do what it intend to do. High PU means that the person think the app is very effective in terms of performing banking activities.

PEU is the degree of the belief that the app is not difficult to use. High PEU means that the person find the app easy to handle and use.

Bassam A. Alyouzbaky Thank you

Agreed many TAM related research are quantitative-based. But whether to use quantitative or qualitative research is depending on your research problem, research objective or conceptual framework. If you want to move from quantitative to qualitative due to inadequate sample size, then you might also need to reconsider / adjust your research problem, objective, conceptual framework etc. One avenue to do so is pickup one / some of the constructs e.g. perceived usefulness, perceived ease of us, attitude towards using etc. to investigate further why & how these constructs related to actual usage of social media by an organization. Alternatively, depending on your sample size of the respondents e.g. if you only have around 30-50 respondents, you can also consider mixed method's sequential explanatory study i.e. conduct a quantitative research first, then deep dive into certain significant / insignificant constructs via qualitative research to find out why or why not etc. - think this is also a form of knowledge contribution.

Sorry - but you do need his permission and should write him. I'm not sure where you got the idea Val that no permission is needed. But Dr. Davis is very responsive (this was true last week and also 17 years ago when I first emailed him for permission while working on my dissertation.) You can find his email on the Texas Tech University Faculty Directory: http://www.ttu.edu/ In general - I would always recommend emailing the original developer of tools and including the approvals in your appendix. Reviewers will expect to see this. If you plan to make any modifications, include that in your permission request. The only case when permission is not needed is when the instrument has a strong statement as part of the instrument stating that it is open source or free to use without permission etc. This is not usually the case. Thanks.

The UTAUT model ( Unified Theory of Acceptance and Use of Technology ) by Venkatesh (2003) is the most comprehensive and it is often used for measure the acceptance of (innovative) technology.

All these modes of e-commerce are gauged based on their social reach, hence organizations are now vying to connect with their potential customers through networking sites and apps. In India many use Whatsapp, which has a wider reach among the people.

Recommended - http://cdn3.yoox.biz/cloud/ynap/uploads/2017/09/1505295592383_YNAP%20x%20WhatsApp.pdf

Please check if this is helpful.

Perhaps you somehow mix IS and CS scholars (Invormation Systems vs. Computer Science). These are different disciplines ....

One can develop a questionnaire study focusing on a "hypothetical" technology which no one as yet ecperience with. Check with your local IRB.

Dennis

Dennis Mazur

In the Diffusion of Innovation Theory, the main variable for clustering of participants is the time of adoption of innovation.

To do this in your survey, you should have asked participants about the time of adoption. Those who adopted the innovation first (in 2,5% on the left side of the Gaussian distribution), you would put into a cluster of innovators, then next 13,5% of participants you would put into a cluster of early adopters etc. (check the image: https://upload.wikimedia.org/wikipedia/commons/1/11/Diffusion_of_ideas.svg)

If you haven't asked your participants about the time of adoption of the innovation, you should do that to make the clustering.

Here is a tip about a method that might be a resource-efficient valuable contribution during iterative system design: https://invitepeople.com/seminars/9936

(the same process was conducted to a DDI system, see the full report here

https://ki.se/sites/default/files/emma_molin_evaluation_of_medication.pdf)

Hi Frederique,

Your first question is actually two questions:

1a) What score to use following factor analysis, and

1b) How to deal with items that load unexpectedly.

I'm dividing this in two parts: a quick help and then some further suggestions.

Quick help:

1a) As David stated, you should calculate factor scores instead of simply averaging. If you use a simple average you could have saved you the trouble doing factor analysis at all.

I figure you used SPSS - you can easily store factor scores with this programm. There is the button "Scores..." where you can stick with the default "Regression". Remember turning this option off again or SPSS will clutter your data table with factor scores each time you run factor analysis (and one usually runs it multiple times with varying settings. A standard setting apart from SPSS's defaults would be: estimation method: maximum likelihood [refering to Davaids comment]; rotation: promax).

1b) Yes, you should include PS1 (and getting the factor scores as described above will do so automatically).

2) Yes, you should calculate some measure of reliability.

Further suggestions:

1b) You seem to have a clear expectation which items should belong to which cluster. If you have such expectations, you should run confirmatory factor analysis (CFA), not exploratory factor analysis. CFA gives you means of testing the theoretically proposed model. Unfortunately, SPSS itself cannot do this, you would need other software (AMOS, MPlus, lavaan in R).

2) Consider not using Cronbachs Alpha, as it has some strong assumptions about your factor model. A good alternative would be McDonalds Omega. Again, SPSS will not give you this, unfortunately. But McDonalds Omega can be easily calculated from the factor loadings. If you would like to have an Excel sheet helping you with this just let me know here.

Hello,

Are these of any help:

Jaber, O. A. (2016). An Examination of Variables Influencing the Acceptance and Usage of E-Learning Systems in Jordanian Higher Education Institutions (Doctoral dissertation, Cardiff Metropolitan University).

Vogelsang, K., Steinhüser, M., & Hoppe, U. (2013). A qualitative approach to examine technology acceptance.

Money, A. G., Atwal, A., Young, K. L., Day, Y., Wilson, L., & Money, K. G. (2015). Using the Technology Acceptance Model to explore community dwelling older adults’ perceptions of a 3D interior design application to facilitate pre-discharge home adaptations. BMC medical informatics and decision making, 15(1), 73.

But I'm not sure whether these are just what you need.

Very best wishes,

Mary

You can do a google search to find out or refer to the following RG links on the TAM.

Alternatively, you can refer to this article if you can access for the literature review about TAM:

If you want to get some examples of TAM survey questionnaire, you can refer to this RG link:

Perhaps this could help:

Good luck!

Dear Delfina,

If you are interested in strategic decision making process and want to get an overview, I can recommend:

Elbanna, S. (2006). Strategic decision-making: Process perspectives. International Journal of Management Reviews, 8(1), 1–20. http://doi.org/10.1111/j.1468-2370.2006.00118.x

Elbanna, S., Thanos, I., & Jansen, R. J. G. (2017). Delimiting the role of context in the strategic decision making process. Journal of Research in Applied Social Sciences.

Papadakis, V., Thanos, I., & Barwise, P. (2010). Research on strategic decisions: Taking stock and looking ahead. In P. C. Nutt & D. C. Wilson (Eds.), Handbook of decision making (pp. 31–69). Chichester.

Rajagopalan, N., Rasheed, A. M. A., & Datta, D. K. (1993). Strategic decision processes: Critical review and future directions. Journal of Management, 19(2), 349–384. http://doi.org/10.1016/0149-2063(93)90057-t

Shepherd, N. G., & Rudd, J. M. (2014). The influence of context on the strategic decision-making process: A review of the literature. International Journal of Management Reviews, 16(3), 340–364. http://doi.org/10.1111/ijmr.12023

Note that some are book chapters, some can be found on researchgate. For the second paper, which I contributed to, I have attached the link to this post:

Best of luck!

Rob

We recently did a work on evaluating user's vulnerability to share PII in Tinder, here is the paper: https://www.researchgate.net/publication/318234669_Evaluating_User_Vulnerability_to_Privacy_Disclosures_over_Online_Dating_Platforms

Thanks Stephen. 

Thank you, Dr. Stephens.

It is the parsimonious TAM with PU, PEU and BI. Number of respondents 241.

Both of these responses are, in fact, excellent resources. Thank you both.

As Han Ping Fung  says, what you seem to have in mind is known as a Sequential Explanatory Design, which I personally would summarize as QUANT --> qual. As a sequential design, the usual goal is to obtain the results from the core, quantitative phase of the project, and then follow up on them to learn more about how and why those results came out the way they did.

That said, I also agree with Dean Whitehead that you have to tailor your results to your particular research question, and I would add, to your particular research circumstances. For example, if your research is primarily being conducted online, how will you get access to the participants for the qualitative portion of your study and how will you collect the qualitative data?

i believe  it can.   Some of my  works are related to TAM. You can try searching for  my name or my sister's .. Penjira Kanthawongs, Ph.D

Hello Murugan, you should investigate NETWORK THEORY AND ANALYSIS.

The suggested path focus on how relationships influence behavior.

Core Assumptions and Statements

Network analysis (social network theory) is the study of how the social structure of relationships around a person, group, or organization affects beliefs or behaviors. Causal pressures are inherent in social structure. Network analysis is a set of methods for detecting and measuring the magnitude of the pressures. The axiom of every network approach is that reality should be primarily conceived and investigated from the view of the properties of relations between and within units instead of the properties of these units themselves. It is a relational approach. In social and communication science these units are social units: individuals, groups/ organizations and societies.

Other theories you should also investigate are:

▪ Theory of Planned Behavior

▪ Social Presence Theory

▪ Uses and Gratifications Approach

Depending on your approach, you should also check

• Cognitive Dissonance theory

• Elaboration Likelihood Model

The common factor based approach to structural equation modeling (SEM) uses common factors to represent unobserved conceptual variables. Amos is a package for estimating factor-based models. The composite-based approach to SEM uses weighted composites to represent unobserved conceptual variables. SmartPLS is one package for estimating composite-based models. (Besides PLS, consider also generalized structured component analysis or GSCA.) Amos does not estimate composite-based models and SmartPLS does not estimate factor-based models. So the question of "accuracy" does not really apply--the two packages estimate two different models. If you try to use the wrong package to estimate a model, you cannot expect the best results, though there are cases where the results will be similar. Many common arguments related to "factor-based SEM vs PLS" (both pro-factor and pro-composite) are incorrect or at least misleading.

Premature for me to recommend which model but following are some suggestions on the thought process how to choose one:

Compliance is most likely impacted by the trade off between the treatment effect (how patients feel as a result of using the therapy) vs. how invasive/cumbersome the therapy is to use.  

CPAP technology has evolved tremendously as a result of more comfortable masks, quieter, smaller, heated humidifiers, auto-adjusting etc. but compliance remains stubbornly low.  

I argue that it is virtually impossible, given the underlying technology, that CPAP could evolve such that the vast majority of patients who do not recognize a treatment effect would be compliant.

Oral appliances are used much more consistently than CPAP because OAT is less cumbersome even if the efficacy is not as good.  The comfort burden is lower, thus a greater number of patients are willing to use the technology even if the recognized treatment effect is low.

A good test of this theory could be by comparing compliance rates with a new positional therapy technology, one that relies on vibration, to compliance rates for positional therapy approaches that rely on pain (tennis ball) or physical restriction (cushions).   

In a soon to be published paper, we found that compliance rates for vibro-tacctile positional therapy while in the 70-80% range are also affected to some degree by human nature.  Unlike CPAP, patients wore the positional device the entire night, they just didn't wear the device every night.  

In Japan the CPAP compliance rates are much higher than the US, is that because they are, by nature, a more compliant society and as a result more willing to follow the orders of their sleep physician, or the fact that compliance is monitored monthly, and their clinicians are kept in the loop with respect to utilization?  

It could be argued we're asking too much from technology in the quest for compliance, and too little from how we approach delivery of OSA care....

Referring to and using the Strategic Alignment Model would be your best approach. Applying the model will aid you in identifying areas where organizations of your research interest may be overlooking and thus not leveraging. In order to measure adaptability, a generic method would be to measure business and IT alignment. The Strategic Alignment Maturity Model (SAMM) tool would be helpful here. You may be able to customize it, of course with due credit and permissions from Dr.Luftmann. 

I think the following article would help me.

Diamantopoulos, A. (2011). Incorporating Formative Measures into Covariance-based Structural Equation Models, MIS Quarterly Vol. 35 No. 2 pp. 335-358

for my second question, "If it is a multidimensional formative construct, can we use AMOS?"

I have to read in detail.

Hi Adel,

Thank you very much for sending the references, As I have requested some of those you mentioned in your reply from the authors, I have to find and review the others,

Warm regards,

Aarash

Research methodology adopted depending on your research problem, research objective(s) and research question(s).  If the abovementiond are steering your research towards quantitative research using TAM model & you have 1 latent construct which is new after rigorous literature review, perhaps you can develop your own instrument / survey questionnaire.  However, you need to test for its reliability and various validity (content, criterion, construct - convergent / discriminant etc) before using data collected for hypotheses testing.