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The minimum expectation is that studies and 5. It may be that one predictor is less important in their study due to the examined technology type. User socialization in a specific culture impacts masculinity-femininity, and technology perception and use.

National culture may uncertainty avoidance; Table 5. Personal 1. Effect on BI is moderated by individual characteristics. Effect on BI is moderated by country culture. Effect on BI is moderated by technology. Costs 4. Effect on BI is moderated by controls.

Effect on U is moderated by individual characteristics. Conditional effect 6. Effect on U is moderated by country culture. Effect on U is moderated by technology. Unconditional effect 8. Effect on U is moderated by controls. Table 8 summarizes our key recommendations for future research and we elaborate on them here.

First, scholars should examine theoretically meaningful predictors. Our meta-analysis found habit to be the most important predictor among the set of original predictors. Although the literature differentiates between types of user behavior, they have received little attention so far. Users may purchase technologies impulsively due to a sudden urge or use technology because of addiction. Similarly, compulsive behavior is another stream in consumer research that scholars should examine to further extend UTAUT.

Research is also needed on situational predictors of technology use such as whether the user is alone when using technology or whether a friend or family member observes, helps or participates in the technology use. However, scholars should assess whether individual-level predictors impact outcomes of technology use at other levels such as the group e. Similarly, group-level predictors e. Studies should examine cross-level moderation effects as well as multi- level mediation. Such theories may provide IS scholars a deeper and richer portrait of technology use and help linking constructs that were previously unlinked in IS literature like individual-level technology use predictors and organizational- level outcomes such as competitive advantage and firm performance.

These new endogenous mechanisms, i. These four predictors relate to different theories that should be used by scholars to deepen our understanding about technology adoption. For example, more research is needed on the antecedents of the four mechanisms and trait theory is a fruitful way of providing insights on how traits like personal innovativeness form and change. It is important to understand how key traits evolve and which other user traits may have an influence on technology use.

Similarly, more research is needed about the process of habit formation and whether firms can contribute to this process. Fourth, we encourage scholars to extend research on outcomes of technology use.

Most of the collected studies examined intention and use. Thus, it may be interesting to examine non-traditional outcomes emphasized in this literature stream e. Also, future research should examine assimilation, diffusion, and routinization of technology use that are not examined much in studies that have employed UTAUT, although they are reasonably well researched in adoption studies at the organizational level.

It is also worth examining whether the predictors display curvilinear effects on these outcomes, as has been shown with some of the UTAUT predictors on individual-level outcomes e. Fifth, the study of novel mediators and moderators is a promising avenue for future research.

One interesting mediator discussed in recent IS research and related literature is brand equity of the firm e. Technology use may improve brand equity that in turn impacts brand loyalty. Regarding moderators, scholars could draw from theories in related fields. The concept of cross-national differences may be useful in this context. Also, it may be more difficult to communicate the performance benefits of technology in more diverse countries making performance expectancy lose importance as a predictor of use in diverse countries.

Also, we noticed that many UTAUT studies do not report information on voluntariness of technology use. Appropriate reporting and testing of this moderator is essential in order to accurately test the theory and the situational contingency i. Sixth, more research is needed to broaden the conceptualization of predictors and moderators. Scholars should test alternative measurements of these predictors.

For instance, our findings suggest that the four new predictors relate to user characteristics i. Similar extensions should be assessed for other user characteristics. With respect to moderators, we suggest incorporating more research and theories from cross- cultural psychology. While existing research on culture and technology use stress the importance of national culture for understanding user behavior, this research stream would greatly benefit from examining more novel conceptualizations of culture.

These cultural orientations of users may be better suited to explain variance in UTAUT relationships than national culture is—Lenartowicz and Roth , p. Related to this, Triandis and Gelfand distinguished between different individual-level cultural orientations including horizontal and vertical individualism and collectivism. Nowadays, it is also common for users to belong to and to be influenced by more than one culture multiculturalism compared to users who belong to just one culture monoculturalism.

Further, scholars should develop novel technology classifications. Our meta-analysis extends Meuter et al. We encourage scholars to engage in more cross-contextual research by collecting data covering a larger number of technologies and start classifying them given that the classification used in our meta-analysis seemed to be useful in explaining variations in UTAUT relationships.

Although longitudinal studies are proposed, as well as collecting data for behavioral intention and use, not many studies are doing it. Research should assess whether we need a specification of UTAUT for various stages of use beyond the conceptualization of experience and its impact on UTAUT relationships, as reported in Venkatesh et al.

The role of time Venkatesh et al. Finally, scholars are encouraged to use different research designs in their studies. UTAUT would benefit from using a purposeful sampling approach to examine theoretically interesting study participants and technologies not covered in the meta-analysis. Studies could examine the specific characteristics of chat bots and social robots e.

Our findings highlight that the theory is less robust than it is often assumed to be. We assessed the impact of 23 potential extensions using SEM and found UTAUT to benefit from the inclusion of four new endogenous mechanisms from different theories i.

Inclusion of these predictors makes some of the original predictors lose importance. Moreover, we contribute to a better understanding about the generalizability and concomitant contextualization of UTAUT in different contexts by identifying various moderators e. We use the insights gained from this comprehensive synthesis of extant research to arrive at a new UTAUT specification.

Against this backdrop, we present directions for future research that can continue to enhance UTAUT and leverage it meaningfully.

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McKenna, B. McLeod, A. Using technology acceptance theory to model individual differences in tax software use. Miltgen, C. Decision Support Systems, 56, International Journal of Information Management, 34 5 , Saeed, K. JNHFB ; 8 : Introduction It is quite challenging for researchers to stay current on all of if any, for further research. The literature review helps the new and updated information being published in a determine what is already known about a research topic, research area.

Summarizing the findings of a specific how extensively the topic has been researched in the past, research topic in the form of a review can aid researchers and identify key questions about a topic that need further and audiences become more informed on a research topic. Other reasons for conducting a review on a specif- Reviews provide readers the benefit of having summarized ic topic include refining and generating new research ideas, information on a research topic without reading all of the assessing the current state of research in an area and creating published evidence.

Well-conducted reviews often provide awareness, identifying the experts and data sources in a synthesized results that are an excellent source of particular research area, determining the methodologies knowledge for evidence-based medicine and practice. Ultimately, reviews help typically studied by different researchers and findings often research move forward and provide evidence to support vary, which makes evidence-based decisions difficult. Reviews can be of different types and Properly synthesized results from different studies minimize depend largely on the purpose of the review.

In this paper, we will discuss how Review articles vary based on the purpose of the review and the results from different studies can be synthesized through the research question being addressed3.

The most common two of the most common approaches: meta-analysis and types of reviews include literature reviews, critical reviews, meta-synthesis. Our objective is to introduce readers to scoping reviews, systematic reviews, qualitative systematic these two important data synthesis processes with examples. Detailed discussions of the different types of reviews have been What is a review?

A review, commonly known as a literature review, is a process of assessing the existing literature to answer a What is the systematic way of conducting a review? A review should be conducted through maintaining a proper Reviews involve searching the existing literature through a process.

There exist systematic methodological approaches defined process using specific inclusion criteria and for conducting reviews.

Although there are variations in the summarizing findings from the selected literature1, 2. These common rize the existing knowledge on a topic and identify the gaps, steps include identification of a clear research question, performing a comprehensive literature search, conducting a Corresponding Author rigorous screening, extracting data from the selected studies, Tanvir C.

Often, meta-anal- ysis provides a more precise estimate of an outcome than an individual study alone can. Meta-analysis helps summarize findings from many quantitative studies that are often complex and conflicting in nature and plays an important role in evidence based medicine4. Besides pooling the results from multiple studies, meta-analysis also helps with examining the heterogeneity of study results.

Meta-analysis is often considered a subset of a systematic review. It is commonly performed in conjunction with a systematic review, although a systematic review need not contain a meta-analysis4. Process of conducting meta-analysis with example Meta-analyses are mostly conducted after systematic reviews. The process associated with systematic reviews is also applicable to meta-analysis.

We discuss below the key steps of performing a meta-analysis on quantitative studies [Box 1]. Box 1. Meta-analysis process on quantitative studies. Figure 1. Process of conducting a systematic review. Depth of synthesis in reviews The nature of a review also depends on the depth and amount of information to be synthesized from the selected studies [Figure 2]. Synthesizing information from different studies can be broadly classified into two categories based on the type of study being used: quantitative or qualitative.

Quantitative studies are synthesized through a process called meta-analysis, while qualitative studies are synthe- Step 1. Frame the research question sized through meta-synthesis. In this article, we will discuss As with systematic reviews, a good meta-analysis is these two very important types of synthesizing processes characterized by a thorough and disciplined literature search that represent the deepest level of data synthesis.

The research question should be clear, and there should be a specific purpose for conducting the meta-analysis. Step 2. Comprehensive search to identify the relevant quantitative studies To identify the relevant studies associated with the research question, a comprehensive search is performed on different databases within a parameter of time using a set of key words related to the research question.

To provide a comprehen- Figure 2. Reviews in relation to the depth of synthesis. Step 3. Screening the quantitative studies Step 7. Usually, two assessors prevalence from different studies, assessing if any independently decide which studies to include or exclude. If heterogeneity exists among the studies, and evaluating a study is excluded from meta-analysis, reasons should be publication bias.

While performing a meta-analysis, the first given. There are no standard criteria for inclusion-exclusion thing to decide is the type of model to use for the analysis.

These models have Screening is generally performed in two steps: title and different underlying assumptions, and investigators need to abstract screening as a first step followed by full-text decide which model to use in performing the meta-analysis. Different software with default commands is available to perform the meta-analysis e.

Software provides the results of the Once the final papers have been selected, pre-determined analysis both in tabular and graphical form. A forest plot is data are extracted on which the meta-analysis is performed. Figure 3 shows a forest plot of estimate of association between exposure and outcome. In prevalence of cardiovascular disease CVD in the this scenario, the measures of association, such as odds ratio Bangladeshi population, a meta-analysis conducted by OR or risk ratio RR , are extracted from the selected Chowdhury et al Meta-analysis is also conducted on measures of disease burden like prevalence or proportions.

In this scenario, the prevalence estimates are extracted from the selected studies6. In addition, meta-analysis can also be conducted for model-performance parameters. Step 5. Depending on the type of study and subject matter, different checklists exist from which to assess quantitative study quality, and investigators need to choose the appropriate checklist for their study.

Table 1. Quantitative study quality assessment tools Figure 3. From Chowdhury MZI et al. Prevalence of cardiovascular disease among Bangladeshi adult population: a systematic review and meta-analysis of the studies. Vasc Health Risk Manag. Step 8. Assess study heterogeneity One significant feature of performing a meta-analysis is it allows investigators to examine sources of heterogeneity, if present, among studies.

There could be several sources of heterogeneity, and identifying sources of heterogeneity often leads to more effective targeting of prevention and treatment strategies and helps generate new hypotheses about a research topic. Step 6. Summarized information is generally present- Inconsistency index I2 are the two major ones. Sub-group ed in tabular form for presentation. Investigators need data across studies by applying an objective formula and to be cautious when interpreting the summary results from coming up with a single estimate that is often more precise meta-analysis when heterogeneity exists.

Combining individual studies allows more data to use in estimating the results more precisely and accurate- Step 9. Assess publication bias ly along with a greater statistical power to detect an effect. Meta-analysis helps identify publication bias in studies. Generalizing the results from a meta-analysis makes more There is a tendency to publish large studies that contain sense than those from single studies, as the process incorpo- significant positive results.

Small studies with non- rates different sets of populations into the analysis and thus significant results are often ignored and are not published. A funnel plot, a graphical way of evaluating publication bias, is perhaps the most Case Study: synthesizing quantitative studies through popular method. Figure 4 provides an example of a funnel meta-analysis plot where the authors assessed publication bias of the Chowdhury MZI et al They summarized and synthesized information on the prevalence of CVD from all published scientific literature through a systematic review and meta-analysis.

It was a quantitative study effect measure was prevalence and had numerical value and the authors used meta-analysis to synthesize the information. The authors clearly stated their research question to assess the prevalence of CVD ; undertook a proper search strategy using a set of key words in three databases MEDLINE, EMBASE, and PubMed and in the grey literature; selected studies based on a set of inclusion-exclusion criteria; assessed study quality using an appropriate checklist; summarized the information; and lastly synthesized information through meta-analysis.

Funnel plot for publication bias. Reused under the assessed sources of heterogeneity through a stratified Creative Commons Attribution - Non Commercial analysis and meta-regression; and assessed publication bias unported, v3. Prevalence of cardiovascular disease among Bangladeshi adult population: a systematic review and metaanalysis of Methodological Overview - Meta-synthesis of qualitative the studies. Interpreting results and reporting findings qualitative studies on a specific topic and translate results This is the final step of meta-analysis and consists of into one interpretation that leads to a deeper and more presenting the research findings that emerged through the complete understanding of the topic Synthesizing the process of quantitative meta-analysis and interpreting the findings from a group of selected qualitative studies, as results.

Readers often fail to Benefits of conducting meta-analysis distinguish the term meta-synthesis from meta-analysis. The Meta-analyses are preformed to assess the strength of two terms are different and serve different purposes. The evidence that exists regarding a research question. General- purpose of meta-analysis is to collect, aggregate, and ly, meta-analyses are performed to produce an overall summarize quantitative studies and express the summarized estimate of an effect and measure the precision of that estimate based on multiple studies.

One key objective of results in a common and standardized numerical value meta-analysis is to obtain a single summary estimate of an i. Validity of a research question or findings from the qualitative studies. Meta-analysis often hypothesis is sometimes hard to justify based on the results helps determine cause and effect inferences, while of a single study, as results typically vary from one study to meta-synthesis focuses on examining a deeper the next.

Meta-analysis provides a mechanism to synthesize understanding of the meaning of a specific topic. Process of conducting meta-synthesis with example performed.

This appraisal often determines whether a study The meta-synthesis process consists of several steps that should be included in the final synthesis. Studies often vary help researchers identify a specific research question and in terms of their quality, with some studies being weak. Generally, these evidence from multiple studies We discuss below the key criteria are based on comparison parameters such as a clear steps of conducting a meta-synthesis on qualitative studies research question and purpose, an appropriate methodologi- [Box 2].

There are Box 2. Meta-synthesis process on qualitative studies. Some of those checklists are more prescriptive and comprehensive than others, despite some overlap. Performing an appraisal of qualitative studies is not easy because the methodological approaches of these studies are quite diverse and difficult to judge. There is also debate as to whether quality criteria should be applied in qualitative research, and there is no consensus on which criteria to use and how to apply them.

Table 2.