1. Introduction

In today's dynamic and highly competitive context, organizations should be "active actors" and capable to adapt to environmental changes "at least to some extent, mainly within the limits of its resources and capabilities" (Makkonen et al., 2014, p. 2707). Sensing and seizing opportunities, as well as taking initiatives to avoid potential threats, is imperative (Teece, 2007). To do so, organizations need to overcome the inertia and to promote the continuous change of their resource base (Makkonen et al., 2014).

Based on the resource-based view (RBV) framework, the perspective of dynamic capabilities (DCs) has emerged to explain how organizations can develop valuable, rare, inimitable and Nonsubstitable attributes (VRIN) resources on dynamic environments (Eisenhardt and Martin, 2000; Teece et al., 1997).

The DCs view focuses on the capacity to survive in dynamic environments by creating new resources and by renewing or changing the resource base (Bowman and Ambrosini, 2003). DCs involve routines and processes that are implemented to reconfigure the resource base in order to adapt to markets as they evolve (Eisenhardt and Martin, 2000). DCs enable organizations to integrate, reconfigure, and recombine their resources in timely manner in order to adjust to environmental changes and demands (Teece et al., 1997).

Despite the increasing relevance of the concept of DCs on strategic management research field and the great amount of theoretical studies on the subject, various authors have criticized this theory for being tautological, difficult to operationalize (Priem and Butler, 2001; Williamson, 1999) and difficult to be measured empirically (Easterby-Smith et al, 2009). As a result, there are few reliable empirical studies regarding dynamic capabilities. Authors plead that empirical studies on DCs are too abstract (Ali et al., 2012).

We defined two research questions:

RQ1. What is the context in which quantitative studies on dynamic capacities are developed?

RQ2. Which criteria are considered to ensure the reliability and validity of the scales?

For this reason, this research aims to identify the existing measure instruments for DCs in order to understand the context of quantitative studies on dynamic capabilities as well as to assess the reliability and validity of these scales. To accomplish this objective, we conducted a systematic review of literature on dynamic capabilities.

As literature indicates, DCs is a fundamental asset to get and sustain competitive advantage, as they allow organizations to rearrange their resources and process according to environment changes and demands (Eisenhardt and Martin, 2000; Teece et al., 1997). Based on these arguments, we believe that this research is relevant for strategic management research field, as it identifies and valuate the reliability of measure instruments that have been used to measure DCs.

Main findings indicate that quantitative researches on DCs have focused on the contexts of innovation, knowledge (other related aspects of knowledge such as absorptive capacity and organizational learning), strategic alliance, relationship with stakeholders (partners, customers, suppliers), organizational capacity and brand.

Findings also show that the initiatives to measure DCs are very recent: out of the 42 analyzed instruments, 38 were published in the 2010's.

Regarding the reliability and validity of the scales, results indicate that quantitative researches on DCs lack more rigorous methodological procedures regarding scale development. As we analyzed the methods of the 42 articles according to the study of Slavec and Drnovesek (2012), we realized that the majority of quantitative studies have not accomplished all recommended steps for scale development.

Even though researchers are aware of the importance of measure reliability and validity, findings show that the majority focused more on the amount of the sampling data than on building an accurate and reliable instrument to measure the object of study.

This research can help researchers as it provides an extensive analysis of existing scales on DCs which can be adopted in future studies. Besides, researchers can make use of research findings by focusing on perspectives of DCs that still lack reliable quantitative studies. Results show that academicians have opportunity to develop rigorous and more accurate empirical studies.

Besides this introduction, this paper presents the theoretical background on DCs, a chapter describing the methodology adopted in this research, the analysis and discussion of research findings and authors' final considerations.

2. Theoretical basis

DCs can be understood as an extension of the RBV on strategic management (Eisenhardt and Martin, 2000). Teece et al. (1997) apply the influence of the dynamism of markets in the theory of RBV perspective. In their view, resources evolve over time in order to adapt to market changes.

The perspective of DCs has emerged to explain how organizations are able to survive and to keep leadership in unstable environments by rearranging competences, assets and abilities, which was not covered by the RBV perspective. For this reason, the framework of DCs can be considered an extension of RBV as it addresses some of the limitations of its antecessor (Ambrosini and Bowman, 2009; Bowman and Ambrosini, 2003).

For Teece et al. (1997, p. 515), a DC "refers to the capacity to renew competences so as to achieve congruence with the changing business environment." These authors emphasize that DCs play a fundamental role on strategic management as they enable organizations to adapt, to integrate and to reconfigure their internal and external resources to respond to changes in the environment.

Teece et al. (1997) and Eisenhardt and Martin's (2000) highlight the impact of environment on organization performance as well as the necessity to adapt to environment in order to sustain competitive advantage. Both papers attest that DCs are related to unstable environments; while other authors, such as Ambrosini and Bowman (2009), point out that DCs can also be developed in stable environments, as they are not about the dynamism of the environment, but about organization's capacity to adapt to environmental changes.

For Eisenhardt and Martin (2000), DCs are sufficient to achieve sustainable competitive advantage. Teece (2007, p. 1344) corroborates this position as he affirms that "if an enterprise possesses resources/competences but lacks DCs, it has a chance to make a competitive return (and possibly even a supra-competitive return) for a short period; but it cannot sustain supra-competitive returns for the long term except due to chance" (Teece, 2007, p. 1344). To sustain competitive advantage, organizations need to pursue the constant renewal of DC's as well as to be able to identify valuable resources faster than its competitors (Collis, 1994). This constant renewal of DCs and organization's resource base can be factors leading to innovation (Teece, 2007).

3. Methodology

This paper follows a qualitative methodological process with the objective to explore scales of DCs. As mentioned above, the objective of this research is to identify the existing measure instruments for DCs in order to understand the context of quantitative studies on DCs as well as to evaluate the reliability and validity of these scales.

To accomplish this objective, we conducted a systematic review of literature regarding DCs. Systematic (literature) review consists of using systematic methods to review studies on a specific theme in order to identify and evaluate the relevant studies on a specific theme (Petticrew and Roberts, 2006).

Following Tranfield et al's (2003) proposed model of systematic literature review (SLR), we did a set of steps to conduct the SLR in three proposed stages: planning the review; conducting the review; reporting and disseminating. Figure 1 shows the main steps of our protocol.

We defined two research questions to be answered by the SLS:

RQ1. What is the context in which quantitative studies on dynamic capacities are developed?

RQ2. Which criteria are considered to ensure the reliability and validity of the scales?

In this SLR, we extracted data from two databases, Web of Science (WoS) and Scopus. To extract articles on DCs from WoS (step 3), we used the keywords "DCs" and "scale."

Then, we filtered the search result using research categories. In this filter, we kept only the articles from management and business research categories. Then, we did another extraction on WoS using keywords "DCs" and "quantitative." To filter this result, we did the same procedure as we did on the first extraction. After this refinement process, it remained 146 articles on the extraction from WoS. On Scopus (step 4), we performed a similar process as we did on WoS. We did two extractions; one using keywords "DCs" and "scale," and the other using keywords "DCs" and "quantitative." To refine the search result on Scopus, we filtered it by selecting articles from "business, management and accounting" research area. In total 162 articles were extracted from Scopus database. It is important to note that both searches included only published or "in-press" articles.

After the extraction, we searched for possible duplicate papers. In this step, 23 papers were excluded from analysis.

Afterwards, we analyzed the abstract, keywords and the indexed keywords of these remaining 285 articles (step 6). In addition, we analyzed their methodology (step 7) to evaluate the methods applied in development of the measure instruments.

To assess the reliability and validity of these scales on DCs, we chose Slavec and Drnovesek's (2012) paper in which we found a consistent and detailed review of scales published in entrepreneurship journals during the years 2009 and 2010. We, then, used the steps of scale development described by Slavec and Drnovesek (2012) to assess the procedures authors used to develop their measuring instruments.

Founded on the classical Churchill (1979) article, Slavec and Drnovesek (2012) propose a ten-step procedure to develop a new scale. These then steps were grouped into three stages: "(1) theoretical importance and existence of the construct, (2) representativeness and appropriateness of data collection, and (3) statistical analysis and statistical evidence of the construct" (Slavec and Drnovesek, 2012, p. 53). Figure 2 illustrates the three-stage procedure for scale development.

In the stage of theoretical importance and existence of the construct, there are three steps: content domain specification (CDS), item pool generation and content validity evaluation (CVE). As you can see in Figure 2, the stage of representativeness and appropriateness of data collection consists of four steps questionnaire development and evaluation, translation and back-translation of the questionnaire, pilot study (PS) performance, and sampling and data collection (Slavec and Drnovesek, 2012). Finally, the stage of statistical analysis and statistical evidence of the construct contains four steps: dimensionality assessment, reliability assessment and construct validity assessment (CVA).

4. Results and discussion

As mentioned above, we analyzed the abstract, keywords, introduction and methodology sections of the selected articles. It is important to mention that in some instances this analysis also included reading the theoretical background and references sections, since occasionally keywords and abstracts did not depict overall content of the papers. For example, even though some articles contained the construct of DC, authors preferred to refer to DCs as the "dynamic perspective on RBV." In this analysis processes, we found 42 measure instruments for DCs.

We divided our analysis into two parts. The first half is related to the first research objective: to understand the context of quantitative studies on DCs. The second half refers to the assessment the reliability and validity of these scales. Table I presents the 42 selected articles and details regarding their context and research objective.

It is important to mention that even though articles were grouped into one specific context, many of them address more than one context. However, to facilitate readers' visualization of findings tabulation, we chose the context which got more emphasis in the study. On top of that, there is a strong interrelation within these contexts which implies that the multidimensional role of DCs on rearranging organizations resources (Teece, 2007; Teece et al, 1997).

As we can see in Table I, quantitative studies on DCs have gained importance on different contexts of organizational life. Within the most cited papers, we find quantitative studies on absorptive capacity (Camison and Fores, 2010 with 411 citations), knowledge (Jantunen, 2005 with 368 citations), and strategic alliance (Lin and Wu, 2014 with 231 citation). It is worth mentioning that the article of Lin and Wu (2014) has gained a great amount of citations in a short period of time.

Regarding the context of DCs, findings shows that quantitative studies on DCs have focused more on four contexts of organizational life: governance (eight articles), innovation (eight articles), knowledge (seven articles), and relationship with stakeholders (ten articles distributed in relationship with customers, relationship with partners, and relationship with suppliers).

An important insight provided by the analysis is that knowledge has a strong correlation with DCs. Besides the eight articles that focused on the context of knowledge, we found other contexts which are very connected with knowledge: absorptive capacity (three articles) and organizational learning (3). That corroborates the argument found in the seminal work of Teece et al. (2007) that says that the ability to recognize opportunities depends on organization's and its members knowledge and learning capacity.

The number of scales (42 out of 285 articles) can be explained by the fact that DCs are difficult to be measured empirically (Easterby-Smith et al., 2009). The difficulty to measure DCs are comprehensible as DCs are strongly related to internal organizational processes (Helfat and Peteraf, 2003; Teece, 2007) which, in turn, are complicated for researchers to identify and to measure empirically.

As we analyzed the main objective of the articles, we noticed that a great amount of the instruments aim to measure the relationship between DCs and some sort of innovation (12 out of 42 articles). This finding is corroborated as we counted the words contained in the abstracts of these articles. In total, the word "innovation" is mentioned 86 times. Figure 3 illustrates the word frequency of the 42 abstracts.

Another interesting finding is that a considerable amount of the select articles (14 out of 42) aim to measure the influence of DCs on some aspect of organization performance--i.e. portfolio performance (Biedenbach and Muller, 2012), customer-oriented organizational performance (Desai et al., 2007), innovation performance (Plattfaut et al., 2015). Even though some argue that the relationship between DCs and organizational performance is difficult to measure (Easterby-Smith et al., 2009), we could observe an increasing interest of researchers on investigating this perspective of DCs. This finding is corroborated by the word frequency of the abstracts--word "performance" is mentioned 94 times (see Figure 2).

In fact, findings indicate that initiatives to develop measure instruments for DC's are recent. Out of the 42 selected measure instruments, 38 were published in the 2010s.

This finding is understandable, since the seminal works of this theory were published between the end of the 1990s and the beginning of the 2000s (i.e. Eisenhardt and Martin, 2000; Teece et al., 1997; Winter, 2003).

As mentioned in the methodology section, to evaluate the validity and reliability of the scales on DCs, we adopted the criteria proposed by Slavec and Drnovesek (2012). We analyzed the methodology adopted by the authors according to the three stages of scale development: theoretical importance and existence of the construct, representativeness and appropriateness of data collection and statistical analysis, and statistical evidence of the construct (Slavec and Drnovesek, 2012).

As we analyze Table II, we can see that only 12 articles (out of 42) followed all the steps of scale development according to Slavec and Drnovesek (2012).

Again, we analyzed the methodological procedures according to our interpretation of Slavec's and Drnovesek's (2012) study. Another important point is that as we analyzed the process of scale development, we verified if the step of translation and back-translation was applicable or not. In most cases, this step was not necessary. Besides that, some studies do not clearly mention the procedures regarding specific steps of scale development. For instance, in the study of Agarwal and Selen (2013), authors do not report the procedures they conduct to develop and evaluate the questionnaire.

Within the 12 reliable and valid instruments, five received at least 60 citations according to Google Scholar: Kandemir et al. (2006), Lin and Wu (2014), Mitrega et al. (2012), Jin et al (2014) and Cheng and Chen (2013).

Within the 42 scales, there are 15 with more than 60 citations. An intriguing finding shows that, within these highly cited papers, ten are not completely reliable and valid according to Slavec and Drnovesek's (2012) criteria. Yet, the scale development process found on these papers follows most of the needed steps for scale development. For instance, Camison and Fores (2010) only omitted the step of CVE; Herrmann et al. (2007), the step of CDS and PS; Santos-Vijande et al. (2013) and Zheng et al. (2011), the step of conducting a PS.

As we analyze the reliability and validity of these 42 instruments, we noted that the steps of scale development that are overseen or not reported more often are CVE (21 articles), CDS (15 articles), PS (16 articles) and CVA (7 articles).

CVE involves getting knowledgeable people to reviewing the scale items. Slavec and Drnovesek (2012) recommend researchers to ask experts (academicians, experienced practitioners) to evaluate the instrument to propose changes. According to research findings, half of authors (21) have neglected this important step. Getting advices from experts minimizes deviations and misconceptions of measurement items, especially regarding the construct of DCs which is too abstract and difficult to evaluate (Ali et al., 2012; Easterby-Smith et al, 2009).

CDS refers to defining what is going to be measured (DeVellis, 2003). Slavec and Drnovesek (2012) suggest researchers to conduct literature reviews and/or exploratory qualitative researches in order to define and delimitate the construct that will be quantitatively evaluated. The fact that many authors have missed this step can indicate a warning regarding empirical studies on DCs. As the construct of DCs remains ambiguous and difficult to identify on organizational settings (Ali et al., 2012), researchers should be more careful as they develop scales to measure it. Otherwise, researchers may develop instruments that will not measure the phenomenon as expected.

PS refers to engaging on a PS with a sample of the target population in order to collect critics, suggestions and thoughts, as well as to prevent possible problems such as semantic issues or misspelling. As findings show, 16 papers authors did not conduct this step nor reported it on their methodology.

CVA refers to the extent to which the scale measures what it is intended to measure in the setting that it will be used (Slavec and Drnovesek, 2012). In our analysis, seven papers have not accomplished this requirement. In some cases, authors do not clearly describe the statistical procedures they conduct during scale development. In these cases, we considered that specific methodological step as "not reported." There are papers in which the description of the statistical procedures is ambiguous and insufficient. For instance, Biedenbach and Muller (2012) use the term unrotated factors analysis, but do not mention if they used exploratory factor analysis (EFA) or confirmatory factor analysis (CFA). In the same manner, Sprafke et al. (2012) present an obscure description of statistical procedures used in the research.

5. Conclusions

The perspective of DCs has emerged to explain how organizations can develop competitive advantage on dynamic environments (Eisenhardt and Martin, 2000; Teece et al., 1997). Despite the increasing interest of the academia on DCs, the empirical studies on DCs are few, not as reliable, too abstract and limited to case studies (Ali et al., 2012). For this reason, this research aims to identify the existing measure instruments for DCs in order to understand the context of quantitative studies on DCs as well as to assess the reliability and validity of these scales. To accomplish this objective, we conducted a systematic review of literature on DCs.

Main findings indicate that quantitative researches on DCs have focused on the contexts of brand innovation, knowledge (other related aspects of knowledge such as absorptive capacity and organizational learning), strategic alliance, relationship with stakeholders (partners, customers, suppliers), organizational capacity and brand.

Findings also show that the initiatives to measure DCs are very recent: out of the 42 analyzed instruments, 38 were published in the 2010's.

Regarding the reliability and validity of the scales, results indicate that quantitative researches on DCs lack more rigorous methodological procedures regarding scale development. As we analyzed the methods of the 42 articles according to the study of Slavec and Drnovesek (2012), we realized that most of quantitative studies have not accomplished all recommended steps for scale development.

Even though researchers are aware of the importance of measure reliability and validity, findings show that the majority focuses more on the amount sampling data than on building an accurate and reliable instrument to measure the object of study.

Finally, results show that academicians have a good opportunity to develop rigorous and more accurate empirical researches on DCS. Academicians need to develop more reliable and valid instruments to measure this important aspect of strategic management.

A limitation of this research is that we have not analyzed in which perspective these 42 instruments were used. Another limitation is that the analysis of reliability and validity of these instruments is based on our interpretation of Slavec and Drnovesek's (2012).

For future studies, we suggest researchers to compare the relationship between qualitative studies and quantitative studies on DCs. By analyzing the similarities and differences of context on qualitative and quantitative studies on DCs researchers can identify the most used methods in both research approaches as well as which research approach is more appropriate according to the context that DCs is analyzed.

This paper was funded by the CNPq project entitled "Exploring the Role of Customer Relationship Management in Organizational Innovation Capability," under Grant No. 459491/2014-8.

DOI 10.1108/REGE-12-2017-0021

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Zahra, S.A. and George, G. (2002), "Absorptive capacity: a review, reconceptualization, and extension", The Academy of Management Review, Vol. 27 No. 2, pp. 185-203.

Zheng, S., Zhang, W., Wu, X. and Du, J. (2011), "Knowledge-based dynamic capabilities and innovation in networked environments", Journal of Knowledge Management, Vol. 15 No. 6, pp. 1035-1051.

Further reading

da Silva, D. and Simon, F.O. (2005), "Abordagem quantitativa de analise de dados pesquisa: construcao e validacao de escala de atitude", Cadernos CERU, Vol. 2 No. 16, pp. 11-27.

Zollo, M. and Winter, S.G. (2002), "Deliberate learning and the evolution of dynamic capabilities", Organization Science, Vol. 13 No. 3, pp. 339-351.

Received 2 January 2018

Revised 21 June 2018

Accepted 24 June 2018

Corresponding author

Cintia Cristina Silva de Araujo can be contacted at: cintyaraujo@gmail.com

Cintia Cristina Silva de Araujo

Administration Graduation Program, Universidade Nove de Julho, Sao Paulo, Brazil

Cristiane Drebes Pedron

Universidade Nove de Julho, Sao Paulo, Brazil, and

Claudia Bitencourt

Universidade do Vale do Rio dos Sinos, Sao Leopoldo, Brazil

Caption: Figure 1.

SLR main steps

Caption: Figure 2.

Ten steps and three stages for scale development

Caption: Figure 3.

Wordcloud designed based on the abstract of the 42 selected articles

Table I.

Measure instruments
for DCs found in the
systematic review
with their respective
context on DCs

Context           Research objective

Innovation        To evaluate how technological
                  governance affects dynamic
                  capability of innovation and
                  cooperation on Brazilian
                  multinationals

                  To propose a model to identify
                  the antecedents of radical
                  product innovation

                  To operationalize specific
                  dynamic capabilities for service
                  innovation, based on Teece's
                  (2007) framework

                  To develop and test a
                  theoretical framework that
                  explains how information
                  technology can contribute to
                  service innovation
                  performance. The framework is
                  based on the dynamic
                  capability theory of Teece
                  (2007)

                  To study innovation capability
                  in the context of export market.
                  Authors also intend to develop
                  a scale to measure innovation
                  capability in exporting
                  organizations. The name of the
                  scale is the INNOVSCALE
                  To examine the relationship
                  between dynamic capabilities
                  (DCs) and technological
                  innovation capabilities as well
                  as to analyze the impact of
                  technological innovation
                  capability on organization's
                  competitiveness. The research
                  was conducted among Iranian
                  large public organizations

                  To analyze and assess the
                  cumulative effect of dynamics
                  capabilities on service
                  innovation

                  To examine relationship
                  between dynamic innovation
                  capabilities and open
                  innovation activities in
                  breakthrough innovation

Organizational    To examine the effect of
learning          organizational learning
                  capability on export intensity
                  and product innovation
                  To build a multidimensional
                  instrument to measure strategic
                  learning process

                  To develop a measurement
                  scale of dynamic learning
                  capabilities

Brand             To develop a multidimensional
                  scale to measure brand
                  management systems in three
                  dimensions: brand orientation,
                  internal branding and strategic
                  brand management. Besides,
                  authors conceptualize brand
                  management system as a
                  dynamic capability

Relationship/     The objective of the paper is to
customer          analyze and to identify the
                  drivers of dynamic capabilities
                  that improve CRM processes in
                  order to achieve customer-
                  oriented organizational
                  performance

                  To analyze the effects of export
                  market exploitation and
                  exploration on export
                  performance

                  To propose a scale to measure
                  organization's capacity to
                  introduce new products and
                  services based on customer
                  knowledge management

Relationship/     To study the role of logistics
supplier          capabilities on supply chain
                  agilities under the dynamic
                  capability perspective of RBV

                  To analyze the relationship
                  between supply chain flexibility,
                  competitive performance and
                  IT-enabled sharing capabilities.
                  Authors denote that IT-enables
                  sharing capabilities comprise
                  the organization's capability to
                  use IT infrastructure to deal
                  with intangible information and
                  to build a network to share
                  information internally and
                  externally

                  To analyze how organizations
                  can increase customer value
                  creation by exploring
                  relationships with supply chain
                  partners, by building internal
                  integration and by developing
                  the dynamic capabilities in
                  order to respond to customer
                  demands. Authors analyze this
                  phenomenon by applying the
                  theory related to relationship
                  marketing and the dynamic
                  capability perspective of RBV

                  To study the role of business
                  intelligence in supply chain
                  agility context by analyzing the
                  relationship between business
                  intelligence, competence, agile
                  capabilities and supply chain
                  agility

                  To theorize and validate a
                  model that addresses the
                  Triple-A (agile, adaptable,
                  aligned) supply chain as an
                  antecedent of supply chain
                  performance, and supply chain
                  performance as antecedent of
                  organizational performance

                  To examine the management of
                  supply chain and innovation.

                  Another objective is to analyze
                  the relationship between
                  strategic supply chain, supply
                  chain capability and industry-
                  led innovation

Relationship/     This study proposes the
partners          construct of networking
                  capability (NC) as a dynamic
                  capability. To accomplish this
                  goal, authors proposed and
                  tested a model

Strategic         To investigate the influence of
alliance          dynamic capabilities on
                  organization's capacity to
                  develop valuable, rare,
                  inimitable and non-
                  substitutable resource in the
                  pursuit of better performance.
                  To achieve this objective,
                  authors employed a survey
                  with 1,000 Taiwanese
                  companies

                  To demonstrate that
                  organization's orientation to
                  alliances can help it to scan the
                  environment for better
                  opportunities which can result
                  on new partnerships and better
                  alliance strategies

Knowledge         To study how absorptive
                  capability of processing
                  organizational knowledge
                  impact innovative performance

                  To analyze the role of
                  knowledge management by
                  focusing on knowledge
                  management practices and on
                  the dynamic capabilities
                  oriented to knowledge
                  management

                  To examine the impact of
                  communication on network
                  relationships and organization
                  performance

                  To analyze the relationship
                  between dynamic capabilities
                  and environmental crisis as
                  well as to study how
                  organizations use dynamic
                  capabilities during unstable
                  periods. This study was
                  conducted under the
                  perspective of the financial
                  crisis of 2008

                  To analyze the manufacturing
                  strategy process (MSP) under
                  the perspective of RBV

                  To develop of a
                  multidimensional scale to
                  measure the individuals'
                  market-oriented behavior in
                  organizational settings

                  To understand the concept of
                  dynamic capabilities from a
                  knowledge-based perspective
                  and to assess the impact of
                  dynamic capabilities on
                  innovation performance

Absorptive        To analyze the relationship
capacity          between absorptive, innovative
                  and adaptive capabilities on
                  project and portfolio
                  performance of R&D projects
                  on pharmaceutical and
                  biotechnology organizations

                  To measure the impact of
                  absorptive capabilities on
                  knowledge management

                  To examine the relationship
                  between organization's
                  openness, absorptive capacity
                  and innovation capability in the
                  in-bound open innovation
                  environment

Operational       To validate an instrument that
capability        measures second-order
                  competences (capabilities). The
                  scale is based on the tripod of
                  sensing, seizing and
                  reconfiguring proposed by
                  Teece (2007)

                  To study the role and definition
                  of operational capabilities as
                  well as to identify the difference
                  between operational and
                  dynamic capabilities. Authors
                  also aimed to develop a
                  measurement instrument of
                  operational capabilities

Governance        To measure the mediating role
                  of organizational capabilities on
                  the relationship between middle
                  managers, middle managers'
                  autonomy and organizational
                  performance

                  To propose technical
                  turbulence as a primary
                  contingency factor in the
                  relationship between strategic
                  orientation and firm
                  performance. Author analyzes
                  thy phenomenon under the
                  perspective of resource-based
                  view (RBV)

                  To analyze the process of
                  capability development in
                  project management settings

                  To propose the idea that
                  individual, managerial and
                  team-related initiatives directly
                  impact dynamic capabilities
                  To measure the impact of the
                  chief marketing executives'
                  mindsets on marketing
                  capabilities as well as the
                  impact of marketing
                  capabilities on performance

                  To evaluate if portfolio
                  management governance
                  enhances firm performance.
                  Authors conduct the study
                  based on the dynamic
                  capability perspective of
                  resource-based view

                  To examine whether the
                  heterogeneity in alliance
                  capability development can be
                  attributed to some specific
                  leadership behaviors. The
                  research also intends to confirm
                  that transformational
                  leadership has positive
                  influence on the development of
                  some strategic dynamic
                  capabilities. Besides, the
                  research aims to test if
                  transformational leadership
                  allows organization to sustain
                  operational capabilities

                  To study how dynamic
                  capabilities of sensing, seizing
                  and reconfiguring are
                  developed in organizations and
                  how they relate to each other

Context           Details

Innovation        The scale evaluates aspects of
                  dynamic capabilities related to the
                  organization's capability to rearrange
                  existing resources and its capability
                  to create new resources

                  The scale measures the impact of
                  dynamic capabilities on the
                  transformation of product and
                  services as well as on the
                  transformation of markets on radical
                  product innovation

                  The scale measures the dynamic
                  capabilities and their impact on
                  service innovation. The scale items
                  are structured according to the three
                  classes of dynamic capabilities
                  (sensing, seizing, transformation)
                  (Teece, 2007)

                  The scale measures how dynamic
                  capabilities of sensing, seizing and
                  transforming can influence service
                  innovation performance. In this
                  study, service innovation
                  performance is considered a dynamic
                  capability as well

                  In the scale focus on new product
                  development. Authors designed the
                  scaled base on the work of Calantone
                  et al. (2002). The scale also strategic
                  capability, technological capability
                  and investments on R&D initiatives

                  The scale measures the relationship
                  between dynamic capabilities and
                  innovation capabilities. The items
                  that measure dynamic capabilities
                  are based on Teece's (2007)
                  framework. The items that measure
                  innovation capability cover
                  capabilities related to organizational
                  learning, R&D, resource allocation,
                  manufacturing, marketing,
                  organizing and strategic planning

                  The scale evaluates dynamic
                  capabilities on network
                  environments. It also evaluates the
                  DCs oriented toward organization's
                  relationship with partners, the DCs
                  for organizational learning and the
                  DCs of innovation capability

                  Authors designed the research as
                  well as the measurement instrument
                  from the absorptive capacity
                  perspective and also based on
                  organizational inertia theory, and
                  open innovation. It is worth
                  mentioning that authors set
                  innovation capability as a dynamic
                  capability

Organizational    The scale evaluates organization's
learning          interaction with the environment and
                  the effect of this interaction on
                  organizational learning capability
                  The scale measures strategic learning
                  process which is divided in four sub-
                  processes: strategic learning creation,
                  distribution, interpretation and
                  implementation. The scale measures
                  strategic learning as a dynamic
                  capability

                  The scale measures dynamic
                  capabilities on the perspective of
                  dynamic learning capabilities. The
                  scale also measures how the
                  organization's capability to rearrange
                  resources affects knowledge

Brand             The scale measures brand orientation
                  and brand management as a dynamic
                  capability. Scale also measures the
                  relationship between brand
                  orientation, organizational innovation
                  capability and customer and business
                  performance

Relationship/     The scale measures aspects of
customer          organizational features (market
                  orientation, resource configuration
                  and social network) and their
                  influence on customer relationship-
                  oriented dynamic capabilities.
                  Besides, the scale measures the
                  indirect effect of these organizational
                  features on CRM performance, as well
                  as the direct effect of dynamic
                  capabilities on CRM performance

                  The scale measures the capability of
                  scanning export market for
                  opportunities and for new customers.
                  It also measures the organization's
                  capability of adapting to market
                  turbulence as well as the organization
                  capability of rearranging resources

                  The scale measures the integrative
                  and structural capacities in managing
                  customer knowledge and their
                  influence on product development

                  The scale was designed to test the
Relationship/     theoretical model proposed by the
supplier          authors. It focuses on supply chain
                  capabilities related to organization's
                  ability to sense and seize
                  opportunities in the market as well as
                  within customers and partners

                  The scale measures the dynamic
                  capabilities of IT-enabled sharing
                  capabilities that allow organizations
                  to adapt to dynamic context of supply
                  chain

                  The scale measures the dynamic
                  capability of relationship-enabled
                  responsiveness which is the
                  organization capability to respond to
                  environment demands by combining
                  resources from multiple parties in
                  supply chain

                  The scale measures the dynamic
                  capability of rearranging resources in
                  order to achieve supply chain agility.
                  It also measures the capability of
                  sensing and responding to
                  environmental changes and demands

                  The scale measures organizations'
                  capabilities to sense and to adapt to
                  market changes and the relationship
                  between these capabilities with
                  supply chain agility and
                  organizational performance. In this
                  scale, organizational performance
                  was divided into two dimensions
                  financial performance and marketing
                  performance

                  The scale measures strategic supply
                  chain capability as a dynamic
                  capability. It also measures supply
                  chain performance, supply chain
                  synchronization and industry-led
                  innovation utilization. Supply chain
                  capability was divided into two
                  dimensions: reconfiguration and
                  adaptation

Relationship/     The scale focuses on the capabilities
partners          related to the relationship between
                  the organization and its business
                  partners (suppliers and customers).
                  Authors named these capabilities as
                  networking capabilities

Strategic         The scale measures four constructs:
alliance          VRIN resources, non-VRIN resources,
                  dynamic capabilities and
                  performance. The items about VRIN
                  resources focuses on organization's
                  know-how, firm reputation and
                  experience on cooperative alliance
                  experience. To measure dynamic
                  capabilities, authors adopted the
                  studies of Teece et al. (1997) and
                  Eisenhardt and Martin (2000)

                  The scale was developed to measure
                  the dynamic capabilities of alliance
                  scanning, alliance coordination and
                  alliance learning. The scale measures
                  the relationship between these
                  capabilities, market orientation and
                  environment turbulence

Knowledge         The scale focuses on the organization
                  capability of knowledge processing
                  (which is divided into knowledge
                  acquisition, knowledge utilization
                  and knowledge dissemination). It also
                  assesses the relationship between
                  knowledge processing capabilities
                  and environment dynamism, in order
                  to evaluate the organization ability to
                  adapt to the environment

                  The scale measures the constructs of
                  knowledge management practices
                  and knowledge management
                  capabilities

                  The scale measures the capability of
                  sharing information with partners
                  and within organization members
                  and as well as the capability of
                  adapting to the environment

                  In this scale, dynamic capabilities are
                  measured in different dimensions:
                  reconfiguration routines, leveraging,
                  learning, knowledge creation, sensing
                  and seizing and knowledge
                  integration

                  The scale measures dynamic
                  capabilities as organization's
                  resource-based orientation. This scale
                  measures organization's capabilities
                  to manage knowledge in order to
                  rearrange its resources in order to
                  sustain competitive advantage

                  The scale measures market-oriented
                  behavior through the lens of dynamic
                  capability perspective. The construct
                  of market-oriented behavior is
                  divided into three dimensions:
                  information acquisition, information
                  sharing and strategic response

                  The scale measures dynamic
                  capabilities divided into three
                  dimensions: knowledge acquisition
                  capability, knowledge generation
                  capability and knowledge
                  combination capability

Absorptive        Scale assesses absorptive capabilities
capacity          distributed on categories: knowledge
                  recognization, knowledge
                  assimilation, knowledge
                  maintenance, knowledge reactivation,
                  knowledge transformation and
                  knowledge application. It also
                  assesses innovation and adaptation
                  capabilities
                  The scale is divided into two
                  categories potential absorptive
                  capacity and realized absorptive
                  capacity

                  In their scale, authors focus on
                  innovation success based on the
                  theory of absorptive capacity and
                  dynamic capabilities

Operational       The scale evaluates the dynamic
capability        capability of assessing new markets
                  and the dynamic capabilities related
                  to R&D. It also assesses the
                  relationship between dynamic and
                  operational capabilities

                  The scale measures the relationship
                  between operational and dynamic
                  capabilities. The scale focuses on the
                  capabilities related to innovation and
                  product. The scale also measures the
                  capabilities related to organization's
                  capacity to respond to and to take
                  advantage of environmental changes

Governance        The scale measures the organizational
                  capabilities under the perspective of
                  dynamic capabilities by including
                  statements regarding organization's
                  capability to respond and to adapt to
                  environmental changes

                  The scale measures the
                  organization's capability to respond
                  to technological turbulence as well as
                  the influence of this capability on
                  performance. It also measures the
                  influence of strategic orientation on
                  organizational performance

                  The scale measures the capability to
                  create and rearrange resources in the
                  context of project and portfolio management
                  The scale measures sensing
                  capabilities on organizations, teams
                  and individuals

                  The scale measures cross-functional
                  and dynamic marketing capabilities.
                  The scale also measures chief
                  marketing executives' mindsets
                  regarding marketing capabilities. The
                  items are based on Teece's (2007) framework

                  The scale combines some items from
                  existing scales. Authors added other
                  items to measure portfolio
                  management governance. The
                  instrument measures portfolio
                  management as a dynamic capability
                  even though scale items do cover
                  some basic aspects of the dynamic
                  capability theory

                  Author designed the scale for
                  dynamic capabilities based on
                  literature review. He divides dynamic
                  capabilities into seven dimensions:
                  proactiveness, innovativeness
                  (innovation capability), risk taking,
                  competitive aggressiveness,
                  relational capital, knowledge, and
                  learning. The scale also measures the
                  capabilities of task control and task
                  proficiency

                  The scale measures the sensing,
                  seizing and reconfiguring capabilities
                  in organizational context. The scale is
                  based on the Teece's (2007)
                  framework. It also measures the
                  relationship between these
                  capabilities and change performance
                  in work units

Context           Authors            Cit (a)

Innovation        da Costa and          5
                  Porto (2014)

                  Herrmann             186
                  et al. (2007)

                  Janssen et al.       26
                  (2015)

                  Plattfaut et al.     11
                  (2015)

                  Vicente et al.       28
                  (2015)

                  Shafia et al.         6
                  (2016)

                  Agarwal and          24
                  Selen (2013)

                  Cheng and            60
                  Chen (2013)

Organizational    Alegre et al.        39
learning          (2012)

                  Siren (2012)         18

                  Verreynne             6
                  et al.(2016)

Brand             Santos-              83
                  Vijande et al.
                  (2013)

Relationship/     Desai et al.         22
customer          (2007)

                  Lisboa et al.        31
                  (2013)

                  Hakimi et al.         6
                  (2014)

Relationship/     Gligor and           39
supplier          Holcomb
                  (2014)

                  Jin et al.           65
                  (2014)

                  Kim et al.           46
                  (2013)

                  Sangari and          28
                  Razmi (2015)

                  Whitten et al.       110
                  (2012)

                  Storer et al.        12
                  (2014)

Relationship/     Mitrega et al.       131
partners          (2012)

Strategic         Lin and Wu           231
alliance          (2014)

                  Kandemir             293
                  et al. (2006)

Knowledge         Jantunen             368
                  (2005)

                  Villar et al.        100
                  (2014)

                  Karayanni             3
                  (2015)

                  Makkonen             109
                  et al. (2014)

                  Paiva et al.         11
                  (2012)

                  Schlosser and        34
                  McNaughton
                  (2009)

                  Zheng et al.         118
                  (2011)

Absorptive        Biedenbach           100
capacity          and Muller
                  (2012)

                  Camison and          411
                  Fores (2010)

                  Nitzsche et al.       6
                  (2016)

Operational       Danneels             30
capability        (2016)

                  Wu et al.            175
                  (2010)

Governance        Ouakouak             44
                  et al. (2014)

                  Pratono               9
                  (2016)

                  Rungi (2015)          4

                  Sprafke et al.       25
                  (2012)

                  Tollin and            5
                  Schmidt
                  (2015)

                  Urhahn and           16
                  Spieth (2014)

                  Schweitzer           31
                  (2014)

                  Maijanen and          1
                  Jantunen
                  (2016)

Note: (a) Number of citations according to Google Scholar--updated on
4-Jun-18

Source: Authors

Table II.

Measure instruments
for DCs with the
analysis of their
validity and reliability
according to Slavec
and Drnovesek (2012)

Authors           Scale validation and statistical tests

Agarwal and       Authors validate the scale by applying
Selen (2013)      exploratory and confirmatory factor analysis.
                  This scale is an improved version of the one
                  designed by Agarwal and Selen (2013)
Alegre et al.     Authors applied multivariate analysis to assess
(2012)            the scale's reliability and its content,
                  discriminant and convergent validity. Authors
                  applied confirmatory factor analysis
Biedenbach and    The proposed model and scale were validated
Muller (2012)     through multiple regression analysis. Canonical
                  correlation analysis was also used to evaluate
                  the relationship between innovative, absorptive
                  and adaptive capabilities and project
                  performance
Camison and       The scale is based on the research of Zahra and
Fores (2010)      George (2002). Then, the scale is validated by
                  applying confirmatory factor analysis based on
                  structural equations modeling (SEM)
Cheng and Chen    To validate the instrument and the hypotheses
(2013)            proposed on the research, authors collected 218
                  valid responses. Authors assessed the construct
                  validity and reliability by assessing the
                  Cronbach's [alpha]. To identify the factor
                  structure, they used the varimax rotation. They
                  also assessed the convergent and discriminant
                  validity. Finally, they validated results by
                  performing the confirmatory factor analysis
                  (CFA)
da Costa and      The scale was validated by applying the
Porto (2014)      multiple regression analysis and other
                  statistical tests (e.g. Cronbach's [alpha])
Danneels (2016)   The scale was validated by applying
                  confirmatory factor analysis and multiple
                  regression analysis
Desai et al.      The scale items were adapted from existing
(2007)            scale on market orientation, CRM, and dynamic
                  capabilities. Then, the scale was evaluated by
                  experts. On the sequence, authors conducted a
                  pilot test with 82 executives. The final
                  version of the scale was used in a survey that
                  collected 334 responses from executives of 29
                  Indian companies from banking, telecom and
                  retail sectors. To assess the reliability of
                  the instrument, authors used EFA and tested the
                  Cronbach's [alpha]. In order to confirm the
                  proposed hypotheses, they use the least square
                  regression
Gligor and        The scale was validated by applying exploratory
Holcomb (2014)    and confirmatory factor analysis (CFA)
Hakimi et al.     The scale was validated by applying exploratory
(2014)            and confirmatory factor analysis. Initially the
                  scale contained 57 items. The final version of
                  the scale contains 16 items
Herrmann et al.   In the first phase, the model was tested by
(2007)            using partial least square modeling (PLS). In
                  the second phase, the scale was tested by
                  applying the confirmatory factor analysis
Janssen et al.    The scale was tested by performing exploratory
(2015)            and confirmatory analysis. Authors also
                  performed structural equation modeling (SEM) to
                  assess the construct correlation
Jantunen (2005)   The scale was validated by applying exploratory
                  factor analysis. The innovative factor was
                  assessed by performing hierarchical linear
                  regression analysis
Jin et al.        The authors performed confirmatory factor
(2014)            analysis (CFA) to validate the scale and also
                  performed structural equation modeling (SEM) to
                  validate the model and hypotheses
Kandemir et al.   The scale was validated by performing
(2006)            confirmatory factor analysis (CFA)
Karayanni         The scale was validated by applying
(2015)            confirmatory factor analysis; the proposed
                  model, by performing structural equation
                  modeling (SME)
Kim et al.        The scale was validated by performing
(2013)            confirmatory factor analysis (CFA)
Lin and Wu        In order to assess data validity, authors
(2014)            tested the Mahalanobis distance, which checks
                  outliers in a sample. To assess the validity of
                  the constructs, authors assessed the Cronbach's
                  [alpha] value of these constructs. Authors also
                  validate the model and the instrument, by using
                  the analysis of variance (ANOVA) and structural
                  equation modeling (SEM). LISREL was the SEM
                  technique adopted by the authors
Lisboa et al.     The instrument was validated by applying
(2013)            confirmatory factor analysis (CFA)
Maijanen and      The scale was validated by applying
Jantunen (2016)   multivariate analysis. To test the hypotheses,
                  authors performed ANOVA tests
Makkonen et al.   Authors validated the instrument by applying
(2014)            confirmatory factor analysis (CFA)
Mitrega et al.    Authors adopted a three-stage process of scale
(2012)            development, which included qualitative and
                  quantitative phases. First, the items emerged
                  based on literature and interviews. Second,
                  authors validated the scale items by conducting
                  focus groups, and finally, after applying a
                  online survey, authors validated the scale by
                  performing exploratory and confirmatory factor
                  analysis. Initially, the scale contained 41
                  items. After the confirmatory factor analysis,
                  only 17 items remained
Nitzsche et al.   Authors wrote the items of the scale based on
(2016)            literature review. Then, they got feedbacks
                  from experts about the scale. On the sequence,
                  authors conducted a pre-test. Afterwards,
                  authors applied a survey using the scale. To
                  test the validity and reliability of the
                  instrument, they applied the exploratory factor
                  analyzed (EFA) on the collected data
Ouakouak et al.   The scale is based on previous studies on
(2014)            innovation capability. Authors applied
                  discriminant and convergent validity tests, and
                  checked the values of KMO (Kaiser-Meyer-Olkin)
                  and Cronbach's [alpha]
Paiva et al.      Scale was applied to Brazilian and Spanish
(2012)            participants. The scale was validated by
                  applying confirmatory factor analysis (CFA)
Plattfaut et      Authors used partial least squares (PLS) to
al. (2015)        validate the model
Pratono (2016)    Author uses partial least squares (PLS) for
                  data analysis and statistical validation
Rungi (2015)      Authors wrote the scale items based on previous
                  literature. After collecting data through a
                  survey, to assess the collected data authors
                  performed the Levene test and checked
                  Cronbach's [alpha] values. Authors do not
                  mention a specific statistical process to
                  validate the scale
Sangari and       The instrument was validated by applying
Razmi (2015)      confirmatory factor analysis (CFA)
Santos-Vijande    The scale was validated by applying
et al. (2013)     confirmatory factor analysis (CFA)
Schlosser and     The scale was validated by applying exploratory
McNaughton        (EFA) and confirmatory factor analysis (CFA).
(2009)            After performing the multivariate analysis, 20
                  items of the scale remained
Schweitzer (2014) The scale was validated by performing partial
                  least squares (PLS)
Shafia et al.     The scale was designed based on literature
(2016)            review. After writing the scale items, authors
                  conducted a survey among technology
                  organizations. To validate the instrument,
                  authors used confirmatory factor analysis (CFA)
                  under structural equation modeling (SEM)
                  approach
Siren (2012)      Author validated the scale by performing
                  exploratory and confirmatory factor analysis.
                  After the statistical validation, the number of
                  items reduced from 24 to 19
Sprafke et al.    To validate the scale, authors analyzed the
(2012)            component factor and factor loadings of the
                  variables. To validate the internal consistency
                  of the scale, they verified the Cronbach's a.
                  To test the research hypotheses, authors used
                  multiple regression analysis
Storer et al.     To validate the instrument, authors used
(2014)            confirmatory factor analysis (CFA) under
                  structural equation modeling (SEM) approach
Tollin and        To validate the model, authors compare the
Schmidt (2015)    degree of variance of the constructs, their
                  Cronbach's [alpha] and their correlation.
                  Authors also perform a cluster analysis to
                  validate the model. Authors do no mention if
                  they applied statistical analysis to validate
                  the scale specifically
Urhahn and        The model was validated by applying structural
Spieth (2014)     equation modeling (SME)
Verreynne et      To validate the scale, authors used exploratory
al. (2016)        (EFA) and confirmatory factor analysis (CFA),
                  with structural equation modeling (SME)
                  approach
Vicente et al.    Authors wrote the scale items based on
(2015)            literature review. On the sequence, they
                  applied a survey among 471 exporting
                  manufacturing organizations. To test the
                  validity and the reliability of the scale,
                  authors performed structural equation modeling
                  (SME)
Villar et al.     To validate the measurement instrument, authors
(2014)            performed structural equation modeling (SME)
Whitten et al.    To validate the scale, authors performed
(2012)            confirmatory factor analysis (CFA) with
                  structural N equation modeling (SME) approach
Wu et al.         To validate the scale, authors performed
(2010)            confirmatory factor analysis (CFA) with
                  structural N equation modeling (SME) approach
Zheng etal.       To validate the instrument, authors conducted a
(2011)            survey on China on which they obtained Y 218
                  valid responses. They validated the construct
                  validity and reliability by assessing the
                  Cronbach's [alpha]. They also performed the
                  structural equation modeling (SME) using the
                  AMOS 7.0 software

                  Theoretical                             Statistical
                  importance and      Representativeness  analysis and
                  existence of the    and                 statistical
                  construct           appropriateness of  evidence of
                                      data collection     the construct

                  CDS   IPG   CVE   QDE   TBT   PS   SD   DA   RA   CVA
Authors

Agarwal and       Y     NR    N     NR    na    Y    Y    Y    Y    Y
Selen (2013)

Alegre et al.     Y     Y     N     NR    na    Y    Y    Y    Y    Y
(2012)

Biedenbach and    Y     Y     Y     Y     na    Y    Y    Y    Y    N
Muller (2012)

Camison and       Y     Y     N     Y     na    Y    Y    Y    Y    Y
Fores (2010)

Cheng and Chen    Y     Y     Y     Y     Y     Y    Y    Y    Y    Y
(2013)

da Costa and      Y     NR    N     NR    N     N    Y    Y    Y    N
Porto (2014)

Danneels (2016)   N     Y     N     NR    na    N    Y    Y    Y    Y

Desai et al.      Y     Y     Y     Y     na      Y    Y    Y  Y    N
(2007)

Gligor and        Y     Y     Y     Y     na    Y    Y    Y    Y    Y
Holcomb (2014)
Hakimi et al.     Y     Y     N     Y     na    Y    Y    Y    Y    Y
(2014)

Herrmann et al.   N     NR    Y     Y     na    N    Y    Y    Y    Y
(2007)

Janssen et al.    Y     Y     Y     Y     na    Y    Y    Y    Y    Y
(2015)

Jantunen (2005)   N     Y     N     NR    na    N    Y    Y    Y    N

Jin et al.        Y     Y     Y     Y     na    Y    Y    Y    Y    Y
(2014)

Kandemir et al.   Y     Y     Y     Y     na    Y    Y    Y    Y    Y
(2006)
Karayanni         N     Y     N     Y     na    N    Y    Y    Y    Y
(2015)

Kim et al.        Y     Y     Y     Y     na    Y    Y    Y    Y    Y
(2013)
Lin and Wu        NR    Y     Y     Y     na    Y    Y    Y    Y    Y
(2014)

Lisboa et al.     Y     Y     Y     Y     Y     Y    Y    Y    Y    Y
(2013)
Maijanen and      N     Y     N     Y     na    N    Y    N    Y    N
Jantunen (2016)

Makkonen et al.   N     Y     N     Y     na    Y    Y    Y    Y    Y
(2014)
Mitrega et al.    Y     Y     Y     Y     na    Y    Y    Y    Y    Y
(2012)

Nitzsche et al.   N     Y     N     NR    na    N    Y    Y    Y    Y
(2016)

Ouakouak et al.   N     Y     N     Y     na    N    Y    NR   Y    N
(2014)

Paiva et al.      N     Y     Y     Y     Y     Y    Y    Y    Y    Y
(2012)

Plattfaut et      N     Y     N     Y     na    Y    Y    Y    Y    Y
al. (2015)
Pratono (2016)    N     Y     N     Y     na    N    Y    Y    Y    Y

Rungi (2015)      Y     Y     Y     Y     na    Y    Y    N    Y    N

Sangari and       N     Y     Y     Y     na    N    Y    Y    Y    Y
Razmi (2015)
Santos-Vijande    Y     Y     Y     Y     na    N    Y    Y    Y    Y
et al. (2013)
Schlosser and     Y     Y     Y     Y     na    Y    Y    Y    Y    Y
McNaughton
(2009)

Schweitzer (2014) Y     Y     Y     Y     na    Y    Y    Y    Y    Y

Shafia et al.     Y     Y     Y     Y     na    Y    Y    Y    Y    Y
(2016)

Siren (2012)      Y     Y     Y     Y     na    N    Y    Y    Y    Y

Sprafke et al.    Y     Y     N     Y     na    N    Y    NR   Y    NR
(2012)

Storer et al.     NR    Y     N     Y     na    N    Y    Y    Y    Y
(2014)

Tollin and        Y     Y     N     Y     na    N    Y    NR   Y    Y
Schmidt (2015)

Urhahn and        Y     Y     N     Y     na    N    Y    Y    Y    Y
Spieth (2014)
Verreynne et      Y     Y     Y     Y     na    Y    Y    Y    Y    Y
al. (2016)

Vicente et al.    Y     Y     Y     Y     Y     Y    Y    Y    Y    Y
(2015)

Villar et al.     N     Y     N     Y     na    Y    Y    Y    Y    Y
(2014)
Whitten et al.    Y     N     Y     na    Y     Y    Y    Y    Y    Y
(2012)

Wu et al.         Y     N     Y     na    Y     Y    Y    Y    Y    Y
(2010)

Zheng etal.       Y     Y     Y     na    N     Y    Y    Y    Y    Y
(2011)

Notes: CDS, contain domain specification; IPG, item pool
generation; CVE, content validity evaluation; QDE, questionanaire
development and evaluation; TBT, translation and back-translation;
PS, pilot study; SD, sample data; DA, dimension assessment; RA,
reliability assessment; CVA, construct validity assessment; Y, yes;
N, no; NR, not reported

Source: Authors