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The purpose of this paper is two-fold: to identify the reliability and content validity of two popular managerial coaching scales – the Ellinger Behavioral Scale and the Park Skills-based Scale – to determine the extent to which the construct, coaching, is more accurately measured as a behavioral construct or a skill-based construct from the perspective of the coach, and from that of his or her direct reports using a single data set.

This research utilized survey research which tested the reliability and validity of two existing coaching scales. Analyses included correlation matrices, principle axis factor analysis, and confirmatory factor analysis.

Results of this research indicate that neither scale is perfectly reliable and valid. However, given the results of the analysis, the authors recommend the Park scale for leaders and the Ellinger scale for team members.

This research indicates that investment in valid scales for use by direct reports to measure the coaching expertise of their managers is warranted.

There are several implications that are evident as a result of this research. First, there are implications for the training and development of employees. Too, many organizations look to coaching and coaching skills as a benchmark for selecting future leaders – the understanding of how current scales are able to identify coaching expertise is important to the manager selection process.

This research offers one of the first comparative analyses of currently available coaching scales. It contributes to the literature on coaching by providing a clear and thorough review and analysis of scales currently available for testing managerial coaching expertise. Practitioners and scholars can benefit from this research by developing a better understanding of the contexts in which these two coaching scales are most reliable and valid.

Considering the relevance of operational excellence as a business strategy, organizations are striving to improve themselves by adopting best practices and universally accepted principles through the process of continuous improvement, and these principles should be embedded in the culture of an organization. Organizations pursue to align themselves by continuously improving their processes by adopting scientifically proven techniques and cultural transformation throughout the organization. However, there is a lack of scientific instruments for the assessment of operational excellence. The objective of this study is to develop a scale for the assessment of practices of operational excellence principles in the organizations. Further reliability and validity of the developed scale are measured by testing the relationship between Human Resource Practices (HRP) and Operational Excellence (OE).

This study comprises quantitative design through exploratory and confirmatory studies and also includes qualitative analysis to develop a scale for the assessment of Operational Excellence (OE). Interviews from industry experts have been conducted to identify the major components for which organizations are striving for OE. Previous literature and excellence models, especially principles of the Shingo Operational Excellence Model (SOEM), have been reviewed and considered to finalize the scale items. Data were collected in two stages from both Telecommunication subsectors (Cellular Mobile Operators and Fixed Local Loop Operators) of Pakistan through the cross-sectional survey. In the first stage, exploratory factor analysis (EFA) was performed on the sample of 611 respondents from both Cellular Mobile and Fixed Local Loop operators of Pakistan. In the second stage, confirmatory factor analysis (CFA) was performed on the sample of 423 respondents from the Fixed local loop operators. EFA was conducted by using SPSS version 23 to finalize the OE scale, and for confirmatory factor analysis, PLS-SEM using Smart PLS was used to confirm the reliability and validity of the OE Scale.

The results of EFA reveal that OE is a multidimensional construct with three dimensions and 23 items. The dimensions of the developed OE Scale explored in this study are cultural enablers (CE), continuous process improvement (CPI) and enterprise alignment (EA). The confirmatory factor analysis of OE confirmed the scale dimensionality, reliability and validity along with the hypothesis testing to measure the impact of antecedent variable HRP on OE.

Organizations pursue to improve and align their operational processes but usually unable to confirm the implementation of their desired objectives. Based on the developed OE scale, managers may assess the implementation of OE principles in their organizations. This research has been conducted in the telecommunication sector of Pakistan only, and the developed instrument needs to be further tested in other organizations.

The instrument developed in this study will help both researchers and practitioners to assess the principles of operational excellence in their organizations and enable them to design the strategies for improving organizational performance.

The results of this study will create awareness about the principles of operational excellence. The developed OE instrument will assist in identifying the gaps in organizational norms and values from the perspective of paying respect to every individual inside and outside the organization. OE instrument will be further helpful in the identification and assurance of health, safety, protection of the environment and community issues.

This study provides a reliable and validated scale for the scientific area of operation management and helps managers with the assessment of operational excellence in their organizations. This newly developed scale is also valid to test and use in different studies and industries by researchers and practitioners.

Factor analysis is the most used tool in organizational research and its widespread use in scale validations contribute to decision-making in management. However, standard factor analysis is not always applied correctly mainly due to the misuse of ordinal data as interval data and the inadequacy of the former for classical factor analysis. The purpose of this paper is to present and apply the Bayesian factor analysis for mixed data (BFAMD) in the context of empirical using the Bayesian paradigm for the construction of scales.

Ignoring the categorical nature of some variables often used in management studies, as the popular Likert scale, may result in a model with false accuracy and possibly biased estimates. To address this issue, Quinn (2004) proposed a Bayesian factor analysis model for mixed data, which is capable of modeling ordinal (qualitative measure) and continuous data (quantitative measure) jointly and allows the inclusion of qualitative information through prior distributions for the parameters’ model. This model, adopted here, presents considering advantages and allows the estimation of the posterior distribution for the latent variables estimated, making the process of inference easier.

The results show that BFAMD is an effective approach for scale validation in management studies making both exploratory and confirmatory analyses possible for the estimated factors and also allowing the analysts to insert a priori information regardless of the sample size, either by using the credible intervals for Factor Loadings or by conducting specific hypotheses tests. The flexibility of the Bayesian approach presented is counterbalanced by the fact that the main estimates used in factor analysis as uniqueness and communalities commonly lose their usual interpretation due to the choice of using prior distributions.

Considering that the development of scales through factor analysis aims to contribute to appropriate decision-making in management and the increasing misuse of ordinal scales as interval in organizational studies, this proposal seems to be effective for mixed data analyses. The findings found here are not intended to be conclusive or limiting but offer a useful starting point from which further theoretical and empirical research of Bayesian factor analysis can be built.

Companies in all industries are searching for new sources of competitive advantage since the competition in their marketplace is becoming increasingly intensive. The resource-based view of the firm explains the sources of sustainable competitive advantages. From a resource-based view perspective, relational based assets (i.e., the assets resulting from firm contacts in the marketplace) enable competitive advantage. The relational based assets examined in this work are brand image and corporate reputation, as components of brand equity, and customer value. This paper explores how they create value. Despite the relatively large amount of literature describing the benefits of firms in having strong brand equity and delivering customer value, no research validated the linkage of brand equity components, brand image, and corporate reputation, simultaneously in the customer value–customer loyalty chain. This work presents a model of testing these relationships in consumer goods, in a business-to-business context. The results demonstrate the differential roles of brand image and corporate reputation on perceived quality, customer value, and customer loyalty. Brand image influences the perception of quality of the products and the additional services, whereas corporate reputation actions beyond brand image, estimating the customer value and customer loyalty. The effects of corporate reputation are also validated on different samples. The results demonstrate the importance of managing brand equity facets, brand image, and corporate reputation since their differential impacts on perceived quality, customer value, and customer loyalty. The results also demonstrate that companies should not limit to invest only in brand image. Maintaining and enhancing corporate reputation can have a stronger impact on customer value and customer loyalty, and can create differential competitive advantage.

The purpose of this paper is to validate empirically a measurement scale of the effectiveness of R & D activities, starting from previous work in which the content was validated.

Following psychometric standards the authors have addressed the analysis phases of construct dimensionality, reliability and validity (convergent, discriminant and nomologic), and the scale criteria are shown to be valid in their three temporal manifestations (retrospective, concurrent and predictive). The empirical evidence was drawn from a sample of 85 companies belonging to the Spanish pharmaceutical sector.

Globally the authors provide evidence of reliability, validity of construct and validity of criterion in their diverse manifestations, for the scale designed and validated, on effectiveness in R & D. The authors divide the results into two groups: one for content of the scale and the other for relationships of the scale with other variables. With respect to the first, it is notable that, although in general the variables analyzed coincide with the previous broad and multidisciplinary theory on the success factors of R & D activities, what the authors provide is empirical evidence of the most important factors and variables for effectiveness in R & D; the authors emphasize that the results of the sample analyzed indicate that the most important factor is the close integration of the R & D activities with the corporate strategy, followed by the proper planning of these activities, and the achievement of financial results for the company. With respect to the relationship of the scale with other variables, the authors have found positive and significant relationships between the effectiveness in R & D and the following financial variables: net turnover and earnings after taxes. The authors have also found positive and significant relationships between different characteristics of the company and the achievement of success in R & D activities. Thus, being a company of larger size, the existence of an R & D department, the existence of specific incentive systems for the R & D personnel, the adoption of new management techniques in the R & D department, and the patents policy of the company are all factors that have a positive influence.

There are three main limitations of the study: the size of the sample; the decision to use a very particular highly innovatory sector, the pharmaceutical industry; and conducting the study in only one specific country, Spain. The results should be interpreted taking into account these limitations. Another limitation is the absence of previously validated scales. This meant that the authors were unable to do any comparative analyses.

The authors have contributed by summarizing and testing the existing theories on the factors of success in R & D. This should give R & D managers a more comprehensive and useful picture of the variables that have been considered more important, and should enable them to choose from among the range of variables proposed those that may be considered most relevant for inclusion in their own balanced scorecard. More generally, the results should help them in the management of their activity. For researchers the authors make available an already validated scale with which to work in various different samples and settings.

The originality of the work resides in two aspects. First, a very wide set of variables proposed in the literature is analyzed, with the object of establishing the relationships and the ranking of these variables, which would not be clear if the variables were analyzed in isolation. Second, there is originality in the methodology employed for measuring the result of activities with a high level of uncertainty and risk, specifically R & D activities in the highly innovative companies of the pharmaceutical industry. It is original because, to date, the scale has only been validated theoretically – there is no work in the literature validating it empirically.

This paper is the main section on quantitative data analysis. It explains the concepts at a greater detail to help non-Math/Stat scholars to understand the basics easily. Proper data analysis is critical to any research. If data are not properly analyzed, then it may give results which either cannot be properly interpreted or wrongly interpreted. This section covers univariate, multivariate analysis and then, factor analysis, cluster analysis, conjoint analysis, and multidimensional scaling (MDS) techniques.

This study identifies important dimensions of the athlete brand identity construct incorporating the athlete perspective. It also uses Rasch analysis to provide a practical tool (the Athlete Brand Identity Scale) to measure how closely an athlete's personal brand identity is aligned with their perceived brand image.

Reference to existing athlete branding measurement tools and consultation with ten athlete experts generated (74) items considered important to an athlete brand. Two different response scales were then used to test those items in wider surveys of athletes and consumers. This allowed for further scale development and measurement of congruence between an athlete's self-image and the brand image held by consumers (within the same survey). Factor analysis and Rasch analysis were carried out to refine the item pool and assess item measurement properties to establish a concise scale for determining athlete brand identity.

Results show successful identification of four dimensions of athlete brand identity measurement: athletic integrity, athletic success, fan engagement and character traits, informed development of the Athlete Brand Identity Scale (ABIdS). The unique and significant aspect of the ABIdS is its capacity to incorporate the athlete's perspective into brand management.

The ABIdS can be utilised by early-career athletes to plan and prioritise branding efforts whilst established athletes can identify incongruence between self-image and consumer perceptions. Such gaps can be evaluated and branding activities modified accordingly. This will enable athletes to better access corporate support/sponsorship thereby reducing reliance on public funds.

The major difference between the ABIdS and other existing scales in the athlete brand research domain is the focus on the athlete perspective, as opposed to the consumer perspectives. Evaluating consumer perspectives does not explain how athletes perceive their own brand or how their own perception of their brand compares to that of people external to the brand (fans and consumers). The ABIdS developed in this study has the potential to achieve this objective as its design was driven by athlete perceptions but tested on both athletes and consumers.

The purpose of this study is to identify clinically meaningful groups of Health of the Nation Scales Learning Disabilities (HONOS-LD) single-item scales that might be used as short scales that are more reliable than single-item scale scores and more focused than the sum of scale scores. The single-item scales are likely to be unreliable in many applications. The sum of scale scores is a heterogeneous measure that is not a good representative of any specific difficulties that people who have intellectual disabilities may have and the effects of interventions on any specific difficulties may be masked by fluctuations in the ratings of other scales.

A total of 2,109 pseudonymised complete HONOS-LD ratings were factor-analysed using principal factor extraction and oblimin rotation. Three-, four- and five-factor rotated patterns were examined.

Three factors that each have three or more strong loadings (≥|0.50|) were identified that jointly included 11 single-item scales: one representing problems with cognitive competencies, one representing depressive phenomena or other mood problems and one representing problems with social competencies. A weaker factor that represents behaviour that challenges services is indicated; it includes five single-item scales. Both the cognitive competencies and social competencies groups of items were also reported in a previous study by Skelly and D’Antonio (2008) and may be stable. The present study’s factor representing behavioural difficulty has some similarity to Skelly and D’Antonio’s “functional behaviour and attachment disturbance” group. In other respects, the present study and the previous study differ.

The outcomes of these factor analyses indicate that some of the single-item scales can be combined into groups. However, the specific groups found in this study must be regarded as possibly unstable because of the likelihood of weak inter-rater reliability in HONOS-LD data and differences between this analysis and Skelly and D’Antonio’s. Further research is needed to support or modify them.

The cognitive competence and social competence groups of items may be used as subscales if they are convenient. The groups representing mood and behavioural problems should be supported by further research before being used.

This is the second published factor analysis of the HONOS-LD and includes a much larger data set than the first. It has some similarities to and differences from the first and is a further step in the process of identifying useful groupings of HONOS-LD single-item scales.

Traditional methods of capturing and determining logistics attribute importance have serious research limitations. The purpose of this paper is to introduce maximum difference (MD) scaling as a new research methodology that will improve validity in measuring logistics attribute importance, overcoming many of the limitations associated with traditional methods. In addition, this new research method will allow logistics researchers to identify meaningful need‐based segments, an important goal of logistics research.

This paper provides an overview of MD scaling along with important research advantages, limitations, and practical applications. Additionally, a detailed research process is put forth so that this technique can be implemented by logistics researchers. Finally, an application of this technique is presented to illustrate the research method.

The importance of truck driver satisfaction attributes was analyzed using bivariate correlation analysis as well as MD scaling analysis. The two sets of results are compared and contrasted. The resulting rank order of attributes is very different and MD scaling results are shown to possess important advantages. As a result of this analysis, MD scaling analysis allows for meaningful, need‐based segmentation analysis, resulting in two unique need‐based driver segments.

From a practitioner viewpoint, knowing which attributes are most important will help in investing scarce resources to improve decision making and raise a firm's ROI. Although a number of relevant applications exist, the most important may include examining: the importance of customer service attributes; the importance of logistics service quality attributes; and the importance of customer satisfaction attributes.

MD scaling is a relatively new research technique, a technique that has yet to be utilized or even explored in existing logistics and supply chain literature. Yet, evidence is mounting in other fields that suggest this technique has many important and unique advantages. This paper is the first overview, discussion, and application of this technique for logistics and supply chain management and creates a strong foundation for implementing MD scaling in future logistics and supply chain management research.