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Key driver analysis (statistically inferred importance analysis) is commonly implemented to understand what customer satisfaction attributes are most important in driving overall customer satisfaction. Building on prior research, this article suggests that dominance analysis and relative weight analysis are the most appropriate statistical techniques for conducting key driver analysis, yet relative weight analysis is the more feasible choice. A process is put forth so that research practitioners can conduct key driver analysis with relative weight analysis implementing a two-step approach. The two-step approach should be used when the key driver analysis contains a large number of attributes, which are theoretically redundant and highly correlated. [ABSTRACT FROM AUTHOR]

For customer satisfaction researchers, key driver analysis is a common practice to understand what product and service attributes are most important in driving the overall customer experience, typically measured by overall satisfaction or the Net Promoter question. To implement key driver analysis, market research practitioners often use statistical techniques such as bivariate correlation analysis or multiple regression, yet these statistical techniques have severe limitations for conducting key driver analysis. As a viable alternative, this article puts forth relative weight analysis (RWA) as an appropriate statistical technique for conducting key driver analysis. To empirically demonstrate this technique, key driver analysis was conducted using data from a B2B software provider. The analysis implemented RWA, correlation analysis, and multiple regression, and the results are compared and contrasted. RWA limitations and best practices are discussed as well as future research. Overall, this research advocates for more use of RWA in marketing research. [ABSTRACT FROM AUTHOR]

This innovative system of teaching and learning includes the implementation of two effective learning technologies: podcasting ("flipping") and classroom response systems ("clicking"). Students watch lectures in podcast format before coming to class, which allows the "entire" class period to be devoted to active learning exercises designed to achieve higher-order learning. Class time is primarily devoted to clicker-based competitions, which are team events employing peer-to-peer learning methods. The competitions motivate students and simultaneously helps them to achieve higher-order learning. This method of teaching and learning overcomes many of the obstacles associated with traditional class formats and has resulted in significantly better learning outcomes.

An adaptive conjoint analysis was performed on the study abroad preferences of a sample of undergraduate college students. The results indicate that trip location, cost, and time spent abroad are the three most important determinants of student preference for different study abroad trip scenarios. The analysis also uncovered four different study abroad need-based segments. In order to increase study abroad participation rates it is recommended that universities determine which of these segments they want to target, and then develop a set of study abroad offerings and communication strategies custom designed to address the particular needs of each targeted segment. Recommendations on how to market to each segment are provided. (Contains 7 tables.)

The purpose of this logistics research methods article is to empirically test and introduce correlated components regression (CCR) as a new statistical technique that will improve the accuracy and validity in testing logistics theoretical models and hypothesised relationships. The purpose of the current study is to use CCR analysis as technique to address multicollinearity. Customer satisfaction data with parcel carriers is analysed with using CCR and multiple regression. To determine the best regression model of these two approaches, cross-validation R2 values are used. In addition, comparisons are made to examine the standardised beta coefficients from both methods and to assess the possible impact from high levels of multicollinearity. Findings of the analysis suggest that CCR has a significantly higher cross-validation R2 value and thus is determined the best model of these two approaches. [ABSTRACT FROM AUTHOR]

Purpose - Owner operators are an important group of truck drivers, yet have been the subject of very little academic research. Specifically, no research has explored retention issues among owner operators. Thus, this paper aims to employ a segmentation approach to owner operator retention, examining important independent variables that include: pay and compensation, top management support, time at home, and safety. Design/methodology/approach - Data were collected from a large truckload (TL) carrier. The drivers surveyed were irregular route long-haul drivers. A total of 239 responses were obtained, for a response rate of 74.6 per cent. Latent class regression (LCR), a technique new to logistics and supply chain management (SCM) research, was utilized for data analysis. Findings - The results show there are four different need-based driver segments, which means that specific driver retention programs must be developed for each segment. Research limitations/implications - The major limitation of this paper is that an attitudinal measure (intent to stay) was used to assess turnover instead of actual behavior. Additionally, only one demographic variable was collected. Implications of this paper include demonstrating the existence of unique segments within the same sample; the specification of the major attitudinal drivers for each segment; and the need to further study a largely neglected but very important group in the supply chain. Practical implications - From a practitioner viewpoint, knowing that different groups of owner operators exist will help in developing programs to improve driver retention. Specific recommended actions for each segment are presented. These results will help managers with the retention of owner operators. Originality/value - Owner operators are an important group when it comes to making the global supply chain function smoothly and efficiently. Yet, little research in logistics and SCM address this group. Driver turnover continues to plague the TL industry and understanding owner operators, and their segments, is critical to improve driver retention, cost structure, and profitability. Also, to determine if segments of this under-researched group exist, LCR was deployed. [ABSTRACT FROM AUTHOR]

Discusses the nature of salesperson logistics expertise along the dimensions of tactical versus strategic and internal versus external expertise. Importance of logistics service to maintain the buyer-seller relationship; Factors influencing importance of salesperson logistics expertise; Effect of logistics-sale interface on supply chain management.

This article introduces measurement model in confirmatory factor analysis as a tool for logistics research to test for construct validity. An important aspect of increased rigor is testing for construct validity and many of its subdimensions, including unidimensionality, convergent validity, discriminant validity, and predictive validity. Advancements in structural equation modeling (SEM) make this statistical technique useful in testing construct validity within a single research study. Construct validity examines the degree to which a scale measures what it intends to measure. The purpose of the measurement model is to describe how well the observed indicators serve as a measurement instrument for the latent variables. The confirmatory factor analysis process for refining and testing for unidimensional constructs should first be done independently with each latent variable. Once unidimensionality and scale reliability are deemed acceptable, convergent, discriminant, and predictive validity can be assessed and tested using the measurement model in SEM.