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Fitness technology feature use items were not available in the literature, and thus, were developed for the current study. Items were developed for each feature set following the procedure described next. We used a four-step process to determine the fitness technology features that make up our first-order subconstructs. First, we compiled a list of currently available fitness devices and their associated apps using lists of wearables published in popular media outlets. The original list contained 72 devices and was compiled using lists of wearables from CNET, PC Magazine, The Wall Street Journal, Engadget, Gizmodo, and others. While not comprehensive, the redundancy across multiple lists suggests that our list, at the very least, contains the most popular devices in the wearables category at the time the data was collected. Second, the three researchers independently visited every website of each device/app on the list and collected the features the company advertised for the fitness technology. Third, all three of the researchers' feature lists were compared, discussed, and used to create an integrated list. Fourth, once survey items were created, an expert panel was convened to examine the feature list and scales as described below.Accepted procedural methods (Churchill 1979;MacKenzie et al. 2011) were followed in developing the fitness technology use items. Once the items were developed, an expert panel was convened. The expert panel consisted of two faculty members who are well versed in surveybased methodologies, two faculty members who were active users of fitness technologies, and two employees of a fitness technology company. The expert panel was instructed to examine the entire survey instrument for clarity and to provide feedback on wording and note if any fitness technology features were missing. The expert panel did not provide any new fitness technology features, which indicated our list was reasonably comprehensive. The expert panel did suggest wording changes to the fitness technology features set use items and the addition of a few more fitness device and apps to our list (primarily new versions of devices already present in our list). We compiled the expert panel wording suggestions and considered each suggestion. Most wording suggestions from the expert panel were implemented, improving the clarity of the survey items.

In this study, we introduce a cooperative parallel tabu search algorithm (CPTS) for the quadratic assignment problem (QAP). The QAP is an NP-hard combinatorial optimization problem that is widely acknowledged to be computationally demanding. These characteristics make the QAP an ideal candidate for parallel solution techniques. CPTS is a cooperative parallel algorithm in which the processors exchange information throughout the run of the algorithm as opposed to independent concurrent search strategies that aggregate data only at the end of execution. CPTS accomplishes this cooperation by maintaining a global reference set which uses the information exchange to promote both intensification and strategic diversification in a parallel environment. This study demonstrates the benefits that may be obtained from parallel computing in terms of solution quality, computational time and algorithmic flexibility. A set of 41 test problems obtained from QAPLIB were used to analyze the quality of the CPTS algorithm. Additionally, we report results for 60 difficult new test instances. The CPTS algorithm is shown to provide good solution quality for all problems in acceptable computational times. Out of the 41 test instances obtained from QAPLIB, CPTS is shown to meet or exceed the average solution quality of many of the best sequential and parallel approaches from the literature on all but six problems, whereas no other leading method exhibits a performance that is superior to this.

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