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The purchase drivers of merchandise sales rank in professional sport are examined at both a conceptual and an empirical level. The paper aims to discuss these issues.

A database was constructed for all 30 clubs in the National Hockey League based on a conceptual model of relevant variables. Both public and private data sources were accessed, covering an extensive 12-season period (1999-2011), including the 2004-2005 lockout when the season was cancelled. Principal-components analysis was used to reduce the number of variables for regression analysis to distinguish relatedness and to gauge the influence of those variables on merchandise sales rank.

The results reveal that six club-based factors impact merchandise sales rank: Overall Fan Satisfaction, Media Exposure, On-Field Performance, Strength of a Club’s Brand, Local Market Dynamics, and Fan Capacity to Pay. These six categories of purchase drivers form a strong predictive model of merchandise sales rank in the National Hockey League.

The resulting model could be extended in future research by adding extra categories to the conceptual framework and by developing alternative or better measures of the variables the authors use. It could also be tested with other sales data as the dependent variable since the study was limited to ranking data on merchandise data for the National Hockey League clubs for each year. Future research could use the actual merchandise dollars for each club in a league to test the model. A further extension would be to model subparts of merchandise (such as jersey sales, impacts of brand changes, etc.). A similar area of future research would be to look at the role of individual athletes as opposed to clubs in driving merchandise sales rank or volume.

For managers in professional sport, the results suggest that there are steps that can be taken to improve merchandise sales rank (and, by extension, merchandise sales volume). Practitioners can develop and follow strategies in this regard. Results also suggest that practitioners should put the achievement of high Regional Television Ratings – the strongest influencing variable on merchandise sales rank – as a priority.

The results confirm that brand is important when attempting to increase merchandise sales rank. Club managers need to be cognizant of their brand and its impact on merchandising in all decisions.

The business of professional sport is evolving globally with new sources of revenue, including merchandising increasing in prominence. This research explores the drivers of merchandise sales rank in professional sport and provide direction on key antecedents. The study proposes and tests a conceptual model.

Studies the problem of forecasting demand on the article level in a competitive consumer goods market. First the conventional approach to forecasting is discussed. A number of weak points of the demand forecasting unit approach are identified. Next, a new approach to forecasting based on applying scaling models, is presented. The method is then tried out and evaluated in the context of a real life business case. Shows that the advantage of the assortment forecasting process is its simplicity and strong means for feedback. Combined with a strong focus on consumer values, the method has potential to produce reliable forecast based on promotion and assortment change inputs.

The paper presents how a European pick‐and‐mix confectionery company has employed a new forecasting approach – assortment forecasting – to reduce significantly time spent on forecasting by working with an entire assortment at a time instead of producing a forecast for each product individually. The implementation of a less time‐consuming forecasting method has enabled the company to involve its salespeople in forecasting and in this way gain access to their product and market knowledge. The case company's implementation of the new forecasting method is described and its forecasting accuracy and time spent on forecasting before and after the implementation are measured. The results demonstrate a remarkable increase in forecasting efficiency as well as improved communication within the company.

Businesses need to make quick decisions and adjustments to fulfill the growing online demand. Previous studies examined various factors affecting the online sales performance of products such as books, electronics and movies; however, they paid limited attention toward the local brand clothing products. The current study investigates the importance of different kinds of seller-generated and consumer-generated signals such as price, discount, product ratings, review volume, review sentiment, number of questions and interaction between some of these factors for predicting the sales performance of clothing products.

The multiple linear regressions has been employed to investigate the influence of various predictor variables on sales performance. The study also examines the importance of these predictor variables by using different machine learning models, including random forest (RF), neural networks and support vector regression (SVR).

The findings of the study emphasize the importance of price and discount rates offered on the product. The quantitative characteristics of reviews, such as review volume and average rating, have been found to be more important predictors than sentiment strengths. However, the sentiment strength of reviews with higher helpfulness scores plays a significant role in predicting sales performance.

The study highlights the varying importance of seller-based and consumer-based signals in predicting sales performance. It also investigates the interaction effect of these two kinds of signals. The consumer-generated signals have been further divided into two components based on social influence theory, and the interaction effects of these components have also been examined.

The purpose of this paper is to articulate whether consumers’ use of music via streaming service benefits niche products and diversified consumption of music. It examines does winner take all or is long tail achieved in the digital music market.

To investigate the degree of concentration in the digital music sales, this study measures multiple concentration metrics using the top 100 songs for 245 weeks listed on the Korean music ranking chart.

Conflicting results are found between the analyses based on short-run and long-run data. When sales distributions are compared weekly or monthly, the results show that streaming services have a less concentrated sales distribution than download services. However, the result becomes the opposite in the long-run analysis (i.e. one year).

This study proposes that the non-technological drivers such as the beneficial addiction of music consumption can be a crucial driver affecting the usage concentration in music industry, coupled with the royalty policy of access-based services.

Although there has been a great deal of interest in the idea of accounting for human resources and considerable theoretical discussion of the problems of measuring human resource value and cost, there has been virtually no empirical research on the validity of proposed methods and models. This paper reports some preliminary evidence on the validity of selected surrogates of a person's value to an organization. It describes a field study conducted to determine the convergent and discriminant validity of three possible surrogates of a person's value to an organization: replacement cost, compensation, and a performance measure.

We examine the role of financial and nonfinancial performance measures in managing revenues derived from life cycles of a type of intellectual property products − motion pictures.

Our study focuses on the first two markets in which audiences can watch a motion picture – the upstream theatrical market and the downstream home video market. We combine data collected from numerous public and proprietary sources and form a final sample of 654 motion pictures. Then we perform regression analysis on the data.

First, three measures of a movie’s performance in the theatrical market, opening box office revenue, peak rank, and weeks at the peak rank, have positive effects on subsequent revenues in the home video market. Second, the same set of performance measures also predicts the motion picture’s life span in the theatrical market. Third, when the actual life span of a motion picture in the theatrical market deviates from its predicted value, the total return on investment in the motion picture decreases.

We do not have data on other downstream markets related to motion pictures, such as pay-per-view and online video streaming.

This study suggests that the public and proprietary data can be used to inform managerial decisions regarding intellectual property product life cycles.

This is the first accounting study that directly examines life cycle revenues of intellectual property products. We also extend literature on revenue driver and revenue management research to the product level.

The challenge faced by supplier companies in the grocery supply chain for implementing collaborative planning, forecasting and replenishment (CPFR) is how to get the retailer to forecast, especially when it has not been necessary before. In this paper a solution that would allow collaboration on a wide scale is presented. The forecasting approach is called “rank and share” and uses input from the retailer’s existing planning process – the category management process. The benefit of using category management as the basis is that the retailer can scale up collaboration with a large number of suppliers without increasing planning resources. For the supplier the benefit is point of sales forecasts at the time of the assortment decision. To support this collaborative forecasting process there is a need for more robust replenishment solutions, new measures to illustrate benefits, and for a distributed planning architecture and software. Potential solutions for these are also discussed in the paper.

Valence and volume of online reviews are generally considered to influence sales positively. However, existing findings regarding the relative influence of these two components have been inconclusive. This paper aims to explain some of these inconsistencies by examining the moderating role of regulatory focus (both as a chronic disposition and as a situational focus induced by the product category) in the relationship between online review volume/valence and consumers purchase decisions.

Two studies were conducted. Study 1 used a 2 (Volume: high/ low) * 3 (Valence: high/medium/low) within-subject experimental design. Study 2 analyzed real-world data from Amazon.com. Logistic and panel regression analyses were used to test the research hypotheses.

The studies confirmed the hypothesized effect of regulatory focus on online review valence and volume effects. Specifically, Study 1 showed that online review valence was more impactful for consumers with a promotion focus than for consumers with a prevention focus. The opposite was true for online review volume effects, where consumers with a prevention focus were influenced more by volume in their decision-making compared to consumers with a promotion focus. Study 2 showed that the pattern of results we found in Study 1 also applied to situational regulatory focus induced by the product category. The effect of review volume on sales rank was stronger for prevention-oriented products, whereas the effect of valence was stronger for promotion-oriented products.

In Study 1, one product category was involved in the study (Digital camera). Involving more different product categories will add reliability to the results of current research. Also, it can offer external validity to current research results. In Study 2, there was no exact measurement for sales, as Amazon.com does not share that kind of information. Instead, Sales Rank was used as a proxy variable. Future research could look into the websites that offer access to the exact sales information.

The current research findings suggest the need for companies to adapt their consumer review management strategy to the regulatory orientation of their target market and products. When a promotion-focused mindset is targeted, strategies for increasing the favorability of product reviews should be used, in contrast, tactics for increasing the quantity of reviews may be more suitable when a prevention-focused mindset is involved.

To the best of the authors' knowledge, this research is the first to investigate the interaction between regulatory focus of consumers and products and online review components.

The purpose of this study is to present a methodology for enhancing the quality and usefulness of online reviews for prospective customers to investigate how this contemporary form of instrumental support can be facilitated to strengthen customer-to-customer support.

This study develops an analytics framework with applications of machine learning models using customer review data from Amazon.com. Linear regression is commonly used for review helpfulness and sales prediction. In this study, Random Forest model is applied because of its strong performance and reliability. To advance the methodology, a custom script in Python is created to generate Partial Dependence Plots for intensive exploration of the dependency interpretations of review helpfulness and sales. The authors also apply K-Means to cluster reviewers and use the results to generate reviewer qualification scores and collective reviewer scores, which are incorporated into the review facilitation process.

The authors find the average helpfulness ratio of the reviewer as the most important determinant of reviewer qualification. The collective reviewer qualification for a product created based on reviewers’ characteristics is found important to customers’ purchase intentions and can be used as a metric for product comparison.

The findings of this study suggest that service improvement efforts can be performed by developing software applications to monitor reviewer qualifications dynamically, bestowing a badge to top quality reviewers, redesigning review sorting interfaces and displaying the consumer rating distribution on the product page, resulting in improved information reliability and consumer trust.

This study adds to the research on customer-to-customer support in the service literature. As customer reviews perform as a contemporary form of instrumental support, the authors validate the determinants of review helpfulness and perform an intensive exploration of its dependency interpretation. Reviewer qualification and the collective reviewer qualification scores are generated as new predictors and incorporated into the helpfulness-based review facilitation services.