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The purpose of this paper is to investigate the effect of perceived product similarity and comparative ad claims on brand responses.

This study uses a two (similarity between the target product and the comparison product: relatively similar vs dissimilar) by three (product attributes of the target product: common to the comparison product, distinct from the comparison product, and a combination of common and distinct attributes) between-subjects factorial design with 300 Thai undergraduate students.

It finds that when perceived similarity between the products is high, a combination of superiority (distinct) and parity (common) ad claims lead to the best brand responses. When perceived similarity is low, superiority claims bring about the best brand responses.

It extends comparative advertising and category-substitution research by addressing the research gaps in perceived similarity and claim type.

Companies should emphasize a product’s superior attributes in general but a combination of common and superior attributes when the product is relatively similar to other products in comparative advertising.

This study provides new evidence that perceived product similarity moderates the effect of comparative ad claims on brand responses.

Because of short life cycle and fluctuating greatly in total sales volumes (TSV), it is difficult to accumulate enough sales data and mine an attribute set reflecting the common needs of all consumers for a kind of experiential product with short life cycle (EPSLC). Methods for predicting TSV of long-life-cycle products may not be suitable for EPSLC. Furthermore, point prediction cannot obtain satisfactory prediction results because information available before production is inadequate. Thus, this paper aims at proposing and verifying a novel interval prediction method (IPM).

Because interval prediction may satisfy requirements of preproduction investment decision-making, interval prediction was adopted, and then the prediction difficult was converted into a classification problem. The classification was designed by comparing similarities in attribute relationship patterns between a new EPSLC and existing product groups. The product introduction may be written or obtained before production and thus was designed as primary source information. IPM was verified by using data of crime movies released in China from 2013 to 2017.

The IPM is valid, which uses product introduction as input, classifies existing products into three groups with different TSV intervals, mines attribute relationship patterns using content and association analyses and compares similarities in attribute relationship patterns – to predict TSV interval of a new EPSLC before production.

Different from other studies, the IPM uses product introduction to mine attribute relationship patterns and compares similarities in attribute relationship patterns to predict the interval values. It has a strong applicability in data content and structure and may realize rolling prediction.

Nowadays, online platforms which provide products or services try to implement their homegrown communities to facilitate users' social interactions. Reviewers' activities in these communities can reflect their interests. Based on the theory of homophily, the authors aim to explore the impacts of the reviewer preference similarity and opinion similarity on the rate of product diffusion.

First, the authors construct reviewer similarity network based on their common interests and propose typical network metrics to measure reviewer preference similarity. Second, the authors measure reviewer opinion similarity with natural language processing. Finally, based on a panel data from an online video platform in China, both the fixed-effect and random-effect panel data models are constructed.

The authors find that reviewer preference similarity has a positive effect on the product diffusion, whereas reviewer opinion similarity has a negative effect on the diffusion. Furthermore, temporal distance moderates the relationship between reviewer similarity and the product diffusion. As a double-edged sword, review preference similarity hinders product diffusion in the initial phase, whereas benefits it in the later phase. Reviewer opinion similarity is always detrimental to product diffusion, especially in the initial phase.

This paper extends the understanding of homophily from the micro peer level to the group level by constructing reviewers' similarity network and highlights the important role of reviewer preference similarity and opinion similarity in product diffusion. The results also provide important insights for managers to design and implement diversity strategies for better product adoption in the community context.

The purpose of this study is to find out how electronic word of mouth (eWOM) may affect evaluations of products with different brand images. In particular, the study explores differential eWOM impacts across several brand types and extension categories.

An experiment with 2 (brand image: prestige/function) × 2 (category similarity: low/high) × 2 (eWOM message type: positive/negative) between-subjects design was used to examine the impacts of eWOM on different types of brand extensions. A total of 268 subjects from a public university in the Southwest participated in the study. Analysis of Variance (ANOVA) was used in analyzing the data.

The findings highlight the differential impact of eWOM on brand extension evaluations with different brand images. First, eWOM is more effective in influencing evaluations of functional brand extensions than prestige brand extensions. Second, whereas negative eWOM does equally bad on both high- and low-similarity brand extensions, positive eWOM is more effective in improving evaluations of high-similarity extensions than low-similarity extensions.

This study is the first to examine the impact of eWOM on products with different brand images. This is a critical issue for brand managers who allocate limited marketing resources to monitoring and managing vast amounts of eWOM activities. The findings provide important guidance for managing social media marketing communications.

The purpose of this paper is to propose a similarity-based approach to accurately retrieve reference solutions for the intelligent handling of online complaints.

This approach uses a case-based reasoning framework and firstly formalizes existing online complaints and their solutions, new online complaints, and complaint products, problems and content as source cases, target cases and distinctive features of each case, respectively. Then the process of using existing word-level, sense-level and text-level measures to assess the similarities between complaint products, problems and contents is explained. Based on these similarities, a measure with high accuracy in assessing the overall similarity between cases is designed. The effectiveness of the approach is evaluated by numerical and empirical experiments.

The evaluation results show that a measure simultaneously considering the features of similarity at word, sense and text levels can obtain higher accuracy than those measures that consider only one level feature of similarity; and that the designed measure is more accurate than all of its linear combinations.

The approach offers a feasible way to reduce manual intervention in online complaint handling. Complaint products, problems and content should be synthetically considered when handling an online complaint. The designed procedure of the measure with high accuracy can be applied in other applications that consider multiple similarity features or linguistic levels.

A method for linearly combining the similarities at all linguistic levels to accurately assess the overall similarities between online complaint cases is presented. This method is experimentally verified to be helpful to improve the accuracy of online complaint case retrieval. This is the first study that considers the accuracy of the similarity measures for online complaint case retrieval.

The purpose of this paper is to solve the problem of low accuracy in new product demand forecasting caused by the absence of historical data and inadequate consideration of influencing factors.

A hybrid new product demand forecasting model combining clustering analysis and deep learning is proposed. Based on the product similarity measurement, the weight of product similarity attributes is realized by using the method of fuzzy clustering-rough set, which provides a basis for the acquisition and collation of historical sales data of similar products and the determination of product similarity. Then the prediction error of Bass model is adjusted based on similarity through a long short-term memory neural network model, where the influencing factors such as product differentiation, seasonality and sales time on demand forecasting are embedded. An empirical example is given to verify the validity and feasibility of the model.

The results emphasize the importance of considering short-term impacts when forecasting new product demand. The authors show that useful information can be mined from similar products in demand forecasting, where the seasonality, product selling cycles and sales dependencies have significant impacts on the new product demand. In addition, they find that even in the peak season of demand, if the selling period has nearly passed the growth cycle, the Bass model may overestimate the product demand, which may mislead the operational decisions if it is ignored.

This study is valuable for showing that with the incorporation of the evaluation method on product similarity, the forecasting model proposed in this paper achieves a higher accuracy in forecasting new product sales.

The authors examine the spillover effects of CEO removal on the corporate financial policies of competing firms among S&P 1500 firms.

The authors used generalized estimating equations (GEE) on a sample of S&P 1,500 firms from 2000 to 2018 to test this study's research hypotheses. Return on assets (ROA), investment policy, and payout policy are used as proxies for corporate policies.

The authors found an increase in ROA and dividend payout in the immediate aftermath. Further, this study's hypothesis does not hold for R&D expenditure and net-working capital as the authors found an insignificant change in them in the immediate aftermath. However, the authors found a significant reduction in capital expenditure, supporting this study's hypothesis in the context of investment policy. Institutional investors and product similarity moderated the spillover effect on corporate policies (ROA, dividend payout, and capital expenditure).

The authors address a novel aspect of CEO performance-induced removal due to poor performance, i.e., the response of other CEOs to CEO performance-induced removal. This study's findings add to the literature supporting the bright side of CEOs' response to CEO performance-induced removal in peer firms due to poor performance.

The purpose of this paper is to explore how the strength of brand associations, different brand breadths, and the similarity between a parent brand's product categories and its extension product categories influence consumers' attitudes toward brand extensions.

An experimental research design was applied to testing the set of hypotheses. A total of 384 respondents participated in the main study. This study analyzed experimental results using analysis of variance (ANOVA).

The paper finds that when a brand is extended to similar product categories, only when the association is strong (trust or affect) will consumers prefer the extension of the narrow brand to that of the broad brand. Conversely, when a brand is extended to dissimilar product categories, regardless of the brand associations (trust or affect), consumers prefer the extension of the favorable broad brand to that of the narrow brand.

For corporations that operate within a narrow brand, brand extension strategies must be based on parent brand associations (trust or affect) that are very strong. In addition, the extension must only be to extremely similar product categories. In contrast, for corporations operating a broad brand, although the chance of brand extension success is better, favorability of consumer brand association (trust or affect) must never be ignored.

The study results reemphasize the importance of brand breadth effects when launching category extensions. Also, the research provides new insight into the strength of parent brand associations when evaluating consumers' brand attitude on brand extension.

This paper aims to study the appearance of drills from one brand by using currently available design tools. It aims to find and discuss the relationship between appearance innovation and maintaining key design features.

The innovation process is studied on drills of a Czech power tool maker and a previously created concept of a new drill. First, the authors explore the similarities between the designed concept and previous models of the brand by calculating the degree of similarity of given shape features. Second, they capture the drills simple shape grammar and strive to generate a sketch of the concept.

Results show the use of several similar shape features from previous models in the innovated design. Shape grammar can create a principally similar concept, but some innovations cannot be achieved this way. A description of appearance innovation within brand identity in terms of shape grammar is given.

The research is limited mainly to a small group of previous products that can be analyzed. It is done only for one particular brand identity. When used with the shape grammars, design generation is limited.

Better understanding of the innovative process aids designers in working with designs for brand identity and may serve to shape grammar enhancement.

The paper describes what happens during the innovation of product appearance and implicates enhancement and meaning of design analysis done by shape grammars and exploring similarities.