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The purpose of this paper is to utilize co-query volumes of brands as relatedness measurement to understand the market structure and demonstrate the usefulness of brand relatedness via a real-world case.

Using brand relatedness measurement obtained using data from Google Trends as data inputs into a multidimensional scaling method, the market structure of the automobile industry is presented to reveal its competitive landscape. The relatedness with brands involved in product-harm crisis is further incorporated in empirical models to estimate the influence of crisis on future sales performance of each brand. A representative incident of a product-harm crisis in the automobile industry, which is the 2009 Toyota recall, is investigated. A panel regression analysis is conducted using US and world sales data.

The use of co-query as brand relatedness measurement is validated. Results indicate that brand relatedness with a brand under crisis is positively associated with future sales for both US and global market. Potential presence of negative spillovers from an affected brand to innocent brands sharing common traits such as same country of origin is shown.

The brand relatedness measured from co-query volumes is considered as a broad concept, which encompasses all associative relationships between two brands perceived by the consumers. This study contributes to the literature by clarifying the concept of brand relatedness and proposing a measure with readily accessible data. Compared to previous studies relying on a vast amount of online data, the proposed measure is proven to be efficient and enhance predictions about the future performance of brands in a turbulent market.

The purpose of this paper is to propose methods for fast incremental indexing with effective and efficient query processing in XML element retrieval. The effectiveness of a search system becomes lower if document updates are not handled when these occur frequently on the Web. The search accuracy is also reduced if drastic changes in document statistics are not managed. However, existing studies of XML element retrieval do not consider document updates, although these studies have attained both effectiveness and efficiency in query processing. Thus, the authors add a function for handling document updates to the existing techniques for XML element retrieval.

Though it will be important to enable fast updates of indices, preliminary experiments have shown that a simple incremental update approach has two problems: some kinds of statistics are inaccurate, and it takes a long time to update indices. Therefore, two methods are proposed: one to approximate term weights accurately with a small number of documents, even for dynamically changing statistics; and the other to eliminate unnecessary update targets.

Experimental results show that this proposed system can update indices up to 32 per cent faster than the simple incremental updates while the search accuracy improved by 4 per cent compared with the simple approach. The proposed methods can also be fast and accurate in query processing, even if document statistics change drastically.

The paper shows that there could be a more practical XML element search engine, which can access the latest XML documents accurately and efficiently.

Being an important data exchange and information storage standard, XML has generated a great deal of interest and particular attention has been paid to the issue of XML indexing. Clear use cases for structured search in XML have been established. However, most of the research in the area is either based on relational database systems or specialized semi‐structured data management systems. This paper aims to propose a method for XML indexing based on the information retrieval (IR) system Okapi.

First, the paper reviews the structure of inverted files and gives an overview of the issues of why this indexing mechanism cannot properly support XML retrieval, using the underlying data structures of Okapi as an example. Then the paper explores a revised method implemented on Okapi using path indexing structures. The paper evaluates these index structures through the metrics of indexing run time, path search run time and space costs using the INEX and Reuters RVC1 collections.

Initial results on the INEX collections show that there is a substantial overhead in space costs for the method, but this increase does not affect run time adversely. Indexing results on differing sized Reuters RVC1 sub‐collections show that the increase in space costs with increasing the size of a collection is significant, but in terms of run time the increase is linear. Path search results show sub‐millisecond run times, demonstrating minimal overhead for XML search.

Overall, the results show the method implemented to support XML search in a traditional IR system such as Okapi is viable.

The paper provides useful information on a method for XML indexing based on the IR system Okapi.

The brand crisis literature remains unilateral and scattered, necessitating academic effort to comprehend the extant body of knowledge. This study aims to provide the required comprehensive overview of the domain, by outlining its significance, progression and future research directions.

Following the PRISMA approach, journal articles for review are selected. The study uses a hybrid (structured and bibliometric) review, to provide a systematic insight and graphical visualization of the existing literature. It applies VOSviewer software to analyse bibliographic data through citation and co-occurrence analysis.

The hybrid review outlines most-cited articles, authors, frequently used theories, methodologies and data analysis techniques in this domain. Findings are further presented as integrative framework that distinctly highlights prior studies from a dichotomous perspective and across three stages of crisis. Finally, research opportunities and directions for future research are presented.

The study is useful for scholars and practitioners to understand the brand crisis literature and to cognize the inferences drawn by distinct researchers. It provides contemporary research agendas using the theory, context and method (TCM) framework, to augment future investigations through interdisciplinary approach.

To the best of our knowledge this is the first study that synthesizes the academic work of brand crisis using a hybrid method. Also, the novelty of the work lies in presenting the future research direction in the form of multiple (macro, meso and micro) levels with inter-disciplinary theoretical underpinnings.

The purpose of this paper is to identify potential competitive relationships among brands by analyzing the dynamic clicking behavior of consumers.

Consumer sequential online click data, collected from JD.com, is used to analyze the dynamic competitive relationship between brands. It is found that the competition intensity across categories of products can differ considerably. Consumers exhibit big differences in purchasing time of durable-like goods, that is, the purchasing probability of such products changes considerably over time. The local polynomial regression model (LPRM) is used to analyze the relationship between brand competition of durable-like goods and the purchasing probability of a particular brand.

The statistical results of collective behaviors show that there is a 90/10 rule for the category durable-like goods, implying that ten percent of the brands account for 90 percent market share in terms of both clicking and purchasing behavior. The dynamic brand cognitive process of impulsive consumers displays an inverted V shape, while cautious consumers display a double V shaped cognitive process. The dynamic consumers’ cognition illustrates that when the brands capture a half of the click volume, the brands’ competitiveness reaches to its peak and makes no significant different from brands accounting for 100 percent of the click volume in terms of the purchasing probability.

There are some limitations to the research, including the limitations imposed by the data set. One of the most serious problems in the data set is that the collected click-stream is desensitized severely, restricting the richness of the conclusions of this study. Second, the data set consists of many other consumer behavioral data, but only the consumer’s clicking behavior is analyzed in this study. Therefore, in future research, the parameters brand browsing by consumers and the time of browsing in each brand should be added as indicators of brand competitive intensity.

The authors study brand competitiveness by analyzing the relationship between the click rate and the purchase likelihood of individual brands for durable-like products. When the brand competitiveness is less than 50 percent, consumers tend to seek a variety of new brands, and their purchase likelihood is positively correlated with the brand competitiveness. Once consumers learn about a particular brand excessively among all other brands at a period of time, the purchase likelihood of its products decreases due to the thinner consumer’s short-term loyalty the brand. Till the brand competitiveness runs up to 100 percent, consumers are most likely to purchase a brand and its product. That indicates brand competitiveness maintain 50 percent of the whole market is most efficient to be profitable, and the performance of costing more to improve the brand competitiveness might make no difference.

There are many studies on brand competition, but most of these research works analyze the brand’s marketing strategy from the perspective of the company. The limitation of this research is that the data are historical and failure to reflect time-variant competition. Some researchers have studied brand competition through consumer behavior, but the shortcoming of these studies is that it does not consider sequentiality of consumer behavior as this study does. Therefore, this study contributes to the literature by using consumers’ sequential clicking behavior and expands the perspective of brand competition research from the angle of consumers. Simultaneously, this paper uses the LPRM to analyze the relationship between consumer clicking behavior and brand competition for the first time, and expands the methodology accordingly.