Search results

The purpose of this study is to analyze the current scenario of private labels from consumers and retailers’ point of view and provide inputs to the retailers that will help them to increase their profitability. Profitability for retailers is a resultant of efficient inventory management in a limited space. This paper studies consumer’s purchase behavior and facilitates retailers in their decision-making of the dilemma between the appropriate mix of national brands (NBs) and private labels to increase their profitability. Retailers will be able to do cross-merchandising of the categories of the goods having strong associations and will increase the shelf space of the products, which are preferred by customers.

Market basket analysis was done for 1,223 transactions including two or more product categories in each transaction. In total, 564 products were studied and these products were further divided into 23 categories. Lift analysis was done 4 times to find an association between the products of all the categories.

The results find a strong association between some categories and advocate the placement of these combinations together – one being a NB and another private label.

Analysis of only a limited set of brands and their product categories for a value retailer cross-merchandising.

The analysis of sales transactions will help retailers in determining the associations between product categories. This association will be helpful in placing their private labels vis-à-vis NBs to do cross-merchandising and allocating judicial space to the product assortment to increase their profitability.

The purpose of this study is to analyse the suitability of photo-sharing platforms, such as Flickr, to extract relevant knowledge on tourists’ spatial movement and point of interest (POI) visitation behaviour and compare the most prominent clustering approaches to identify POIs in various application scenarios.

The study, first, extracts photo metadata from Flickr, such as upload time, location and user. Then, photo uploads are assigned to latent POIs by density-based spatial clustering of applications with noise (DBSCAN) and k-means clustering algorithms. Finally, association rule analysis (FP-growth algorithm) and sequential pattern mining (generalised sequential pattern algorithm) are used to identify tourists’ behavioural patterns.

The approach has been demonstrated for the city of Munich, extracting 13,545 photos for the year 2015. POIs, identified by DBSCAN and k-means clustering, could be meaningfully assigned to well-known POIs. By doing so, both techniques show specific advantages for different usage scenarios. Association rule analysis revealed strong rules (support: 1.0-4.6 per cent; lift: 1.4-32.1 per cent), and sequential pattern mining identified relevant frequent visitation sequences (support: 0.6-1.7 per cent).

As a theoretic contribution, this study comparatively analyses the suitability of different clustering techniques to appropriately identify POIs based on photo upload data as an input to association rule analysis and sequential pattern mining as an alternative but also complementary techniques to analyse tourists’ spatial behaviour.

From a practical perspective, the study highlights that big data sources, such as Flickr, show the potential to effectively substitute traditional data sources for analysing tourists’ spatial behaviour and movement patterns within a destination. Especially, the approach offers the advantage of being fully automatic and executable in a real-time environment.

The study presents an approach to identify POIs by clustering photo uploads on social media platforms and to analyse tourists’ spatial behaviour by association rule analysis and sequential pattern mining. The study gains novel insights into the suitability of different clustering techniques to identify POIs in different application scenarios.

本论文旨在分析图片分享平台Flickr对截取游客空间动线信息和景点（POI）游览行为的适用性, 并且对比最知名的几种聚类分析手段, 以确定不同情况下的POI。

本论文首先从Flickr上摘录下图片大数据, 比如上传时间、地点、用户等。其次, 本论文使用DBSCAN和k-means聚类分析参数来将上传图片分配给POI隐性变量。最后, 本论文采用关联规则挖掘分析（FP-growth参数）和序列样式勘探分析（GSP参数）以确认游客行为模式。

本论文以慕尼黑城市为样本, 截取2015年13,545张图片。POIs由DBSCAN和k-means聚类分析将其分配到有名的POIs。由此, 本论文证明了两种技术对不同用法的各自优势。关联规则挖掘分析显示了显著联系（support：1%−4.6%；lift：1.4%−32.1%）, 序列样式勘探分析确立了相关频率游览次序（support：0.6%−1.7%。

本论文的理论贡献在于, 根据图片数据, 通过对比分析不同聚类分析技术对确立POIs, 并且证明关联规则挖掘分析和序列样式勘探分析各有千秋又互相补充的分析技术以确立游客空间行为。

本论文的现实意义在于, 强调了大数据的来源, 比如Flickr,证明了其对于有效代替传统数据的潜力, 以分析在游客在一个旅游目的地的空间行为和动线模式。特别是这种方法实现了实时自动可操作性等优势。

本论文展示了一种方法, 这种方法通过聚类分析社交媒体上的上传图片以确立POIs, 以及通过关联规则挖掘分析和序列样式勘探分析来分析游客空间行为。本论文对于不同聚类分析以确立不同适用情况下的POIs的确立提出了独到见解。

The purpose of this exploratory study is to propose a new methodological approach to investigate brand associations. More specifically, the study aims to show how brand associations can be identified and analysed in an online community of international consumers of fashion to determine the degree of matching with company-defined brand associations.

The methodology is two-pronged, integrating qualitative market research techniques with quantitative text mining. It was applied to determine types and perceptions of brand associations among fashion bloggers with reference to three leading Italian fashion houses. These were then compared to brand associations found in company-generated texts to measure the degree of matching.

The results showed consistent brand associations across the three brands, as well as substantial matching with company-defined brand associations. In addition, the analysis revealed the presence of distinctive brand association themes that shed further light on how brand attributes were perceived by blog participants.

The methods described can be used by managers to identify and reinforce favourable brand associations among consumers. This knowledge can then be applied towards developing and implementing effective brand strategies.

The authors propose an interdisciplinary approach to investigate brand associations in online communities. It incorporates text mining and computer-assisted textual analysis as techniques borrowed from the field of linguistics which have thus far seen little application in marketing studies, but can nonetheless provide important insights for strategic brand management.

In academic work, it is important to identify a specific domain of research. Many researchers may look to conference issues to determine interesting or new topics. Furthermore, conference issues can help researchers identify current research trends in their field and learn about cutting-edge developments in their area of specialization. However, so much conference information is published online that it can be difficult to navigate and analyze in a meaningful or productive way. Hence, the use of knowledge management (KM) could be a way to resolve these issues. In KM, ontology is widely adopted, but most ontology construction methods do not consider social information between target users. Therefore, this study aims to propose a novel method of constructing research topic maps using an open directory project (ODP) and social information.

The approach is to incorporate conference information (i.e. title, keywords and abstract) as sources and to consider the ways in which social information automatically produces research topic maps. The methodology can be divided into four modules: data collection, element extraction, social information analysis and visualization. The data collection module collects the required conference data from the internet and performs pre-processing. Then, the element extraction module extracts topics, associations and other basic elements of topic maps while considering social information. Finally, the results will be shown in the visualization module for researchers to browse and search.

The results of this study propose three main findings. First, creating topic maps with the ODP category information can help capture a richer set of classification associations. Second, social information should be considered when constructing topic maps. This study includes the relationship among different authors and topics to support information in social networks. By considering social information, such as co-authorship/collaborator, this method helps researchers find research topics that are unfamiliar but interesting or potential cooperative opportunities in the future. Third, this study presents topic maps that show a clear and simple pathway in interested domain knowledge.

First, this study analyzes and collects conference information, including the titles, keywords and abstracts of conference papers, so the data set must include all of the abovementioned information. Second, social information only analyzes co-authorship associations (collabship associations); other social information could be extracted in the future study. Third, this study only analyzes the associations between topics. The intensity of associations is not discussed in the study.

The study will have a great impact on learned societies because it bridges the gap between theory and practice. The study is useful for researchers who want to know which conferences are related to their research. Moreover, social networks can help researchers expand and diversify their research.

With society’s growing environmental concern, developing a green brand identity provides cities with opportunities to enhance their competitiveness. Nevertheless, few studies have explored green city branding and specifically considered the diverse perceptions of multiple stakeholders. Accordingly, this study aims to explore green city branding from the perceptions of multiple stakeholders.

Based on associative network theory, the study uses brand concept maps and network analysis approaches to construct and analyze the content and structure of mental models among local residents and foreign tourists for a green city brand. This study further seeks empirical support for the findings via a survey, using the sample case of Yilan County in Taiwan.

The results of this study reveal that foreign tourists possess a more diverse and heterogeneous brand perception than local residents. Additionally, the study uncovers significant green city brand associations regarding their influences on the behavioral decisions of local residents and foreign tourists.

This research is the first attempt to advance the knowledge of green city branding by empirically exploring the green city brand perceptions of multiple stakeholders based on associative network theory. The results provide brand researchers with different analytical perspectives on the existing knowledge about city brand perceptions and offer strategic information for city managers.

Nowadays, database management system has been applied in library management, and a great number of data about readers’ visiting history to resources have been accumulated by libraries. A lot of important information is concealed behind such data. The purpose of this paper is to use a typical data mining (DM) technology named an association rule mining model to find out borrowing rules of readers according to their borrowing records, and to recommend other booklists for them in a personalized way, so as to increase utilization rate of data resources at library.

Association rule mining algorithm is applied to find out borrowing rules of readers according to their borrowing records, and to recommend other booklists for them in a personalized way, so as to increase utilization rate of data resources at library.

Through an analysis on record of book borrowing by readers, library manager can recommend books that may be interested by a reader based on historical borrowing records or current book-borrowing records of the reader.

If many different categories of book-borrowing problems are involved, it will result in large length of encoding as well as giant searching space. Therefore, future research work may be considered in the following aspects: introduce clustering method; and apply association rule mining method to procurement of book resources and layout of books.

The paper provides a helpful inspiration for Big Data mining and software development, which will improve their efficiency and insight on users’ behavior and psychology.

The paper proposes a framework to help users understand others’ behavior, which will aid them better take part in group and community with more contribution and delightedness.

DM technology has been used to discover information concealed behind Big Data in library; the library personalized recommendation problem has been analyzed and formulated deeply; and a method of improved association rules combined with artificial bee colony algorithm has been presented.

The purpose of this paper is to provide a framework for understanding core technological competencies and identifying the trends on the technological convergence of a business ecosystem using the patent information of leading firms in the system.

The proposed framework is composed of two steps: time-sequential text clustering analysis for comprehending changes in general technological fields and association rule analysis for identifying the trends of convergences in each field. The authors applied the proposed framework to the patents applied to United States Patent Trademark Office by Samsung Electronics, a market leader of the electronics industry, during the period from 2000 to 2011.

In the sequential text clustering analysis, trends of 14 technological fields such as data storage medium and data processing, mobile, lights and heats and memory are identified. Moreover, changes of technological convergence in each field are identified using association rule analysis. For instance, in the case of technologies related to lights and heats, convergences occurred between radio transmission systems and modulated-carrier systems during the period from 2000 to 2001. However, recent convergences appeared between technologies regarding controlling lights and liquid crystal materials since 2008.

Utilization of the framework will suggest new business opportunities to SMEs in a business ecosystem by identifying the trends of technological convergences.

The General Society Survey(GSS) is a kind of government-funded survey which aims at examining the Socio-economic status, quality of life, and structure of contemporary society. GSS data set is regarded as one of the authoritative source for the government and organization practitioners to make data-driven policies. The previous analytic approaches for GSS data set are designed by combining expert knowledges and simple statistics. By utilizing the emerging data mining algorithms, we proposed a comprehensive data management and data mining approach for GSS data sets.

The approach are designed to be operated in a two-phase manner: a data management phase which can improve the quality of GSS data by performing attribute pre-processing and filter-based attribute selection; a data mining phase which can extract hidden knowledge from the data set by performing data mining analysis including prediction analysis, classification analysis, association analysis and clustering analysis.

According to experimental evaluation results, the paper have the following findings: Performing attribute selection on GSS data set can increase the performance of both classification analysis and clustering analysis; all the data mining analysis can effectively extract hidden knowledge from the GSS data set; the knowledge generated by different data mining analysis can somehow cross-validate each other.

By leveraging the power of data mining techniques, the proposed approach can explore knowledge in a fine-grained manner with minimum human interference. Experiments on Chinese General Social Survey data set are conducted at the end to evaluate the performance of our approach.

An increasing amount of attention is being paid to three-dimensional (3D) printing technology. The technology itself is based on diverse technologies such as laser beams and materials. Hence, 3D printing technology is a converging technology that produces 3D objects using a 3D printer. To become technologically competitive, many companies and nations are developing technologies for 3D printing. So to know its technological evolution is meaningful for developing 3D printing in the future. The paper aims to discuss these issues.

To get technological competitiveness of 3D printing, the authors should know the most important and essential technology for 3D printing. An understanding of the technological evolution of 3D printing is needed to forecast its future technologies and build the R & D planning needed for 3D printing. In this paper, the authors propose a methodology to analyze the technological evolution of 3D printing. The authors analyze entire patent documents related to 3D printing to construct a technological evolution model. The authors use the statistical methods such as time series regression, association analysis based on graph theory, and principal component analysis for patent analysis of 3D printing technology.

Using the proposed methodology, the authors show the technological analysis results of 3D printing and predict its future aspects. Though many and diverse technologies are developed and involved in 3D printing, the authors know only a few technologies take lead the technological evolution of 3D printing. In this paper, the authors find this evolution of technology management for 3D printing.

If not all, most people would agree that 3D printing technology is one of the leading technologies to improve the quality of life. So, many companies have developed a number of technologies if they were related to 3D printing. But, most of them have not been considered practical. These were not effective research and development for 3D printing technology. In the study, the authors serve a methodology to select the specific technologies for practical used of 3D printing.

Diverse predictions for 3D printing technology have been introduced in many academic and industrial fields. Most of them were made by subjective approaches depended on the knowledge and experience of the experts concerning 3D printing technology. So, they could be fluctuated according to the congregated expert groups, and be unstable for efficient R & D planning. To solve this problem, the authors study on more objective approach to predict the future state of 3D printing by analyzing the patent data of the developed results so far achieved. The contribution of this research is to take a new departure for understanding 3D printing technology using objective and quantitative methods.

This study aims to examine companies’ socially responsible behaviors by studying the relationship between different proxies of corporate environmental responsibility (CER) and earnings management (EM) with emphasis on companies’ pro-environmental behavior and business ethics domain.

This meta-analysis synthesized the results from 31 studies with 110,024 firm-year observations concerning the relationship between CER and EM. The study has used corporate environmental disclosure index, corporate environmental performance ratings (CEPR), corporate environmental performance indicator and environmental regulations as proxies for CER to investigate the meta-results. Furthermore, the research then used emission level (measured using per capita CO₂ in metric tons), human development index for economic development, number of environmental mandatory policies (measured using Carrots and Sticks Report 2023) and western vs eastern culture as moderator variables.

The findings of this study revealed a significant negative relationship between CER and EM. Among different combined proxies of CER, CEPR reveal a significant and negative relationship with EM. Furthermore, the study suggests that future studies can explore this understudied area using proxies of EM, i.e. real EM, earnings persistence, value relevance and accounting conservatism.

This study offers insights to managers for transparent auditing and supports CER as a long-term sustainability plan. The regulators need to develop a global framework for environmental responsibility that does not compromise the quality of nonfinancial disclosers.

The findings of this study provide valuable insights for investors to make more informed decisions regarding green investments and suggest implications for policymakers to promote policies related to environmental sustainability and corporate transparency, thereby benefiting both investors and society. On a global scale, this study contributes to discussions concerning the alignment of corporate behavior with long-term environmental and financial integrity.

The meta-analysis addresses the long-standing two-decade debate of 2003–2023 on whether companies use CER as a transparency tool or use it as a greenwash to conceal their unethical earnings practices. To the best of the authors’ knowledge, this is the first meta-analysis to provide a comprehensive view to measure CER using different proxies to examine corporate ethical earnings behavior.