We employed a technique known as a systematic mapping study to carry out this study, which aids in developing a solid understanding of the subject matter. This method makes it possible to identify study trends and pinpoint knowledge gaps. As part of a methodical mapping effort, information regarding the locations of publications is acquired, and the information is subsequently used to create a map. It is possible to apply this strategy to various types of media, such as books, newspapers, and articles, although it is commonly used for published papers. The approach was applied in the following ways:
Research Questions
To comprehend and identify works that may be connected to online counterfeiting and anticounterfeiting strategies utilizing business intelligence, the following research questions were developed. General Questions for understanding the background of the domain, A specific question as a focused question, and statistical information for understanding the footprint of the studies were developed for this investigation. Understanding how business intelligence supports online anticounterfeiting efforts and the effects of online counterfeiting is the goal of these broad questions. The goal of FQs is to show how particular business intelligence technologies and approaches are applied to online anticounterfeiting efforts. Table 1 provides the answers to these queries.
Table 1
References
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Questions
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General Questions
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GQ1
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How is counterfeiting happening?
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GQ2
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What technologies/methods used for online anti-counterfeiting?
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Functional Questions
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FQ1
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The role of Business Intelligence in online anti-counterfeiting
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Statistical Questions
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SQ1
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Where were the results of the study published?
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SQ2
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How many articles are published each year?
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Research process
Using the research process suggested by (Petersen et al., 2008), we first specified the search string “Online counterfeiting”, “Online anticounterfeiting” and “Business Intelligence” as the major search terms. Synonyms were identified based on an online dictionary called Collins and are listed in Table 2. The other steps involve choosing the databases to apply and obtaining the results.
Table 2
Major Terms
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Synonyms
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Counterfeiting
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Online {fake OR copying OR Forging OR imitation}
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Business Intelligence
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N/A
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Business Intelligence Frameworks
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N/A
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Business Analytics
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Data Analytics
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Trademark infringement
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E-commerce
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The targeted search strings for the research databases were produced by the above terms. The last search term is displayed below. :
((counterfeiting OR Online fake OR Online imitation OR Online Forging OR Online anticounterfeiting OR Online counterfeiting with Business Intelligence OR Business Intelligence AND Online counterfeiting OR Business Intelligence Frameworks OR Business Analytics OR Data Analytics OR Trademark infringement AND Ecommerce AND Business Intelligence))
Research parameters were properly defined in the databases after the search string was created. The next step in the process was to determine which databases were relevant for the area of research covered by the search phrase. Six different research databases were ultimately chosen for this procedure based on their applicability and usefulness.
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Emerald
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IEEE Xplore
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Science direct
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Springer Library
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Taylor and Francis
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Wiley
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ACM Digital library
Using the advanced search capabilities of IEEE Xplore, a search string was added to the database. To produce more pertinent results, the research procedure at the Springer library involved an advanced search, employing the search criteria "extract phrases" and "with at least one of the words." In addition to the databases above, Google Scholar was also included in the query string for an advanced search that focused only on titles. The query string for the Emerald database search operation needed to be constructed using the embedded function. The search terms were input into the Wiley database using computer science as the subject to improve the relevancy of the results. The Sage database search's advanced search feature "everywhere" box was used to insert the aforementioned search query. The Science Direct database was used for the advanced search.
Filtering process
The most pertinent study papers were chosen from among the studies that were found through the aforementioned search. The following criteria were applied as inclusion criteria:
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IC1: The study is in a journal, workshop or conference
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IC2: Relevancy to online counterfeiting, online anticounterfeiting and business intelligence
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IC3: Should be a full paper
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IC4: Should be a thesis
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IC4: Should be a book chapter.
Additionally, we established the following exclusion criteria:
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EC1: should be published after 2008
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EC2: Studies published in the magazine.
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EC3: Studies that were not written in English
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EC4: Works not related to the current mapping study or research questions
The most pertinent studies were selected and extracted while excluding irrelevant studies according to the inclusion and exclusion criteria. The authors utilized the three-pass procedure recommended by choosing the studies and scanning them (Keshav and Cheriton, n.d.). First, the title, abstraction, and instructions for each extracted result were checked. Second, the paper was scanned based on the pertinent diagrams and figures, and third, the entire document was examined to ensure its applicability. The study by Khan et al. (2021) concentrated on a review of luxury consumption and offered a possible direction for further research. Frequent-pattern mining was proposed by Benatia et al. (2022) as a method for identifying counterfeit goods. The study also showed how pattern mining may be used for supply chain management and product traceability. Another study that is part of this one reviewed existing studies on counterfeiting and identified potential future directions (Baruönü Latif et al., 2018). Both Staake et al. (2009) and Samaddar and Menon (2020) provided reviews and recommendations for future research on the counterfeit trade. In their research, Chand and Fei (2021) used a structural equation model to develop hypotheses to explain why people in developing nations buy fake luxury goods. After the search process was complete, the research was meticulously reviewed and filtered to include only studies that met all the necessary inclusion criteria, namely, IC1, IC2, IC3, and IC4.
Once the filtration process was complete, the remaining papers were saved in Zotero, an open source reference management system. The relevant studies were then sorted and stored in their respective folders based on their database. Any duplicates were removed during this process.
3.4 Quick screening and full-text reading
This study used skin and scamming techniques to quickly scan titles, keywords, and abstracts to discover relevant articles. The goal was to speed up the search process. After this exercise, we found 37 articles covering online counterfeiting, business intelligence and counterfeiting, and counterfeiting methods on online eCommerce platforms. Once selected, the full-text sequence was scanned.
3.5 Snowballing and Quality Assessment
Following the full-text reading, we chose an initial batch of 32 out of 37 articles, which inspired additional research. For this purpose, forward and backwards snowballing was used (Wohlin, 2014) to ensure that no relevant studies were missing. A quality check was ultimately applied as described by Hoda et al. (2017). A quality assessment was performed to ensure that the chosen articles exceeded the minimal requirement of at least three of the following four criteria:
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Being in line with the goals and inquiries of the research
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The extracted data included pertinent data.
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This article's contribution to the research goal.
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Impact factor for journals
3.6 Data extraction and limitations
To explain the researcher's justification for suggesting a business intelligence design strategy for online anticounterfeiting initiatives, a systematic assessment of the final selection was carried out. After examining the prior work, the review's major body comprised a thorough analysis of business intelligence and online anticounterfeiting. Following a study of both primary and secondary research, the following information was taken from 32 chosen papers to answer the following research questions: (1) the title, (2) the authors, (3) the publication year, (4) the publication venue and its caliber index, and (5) the present business intelligence techniques. The coauthors examined and approved the first author's data extraction to guarantee sufficient data accuracy. According to previous reviews (Khan et al., 2021), assessment bias for extracted data is based on the author's perspective, and relatively few constraints were detected when conducting this study. When performing this study, we were unable to completely eliminate personal bias, but to increase the validity of the papers we chose, we carefully obtained the preapproval of all the coauthors. Using search strings against databases was another challenge we had to face. As some publications were disregarded within the search scope and excluded during the search process, we cannot guarantee that we were obtaining the entire set of studies. We considered adapting the procedure recommended by Mian et al. (2005), which offers a way to analyse search strings in the design phase to reduce risk and limitations.