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The purpose of this study is to examine the role of digital infrastructure in supporting compliance with travel restrictions. The purpose of this study is to examine the role of digital infrastructure in supporting compliance with travel restrictions. In response to the COVID-19 pandemic, countries around the world have issued “stay-at-home” orders and curtailed a variety of economic activities. As countries have adopted aggressive policies to limit the spread of COVID-19, varying levels of national infrastructure to provide internet access have limited some nations’ ability to reduce travel requirements. As national policies struggle to address public health issues, location analytics enabled by big data provide unique insights regarding the efficacy of digital infrastructure. These insights can provide valuable tools to public health officials and regulators in understanding how health recommendations are implemented within an economy.

This study analyzes mobile phone movement data during the first half of 2020 and finds that countries that provided greater access to internet capabilities were better able to reduce work-related mobility.

This study’s findings indicate that greater levels of digital infrastructure may better prepare countries to adapt to societal disruptions such as COVID-19.

This study’s findings demonstrate that public health controls regarding movement and person-to-person interaction are less likely to be effective in nations with weaker digital infrastructure, even after accounting for variation attributable to gross domestic product (GDP) and pandemic severity. This could limit public health options in developing countries when faced with future socially disruptive events and encourage national investment in digital infrastructure.

This study’s findings highlight positive externalities associated with reducing the digital divide. Developing better digital business infrastructure globally may reduce human exposure to future pandemic risks.

This research demonstrates the practical development implications of analysis of aggregate data widely available through mobile technology. As institutions develop techniques to ethically and effectively analyze this data, greater opportunities to support economic development may be revealed.

Location-based services (LBS) have become an effective commercial marketing tool. However, regarding retail store location selection, it is challenging to collect analytical data. In this study, location-based social network data are employed to develop a retail store recommendation method by analyzing the relationship between user footprint and point-of-interest (POI). According to the correlation analysis of the target area and the extraction of crowd mobility patterns, the features of retail store recommendation are constructed.

The industrial density, area category, clustering and area saturation calculations between POIs are designed. Methods such as Kernel Density Estimation and K-means are used to calculate the influence of the area relevance on the retail store selection.

The coffee retail industry is used as an example to analyze the retail location recommendation method and assess the accuracy of the method.

This study is mainly limited by the size and density of the datasets. Owing to the limitations imposed by the location-based privacy policy, it is challenging to perform experimental verification using the latest data.

An industrial relevance questionnaire is designed, and the responses are arranged using a simple checklist to conveniently establish a method for filtering the industrial nature of the adjacent areas. The New York and Tokyo datasets from Foursquare and the Tainan city dataset from Facebook are employed for feature extraction and validation. A higher evaluation score is obtained compared with relevant studies with regard to the normalized discounted cumulative gain index.

Data mining along with its varied technologies like numerical mining, textual mining, multimedia mining, web mining, sentiment analysis and big data mining proves itself as an emerging field and manifests itself in the form of different techniques such as information mining; big data mining; big data mining and Internet of Things (IoT); and educational data mining. This paper aims to discuss how these technologies and techniques are used to derive information and, eventually, knowledge from data.

An extensive review of literature on data mining and its allied techniques was carried to ascertain the emerging procedures and techniques in the domain of data mining. Clarivate Analytic’s Web of Science and Sciverse Scopus were explored to discover the extent of literature published on Data Mining and its varied facets. Literature was searched against various keywords such as data mining; information mining; big data; big data and IoT; and educational data mining. Further, the works citing the literature on data mining were also explored to visualize a broad gamut of emerging techniques about this growing field.

The study validates that knowledge discovery in databases has rendered data mining as an emerging field; the data present in these databases paves the way for data mining techniques and analytics. This paper provides a unique view about the usage of data, and logical patterns derived from it, how new procedures, algorithms and mining techniques are being continuously upgraded for their multipurpose use for the betterment of human life and experiences.

The paper highlights different aspects of data mining, its different technological approaches, and how these emerging data technologies are used to derive logical insights from data and make data more meaningful.

The paper tries to highlight the current trends and facets of data mining.

Big Data is disrupting the fashion retail industry and revolutionising the traditional fashion business models. Nowadays, leading fashion brands and new start-ups are actively engaging with Big Data analytics to enhance their operations and maximise on profitability. In hope of motivating and providing direction to fashion retail managers, industry experts, and academics alike, the purpose of this paper is to consider the most recent and trending applications of Big Data in fashion retailing with the aim of concisely summarising the industry’s current position and status.

This conceptual paper provides a brief introduction to the emerging topic of Big Data in fashion retailing by briefly synthesising findings from industry, market and academic research.

Most existing fashion brands are yet to fully engage with Big Data. The authors find that the main reasons underlying the application of Big Data analytics in fashion are trend prediction, waste reduction, consumer experience, consumer engagement and marketing, better quality control, less counterfeits and shortening of supply chains. The authors also identify key challenges which must be overcome for the most fashionable industry to be able to capitalize on Big Data to understand and predict fashion consumer behaviour.

The brief synthesis provides a foundation for future investigations into the use of Big Data in fashion retailing.

This paper serves as an up-to-date introduction to how Big Data can transform fashion retailing and can act as a sound reference guide for fashion industry managers and professionals grappling with Big Data-related issues.

This paper aims to test whether passive Wi-Fi visitor analytics is a useful and effective method to measure consumer engagement towards food trucks located within an outdoor activation area at an Australian metropolitan university.

Using passive Wi-Fi visitor analytics to ping and track smart devices, data was collected over 90 weekdays capturing data from 522,548 unique smart devices.

The data collected in this feasibility study was able to identify the most and least popular food trucks by displaying the differences in both bounce and engagement rates, suggesting that passive Wi-Fi visitor analytics are feasible and useful in this context. Furthermore, the results also demonstrate that food truck vendors and marketers should not engage in random rotation, but instead remain static to try and increase familiarity.

Current visitor tracking technology (i.e. ticketed sales, sales data and survey) is limited as it may not provide an accurate measurement of foot traffic, identify engaged patrons who passed by but did not complete a purchase and be available due to commercial sensitivity and confidentiality. Thus, the current research is the first to examine customer engagement (i.e. unengaged walk-by vs engaged but bounced vs engaged sales) with food trucks within an activation area by using passive Wi-Fi visitor analytics.

当前的论文旨在研究被动 Wi-Fi 访客分析是否是衡量消费者对位于澳大利亚城市大学户外活动区域内的流动餐车的参与度的有用且有效的方法。

使用被动 Wi-Fi 访客分析来跟踪智能设备, 从 522,548 个独特的智能设备收集了超过 90 个工作日的数据。

该可行性研究中收集的数据能够通过显示跳出率和参与率的差异来识别最受欢迎和最不受欢迎的流动餐车, 这表明被动 Wi-Fi 访客分析在这种情况下是可行和有用的。 此外, 我们的结果还表明, 流动餐车供应商和营销人员不应随意轮换, 而应保持静止从而增加顾客熟悉度。

当前的访客跟踪技术（即售票销售、销售数据和调查）是有限的, 因为它可能无法：（1）提供客流量的准确测量; (2) 识别路过但未完成购买的参与顾客; (3) 由于商业敏感性和保密性而可用。 因此, 目前的研究是第一个通过使用被动 Wi-Fi 访客分析来检查激活区域内流动餐车的客户参与度（即, 未参与路过, 相比于参与但跳出, 相比于参与售出额）。

Increased fraudulent practices have heightened the need for innovation in anti-fraud programs, necessitating the development of analytics techniques for detecting and preventing fraud. The subject of fraud analytics will continue to expand in the future for public-sector organizations; therefore, this research examined the progress of fraud analytics in public-sector transactions and offers suggestions for its future development.

This study systematically reviewed research on fraud analytics development in public-sector transactions. The review was conducted from June 2021 to June 2023 by identifying research objectives and questions, performing literature quality assessment and extraction, data synthesis and research reporting. The research mainly identified 43 relevant articles that were used as references.

This research examined fraud analytics development related to public-sector financial transactions. The results revealed that fraud analytics expansion has not spread equally, as most programs have been implemented by governments and healthcare organizations in developed countries. This research also exposed that the analytics optimization in fraud prevention is higher than for fraud detection. Such analytics help organizations detect fraud, improve business effectiveness and efficiency, and refine administrative systems and work standards.

This research offers comprehensive insights for researchers and public-sector professionals regarding current fraud analytics development in public-sector financial transactions and future trends.

This study presents the first systematic literature review to investigate the development of fraud analytics in public-sector transactions. The findings can aid scholars' and practitioners' future fraud analytics development.

This paper aims to perform a longitudinal assessment of the visitors to the Mandela Portal using Web analytics over a period of seven years, between 2009 and 2015.

This paper is based on Web analytics methodology that consists of a four-step process and utilises the first and second steps with data collected using Google Analytics.

The research process found a number of trends relating to the Portal’s visitors, including changes in the ranking of countries from which the visitors accessed the website as well as the variety of language settings in the Web browsers. It identified some issues both with the Google Analytics tool and broader implications on the trustworthiness of data.

This paper provides an analysis of Web visitors to the Mandela Portal, offering trends that are only possible when viewed over a long period. It also explores issues of data trustworthiness.

Digital collection assessment has focused mainly on evaluating systems, metadata and usability. While use evaluation is discussed in the literature, there are no standard criteria and methods for how to perform assessment on use effectively. This paper asserts that use statistics have complexities that prohibit meaningful interpretation and assessment. The authors aim to discover the problems inherent in the assessment of digital collection use statistics and propose solutions to address such issues.

This paper identifies and demonstrates five inherent problems with use statistics that need to be addressed when doing assessment for digital collections using the statistics of assessment tools on local digital repositories. The authors then propose solutions to resolve the problems that present themselves upon such analysis.

The authors identified five problems with digital collection use statistics. Problem one is the difficulty of distinguishing different kinds of internet traffic. Problem two is the lack of direct correlation of a digital item to its multiple URLs, so statistics from external web analytics tools are not ideal. Problem three is the analytics tools’ inherent bias in statistics that are counted only in the positive way. Problem four is the different interaction between digital collections with search engine indexing. Problem five is the evaluator’s bias toward simple growing statistics over time for surmising a positive use assessment. Because of these problems, statistics on digital collections do not properly measure a digital library’s value.

Findings highlight problems with current use measures and offer improvements.

This paper identifies five problems that need to be addressed before a meaningful assessment of digital collection use statistics can take place. The paper ends with a call for evaluators to try to solve or mitigate the stated problems for their digital collections in their own evaluations.

The purpose of this study is to analyse the level of adoption of in-store analytics by brick-and-mortar retailers. Web analytics technology has been widely adopted by online retailers, and the technology to gather similar information in physical stores is already available. This study explores how such technology is valued and adopted by retailers.

This study is based on interviews and a focus group of 21 retail executives using a semi-structured interview methodology. An in-store analytics service was defined, along with specific key performance indicators (KPIs) and use cases to structure respondents' feedback.

Although noteworthy differences have been found in the value of KPIs and use cases by type of business, the main finding is that none of the respondents reached the stage of a brick-and-mortar data-driven company. In-store analytics services are in the early stages of Rogers' (1983) model of diffusion of innovations. Three main reasons are presented: lack of technology knowledge, budget priority and a data culture inside the companies.

The results should encourage scholars to further investigate the drivers accelerating the adoption of these technologies. Practitioners and solution providers should strive for improvement in the simplicity of their solutions.

This study is the first to analyse the level of adoption of in-store analytics from the perspective of retailers.