Understanding customer malling behavior in an urban shopping mall using smartphones

Lee, SangJeong; Min, Chulhong; Yoo, Chungkuk; Song, Junehwa

doi:10.1145/2494091.2497344

Cited by 38 publications

(7 citation statements)

References 14 publications

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“…When a person enters a shopping mall, the mobile provider gives impersonalized information to the mall data center (like gender, age, etc.). So, mall owners can provide their tenants with reliable information about the target audience (Lee et al 2013).…”

Section: Resultsmentioning

confidence: 99%

Antecedents and Consequences of Digital Entrepreneurial Ecosystems in the Interaction Process with Smart City Development

Gorelova¹,

Dmitrieva²,

Dedova³

et al. 2021

Administrative Sciences

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The nature of entrepreneurship and its developmental paths in the urban environment are extensively studied in the scientific literature. With a rising interest of scholars in the smart city phenomenon, the role entrepreneurship plays in the development of smart cities became a central topic in academia. However, there is a lack of discussion concerning the specific settings and characteristics of digital entrepreneurship in the smart city scenario. Nowadays, the concept of digital entrepreneurship is considered as a part of the digital entrepreneurial ecosystems (DEE) that provides an environment for effective entrepreneurial activities. Hence, the investigation on how DEE is interconnected with smart cities and how they both can contribute to their mutual development appears both timely and necessary. To reach this research objective, the authors, after giving a clear definition of each component of DEE based on an extensive literature review, consider its interconnection with the smart city model. The connection between the dimensions of a smart city and the structural constituents of DEE is also tracked, highlighting the contribution of each element to the development of a smart city. Through the creation of a comprehensive framework, the results of the paper show clearly that DEE is an inevitable part of a smart city environment. The research also covers the model of DEE engagement in smart city architecture.

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Section: Resultsmentioning

confidence: 99%

Antecedents and Consequences of Digital Entrepreneurial Ecosystems in the Interaction Process with Smart City Development

Gorelova¹,

Dmitrieva²,

Dedova³

et al. 2021

Administrative Sciences

View full text Add to dashboard Cite

show abstract

“…The system trains a trajectory classifier to label a given motif group as shopping or non-shopping. Lee et al [80] study customer malling behavior patterns in shopping mall over different activities, such as eating, shopping, reading, resting, and so on. They propose a computational framework, named MallingSense, to understand customers' behaviors in shopping malls.…”

Section: ) Coarse-grained Shopping Activitiesmentioning

confidence: 99%

Harnessing the Power of the General Public for Crowdsourced Business Intelligence: A Survey

et al. 2019

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Crowdsourced business intelligence (CrowdBI), which leverages the crowdsourced usergenerated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI.

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“…Shopping behavior: [28] places sensors on people to track their trajectories, and uses a clustering scheme to predict users' future behaviors (e.g., fast walking, idle walking, or stopping). [31] studies 701 hours of sensor data collected from 195 in-situ customers to understand customer behavior in shopping malls. [52] monitors customers' shopping time.…”

Section: Related Workmentioning

confidence: 99%

Enabling physical analytics in retail stores using smart glasses

Rallapalli

Ganesan

Chintalapudi

et al. 2014

Proceedings of the 20th Annual International Conference on Mobile Computing and Networking

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We consider the problem of tracking physical browsing by users in indoor spaces such as retail stores. Analogous to online browsing, where users choose to go to certain webpages, dwell on a subset of pages of interest to them, and click on links of interest while ignoring others, we can draw parallels in the physical setting, where a user might walk purposefully to a section of interest, dwell there for a while, gaze at specific items, and reach out for the ones that they wish to examine more closely.As our first contribution, we design techniques to track each of these elements of physical browsing using a combination of a firstperson vision enabled by smart glasses, and inertial sensing using both the glasses and a smartphone. We address key challenges, including energy efficiency by using the less expensive inertial sensors to trigger the more expensive vision processing.Second, during gazing, we present a method for identifying the item(s) within view that the user is likely to focus on based on measuring the orientation of the user's head.Finally, unlike in the online context, where every webpage is just a click away, proximity is important in the physical browsing setting. To enable the tracking of nearby items, even if outside the field of view, we use data gathered from smart-glasses-enabled users to infer the product layout using a novel technique called AutoLayout. Further, we show how such inferences made from a small population of smart-glasses-enabled users could aid in tracking the physical browsing by the many smartphone-only users.

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Understanding customer malling behavior in an urban shopping mall using smartphones

Cited by 38 publications

References 14 publications

Antecedents and Consequences of Digital Entrepreneurial Ecosystems in the Interaction Process with Smart City Development

Antecedents and Consequences of Digital Entrepreneurial Ecosystems in the Interaction Process with Smart City Development

Harnessing the Power of the General Public for Crowdsourced Business Intelligence: A Survey

Enabling physical analytics in retail stores using smart glasses

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