Predicting shoppers' interest from social interactions using sociometric sensors

Kim, Taemie J.; Chu, Maurice; Brdiczka, Oliver; Begole, Bo

doi:10.1145/1520340.1520692

Cited by 17 publications

(6 citation statements)

References 6 publications

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“…Researchers can also perform indoor user localization by measuring received signal strength from fixed based stations, detecting proximal travel patterns. Figure 6 shows sample data from a couple shopping for furniture (Kim, Brdiczka, Chu, & Begole, 2009). A couple was asked to wear the sociometric badges during their time of strolling through a furniture store and browsing multiple items.…”

Section: Resultsmentioning

confidence: 99%

Sociometric badges: Using sensor technology to capture new forms of collaboration

Kim

McFee²,

Olguı́n³

et al. 2012

J Organ Behavior

131

116

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SummaryThis article introduces sociometric badges as a research tool that captures with great accuracy fine-scale speech patterns and body movements among a group of individuals at a scale that heretofore has been impossible in groups and teams studies. Such a tool offers great potential for studying the changing ecology of team structures and new modes of collaboration. Team boundaries are blurring as members disperse across multiple cultures, convene through various media, and operate in new configurations. As the how and why of collaboration evolves, an opportunity emerges to reassess the methods used to capture these interactions and to identify new tools that might help us create synergies across existing approaches to teams research. We offer sociometric badges as a complement to existing data collection methods-one that is well-positioned to capture real-time collaboration in new forms of teams. Used as one component in a multi-method approach, sociometric badges can capture what an observer or cross-sectional survey might miss, contributing to a more accurate understanding of group dynamics in new teams. We also revisit traditional teams research to suggest how we might use these badges to tackle long-standing challenges. We conclude with three case studies demonstrating potential applications of these sociometric badges.

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Section: Resultsmentioning

confidence: 99%

Sociometric badges: Using sensor technology to capture new forms of collaboration

Kim

McFee²,

Olguı́n³

et al. 2012

J Organ Behavior

131

116

View full text Add to dashboard Cite

show abstract

“…For example, we know that under some circumstances (disasters and citizen participation, for example), people act as social sensors of the world around them by sharing information; thus leveraging the scale of the online population. This leveraging then serves as a potential solution for general scalability issues associated with analytics of online behaviour (Bell, McDiarmid, & Irvine, ; Christakis & Fowler, ; Kim, Chu, Brdiczka, & Begole, ; Lazer et al, ). Examples from social media demonstrate how useful information can be culled from a vast ocean of data.…”

Section: Conclusion: the Future Development Of Social Sensorsmentioning

confidence: 99%

Connecting performance to social structure and pedagogy as a pathway to scaling learning analytics in MOOCs: an exploratory study

Goggins

Galyen

Petakovic

et al. 2016

Computer Assisted Learning

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This exploratory study focuses on the design and evaluation of teaching analytics that relate social learning structure with performance measures in a massive open online course (MOOC) prototype environment. Using reflexive analysis of online learning trace data and qualitative performance measures we present an exploratory empirical study that: (a) rigorously evaluates a novel, multi‐dimensional performance construct; (b) describes differences in small group dynamics and structure; and (c) draws a connection between learning performance and group structure. Performance is operationalized using a combination of knowledge construction measurement from discussion boards, rigorous analysis of student work products and several indicators of small group identity in the course examined. Interview and observational data are used to develop an approach for deriving and validating a model of the social structure of students in the course using traces of interaction data. The connection between performance and structure is developed at the small group unit of analysis. Implications for MOOC design, scaling MOOC analytics and a vision for developing social sensors in MOOC environments are presented in the discussion and conclusion.

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“…What is more, it should be noted that there are sensible differences in customer behavior depending on their interest level. When customers are interested in an item, they observe the item for a longer duration of time and have a more balanced speaking style (Kim et al 2009). It is also important to create for the contemporary customer a quick service with high quality resources and products without wasting time on extra activities (Stefanini et al 2018).…”

Section: Market Factormentioning

confidence: 99%

Building Relationships with Customer 4.0 in the Era of Marketing 4.0: The Case Study of Innovative Enterprises in Poland

Wereda

Woźniak

2019

Social Sciences

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1) Background: Contemporary enterprises are putting more and more emphasis on shaping lasting and effective relationships with clients. This is not an easy task, especially in the conditions of Marketing 4.0, which imposes on enterprises the need to holistically consider the needs as well as the skills and inventiveness of customers. It can be assumed that Clients 4.0 require both changes in the scope of business processes and shaping communication with their environment. That is why it is important today to skillfully and correctly determine the complexity of communication with Clients 4.0, as well as to determine the importance of communication with clients for the development of enterprises-especially those operating in innovative industries. (2) Methods: Empirical research was carried out on a sample of 100 innovative enterprises listed on the NewConnect market in Poland. The respondents were managers at various levels. Two indicators were constructed based on these assessments: Customer Relationships Importance Index (CRII) and Customer Communication Complexity Index (CCCI). (3) Results: It was determined that the complexity of communication of innovative enterprises with customers is at a high level. What is more, the importance of relationships with Customers 4.0 for the development of innovative enterprises is at a high level.It was also indicated that there is no correlation between the level of complexity of relationships with Customers 4.0 and the importance of these relationships for the development of innovative enterprises. (4) Conclusions: Based on the survey, it can be assumed that the relations with Clients 4.0 have the greatest share in shaping the development of innovative enterprises in the areas of minimizing the number of complaints, financial liquidity and efficiency of core business processes. It is also important that, in shaping the complexity of communication with Clients 4.0, enterprises mainly consider traditional phone calls and email account.

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Predicting shoppers' interest from social interactions using sociometric sensors

Cited by 17 publications

References 6 publications

Sociometric badges: Using sensor technology to capture new forms of collaboration

Sociometric badges: Using sensor technology to capture new forms of collaboration

Connecting performance to social structure and pedagogy as a pathway to scaling learning analytics in MOOCs: an exploratory study

Building Relationships with Customer 4.0 in the Era of Marketing 4.0: The Case Study of Innovative Enterprises in Poland

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