Cognitive Analytics

Gudivada, Venkat N.; Irfan, Mohammad T.; Fathi, Ehsan; Rao, Dhana

doi:10.1016/bs.host.2016.07.010

Cited by 58 publications

(14 citation statements)

References 28 publications

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“…The development of new technology such as cognitive computing for data management and analytics allows brick-and-mortar retailers to leverage data in their business processes. Notable technologies like machine learning (ML), natural language processing (NLP), big data, Internet of Things (IoT) and cloud computing enable cognitive systems (Gudivada, 2016). Analytics services of the cognitive system are descriptive, diagnostics, predictive, prescriptive and cognitive analytics (Behera et al , 2019).…”

Section: Introductionmentioning

confidence: 99%

Retail atmospherics effect on store performance and personalised shopper behaviour: a cognitive computing approach

Behera

Bala

Vijay

et al. 2021

IJOEM

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PurposeThe best possible way for brick-and-mortar retailers to maximise engagement with personalised shoppers is capitalising on intelligent insights. The retailer operates differently with diversified items and services, but influencing retail atmospheric on personalised shoppers, the perception remains the same across industries. Retail atmospherics stimuli such as design, smell and others create behavioural modifications. The purpose of this study is to explore the atmospheric effects on brick-and-mortar store performance and personalised shopper's behaviour using cognitive computing based in-store analytics in the context of emerging market.Design/methodology/approachThe data are collected from 35 shoppers of a brick-and-mortar retailer through questionnaire survey and analysed using quantitative method.FindingsThe result of the analysis reveals month-on-month growth in footfall count (46%), conversation rate (21%), units per transaction (27%), average order value (23%), dwell time (11%), purchase intention (29%), emotional experience (40%) and a month-on-month decline in remorse (20%). The retailers need to focus on three control gates of shopper behaviour: entry, browsing and exit. Attention should be paid to the cognitive computing solution to judge the influence of retail atmospherics on store performance and behaviour of personalised shoppers. Retail atmospherics create the right experience for individual shoppers and forceful use of it has an adverse impact.Originality/valueThe paper focuses on strategic decisions of retailers, the tactical value of personalised shoppers and empirically identifies the retail atmospherics effect on brick-and-mortar store performance and personalised shopper behaviour.

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Section: Introductionmentioning

confidence: 99%

Retail atmospherics effect on store performance and personalised shopper behaviour: a cognitive computing approach

Behera

Bala

Vijay

et al. 2021

IJOEM

View full text Add to dashboard Cite

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“…(2) diagnostic analytics, which explains why things happened; (3) predictive analytics, which, by using predictive models, forecasts what is likely to happen based on observed data; (4) prescriptive analytics, which recommends a course of action based on the results of a predictive model; and (5) cognitive analytics, which exploits the advances in ML and AI (i.e., intelligent systems) through High Performance Computing to develop analytic models with a human-like intelligence (Dey, 2016;Gudivada et al, 2016;Lepenioti et al, 2020). Note that these approaches open new possibilities on analysing Psychology data that go beyond traditional summary statistics (i.e., mean, median, range, and standard deviations), correlation/regression analyses, and the assessment of psychometric properties of a clinical instrument (Cuartas Arias, 2017).…”

Section: Data Explosion In Psychology: a Place For Data Sciencementioning

confidence: 99%

Machine Learning based Psychology: Advocating for A Data-Driven Approach

Vélez

2021

Int. j. psychol. res.

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Since its beginnings, Psychology has been prone to both data generation and understanding of human behavior through data analysis. Back in 1879, Dr. Wilheim Wundt opened the first experimental psychology lab at the University of Leipzig to study reaction times. To many, this is considered the start of Psychology as a separate scientific discipline and the use of data analysis for data-driven decision making in the field (Flis, 2019; Tweney, 2003). In this Editorial, we briefly discuss how Psychology students, clinicians, and researchers may take part of the data revolution and help transforming Psychology, as we know it, into Machine Learning Psychology.

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“…After a brief reading in the 32 abstracts, we classified them in six main categories as shown in Table 3. (Malekian et al, 2020), (Vijayalakshmi & Venkatachalapathy, 2019), , (Boncea et al, 2019), (Pablo, 2020), (Cheng & Zhang, 2020), (Aveleyra et al, 2018), (Tsiakmaki & Kostopoulos, 2020), (Czibula et al, 2019), , (Monllaó Olivé, et al, 2020), (Chunqiao et al, 2018), (Preuveneers et al, 2020), (Kőrösi & Farkas, 2020), (Injadat et al, 2020), (Qiu et al, 2019) Representation Learning (Zhang et al, 2019) Knowledge (Lee & Yeung, 2019), (Yang & Cheung, 2018), (Sha & Hong, 2017) Tracing Pedagogical Data Analytics (Guo & Zeng, 2020), (Hernández-Blanco, 2019), (Gudivada, et al, 2016) Decision Support System (Gutu-Robu et al, 2018), (Stoica, et al, 2019), (Holmes, 2020), (Moore et al, 2019), (Pensel & Kramer, 2020) Evaluation (Doleck et al, 2020) As expected, most of the papers (20) published belong to the Student Modeling category. Basically, the general focus represents cognitive aspects of student activities, such as analyzing students' performance, isolating underlying misconceptions, representing students' goals and plans, identifying prior and acquired knowledge, maintaining an episodic memory, and describing personality characteristics (Bakhshinategh et al, 2017).…”

Section: Keywording Using Abstractsmentioning

confidence: 99%

“…Transfer learning and neural network (Gudivada, et al, 2016) To propose a reference architecture for cognitive analytics and indicate ways to implement the architecture General (Czibula et al, 2019) To predict the final grade of students of a certain academic class…”

Section: Data Extraction and Mapping Processmentioning

confidence: 99%

Use of the Tensorflow Framework to Support Educational Problems: A Systematic Mapping

Padilha¹,

Catrambone²

2021

International Conference on Education

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The Google framework called TensorFlow has been widely used for decision making in several areas, including Education. Predicting student risk and optimizing a student’s learning path are, for example, two traditional educational problems that have been explored for years but there are a myriad of different data mining approaches involved. This paper’s goal is to illustrate the results of a systematic mapping process conducted on educational data mining studies using the TensorFlow framework. Furthermore, this paper will assist in illustrating what kind of problems to focus on (which can paradoxically be seen as opportunities), identify, demonstrate, and catalogue all the academic studies that have discussed it, and the approaches leveraged (neural network, decision tree, natural language processing, and so on). The mapping process followed five phases with rigor, returning a set of 32 relevant papers in the study area with detailed information related to the research questions. The outcome of this systematic study will be of benefit to academic managers, researchers, and students who use this framework as support to solve educational problems. Keywords: systematic mapping; Educational problems; TensorFlow; data mining

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Cognitive Analytics

Cited by 58 publications

References 28 publications

Retail atmospherics effect on store performance and personalised shopper behaviour: a cognitive computing approach

Retail atmospherics effect on store performance and personalised shopper behaviour: a cognitive computing approach

Machine Learning based Psychology: Advocating for A Data-Driven Approach

Use of the Tensorflow Framework to Support Educational Problems: A Systematic Mapping

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