“Custolytics”

2019 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Samir Yerpude ◽  
Tarun Kumar Singhal
Keyword(s):  
Real Time ◽  
New Product Development ◽  
Emerging Market ◽  
Customer Engagement ◽  
Time Data ◽  
Loyalty Program ◽  
Content Type ◽  
Real Time Data ◽  
Customer Analytics ◽  
The Impact

Purpose The purpose of this paper is to build a customer engagement strategy for an emerging market using the Internet of Things (IoT) origin real-time data analytics for a classical retail business to customer domain. Design/methodology/approach The study presented is twofold. First, it empirically tests a theoretical model where the impact of different parameters influencing customer engagement are validated, and its influence on the resultant parameters, i.e. brand loyalty and brand ambassador, is analyzed. Second, it emphasizes on the use of real-time IoT origin data in customer analytics to determine a customer engagement strategy. Findings Results indicate that the four parameters, i.e., value propositions basis the buying patterns, loyalty programs, personalized communication and involving the customer in the new development process are influencing customer engagement positively, whereas the parameter loyalty program scores the maximum regression weight. IoT plays a crucial role in generating the real-time data used for generating customer analytics that proves to be vital for the longevity of the organization. Practical implications The organizations need judicious blend of four parameters such as value proposition based on buying patterns, participation in new product development, personalized communication and loyalty program while designing the customer engagement strategy. Results drawn from the focused group interview highlight the power of IoT origin real-time data in the customer analytics further strengthening the need of customer centricity in an organization. Originality/value Identified need of building a customer engagement strategy for an emerging market with the help of IoT data is addressed in this paper that is identified as an unexplored area and a research gap.

Digitally driven new product development: an involved contemporary innovation case

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Samir Yerpude ◽  
Sonica Rautela
Keyword(s):  
Product Development ◽  
Sentiment Analysis ◽  
Real Time ◽  
New Product Development ◽  
New Product ◽  
Time Data ◽  
Content Type ◽  
Customer Data ◽  
Real Time Data ◽  
The Impact

PurposeThe purpose of this paper is to study the impact of real-time data emerging from implementation of the Internet of Things (IoT) and netnography on the efficiency of the new product development (NPD).Design/methodology/approachCustomer-oriented organizations are the ones that survive in the market with a flow of new products to the market. Expectations like reduced timelines with quality focus provoke innovations. Customer inputs become the soul for a successful product wherein it becomes important to keep a constant stream of information flow back from the market. Literature review states that real-time data gathering with the implementation of IoT ensures the same. Along with real-time data, researchers have envisaged the need to identify the customer persona before incorporating customer opinion and sentiments vide netnography.FindingsThe organization can leverage the collaboration of IoT origin real-time data and sentiment analysis to effectively manage the NPD. Real-time customer data coupled with customer opinions and sentiments prove to be a game changer in the NPD process.Originality/valueThe originalities of this study are impact of IoT origin real-time data coupled with sentiment analysis on the NPD process. While impact of IoT origin data is reported in isolation similar to sentiment analysis, influence of collaboration of real-time data with sentiment analysis on NPD process is reported in this study.

Enhancing New Product Development Effectiveness With Internet of Things Origin Real Time Data

2018 ◽  
Vol 20 (3) ◽  
pp. 21-35 ◽  
Author(s):  
Samir Yerpude ◽  
Tarun Kumar Singhal
Keyword(s):  
Product Development ◽  
Real Time ◽  
New Product Development ◽  
New Product ◽  
Product Development Process ◽  
Primary Data ◽  
Time Data ◽  
Organizational Boundaries ◽  
Real Time Data ◽  
The Impact

The purpose of this article is twofold. First is to ascertain and establish the collaboration required between different stakeholders in the fundamental process of New Product Development (NPD). Augmentation of the process with the IoT origin real time data to enrich the efficacy of the New Product Development process forms the second part of the study. The primary data is collated from over 100 plus professionals while the qualitative data required for the second part is collated with the help of focused group interviews. The Likert scale with five points was deployed to record the opinions. The empirical analysis supports the theory that an effective collaboration is required between the different entities such as Sales, Marketing, R&D and going beyond the organizational boundaries Suppliers & Customers for the new product to be fruitful and successful in the market. The impact of using the IoT origin real time data on the effectiveness of the New Product Development is evaluated. In the current scenario for an organization to lead the market, it is essential that it has a descent product roadmap and an effective NPD. The current study reveals the importance of the NPD and contributes towards making it more effective with the IoT origin real time data.

Why Ecological Momentary Assessment Surveys Go Incomplete?: When It Happens and How It Impacts Data

2020 ◽  
Author(s):  
Yu-Hsiang Wu ◽  
Jingjing Xu ◽  
Elizabeth Stangl ◽  
Shareka Pentony ◽  
Dhruv Vyas ◽  
...  
Keyword(s):  
Real Time ◽  
Self Report ◽  
Data Logging ◽  
Time Data ◽  
Real Time Data ◽  
Report Data ◽  
Self Report Data ◽  
Ecological Momentary ◽  
The Impact ◽  
Momentary Assessment

Abstract Background Ecological momentary assessment (EMA) often requires respondents to complete surveys in the moment to report real-time experiences. Because EMA may seem disruptive or intrusive, respondents may not complete surveys as directed in certain circumstances. Purpose This article aims to determine the effect of environmental characteristics on the likelihood of instances where respondents do not complete EMA surveys (referred to as survey incompletion), and to estimate the impact of survey incompletion on EMA self-report data. Research Design An observational study. Study Sample Ten adults hearing aid (HA) users. Data Collection and Analysis Experienced, bilateral HA users were recruited and fit with study HAs. The study HAs were equipped with real-time data loggers, an algorithm that logged the data generated by HAs (e.g., overall sound level, environment classification, and feature status including microphone mode and amount of gain reduction). The study HAs were also connected via Bluetooth to a smartphone app, which collected the real-time data logging data as well as presented the participants with EMA surveys about their listening environments and experiences. The participants were sent out to wear the HAs and complete surveys for 1 week. Real-time data logging was triggered when participants completed surveys and when participants ignored or snoozed surveys. Data logging data were used to estimate the effect of environmental characteristics on the likelihood of survey incompletion, and to predict participants' responses to survey questions in the instances of survey incompletion. Results Across the 10 participants, 715 surveys were completed and survey incompletion occurred 228 times. Mixed effects logistic regression models indicated that survey incompletion was more likely to happen in the environments that were less quiet and contained more speech, noise, and machine sounds, and in the environments wherein directional microphones and noise reduction algorithms were enabled. The results of survey response prediction further indicated that the participants could have reported more challenging environments and more listening difficulty in the instances of survey incompletion. However, the difference in the distribution of survey responses between the observed responses and the combined observed and predicted responses was small. Conclusion The present study indicates that EMA survey incompletion occurs systematically. Although survey incompletion could bias EMA self-report data, the impact is likely to be small.

Customer behavior analysis using real-time data processing

2019 ◽  
Vol 31 (1) ◽  
pp. 265-290 ◽  
Author(s):  
Ganjar Alfian ◽  
Muhammad Fazal Ijaz ◽  
Muhammad Syafrudin ◽  
M. Alex Syaekhoni ◽  
Norma Latif Fitriyani ◽  
...  
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Association Rule ◽  
Customer Behavior ◽  
Time Data ◽  
Content Type ◽  
The Real ◽  
Real Time Data ◽  
Real Time Data Processing ◽  
Big Data Technology

PurposeThe purpose of this paper is to propose customer behavior analysis based on real-time data processing and association rule for digital signage-based online store (DSOS). The real-time data processing based on big data technology (such as NoSQL MongoDB and Apache Kafka) is utilized to handle the vast amount of customer behavior data.Design/methodology/approachIn order to extract customer behavior patterns, customers’ browsing history and transactional data from digital signage (DS) could be used as the input for decision making. First, the authors developed a DSOS and installed it in different locations, so that customers could have the experience of browsing and buying a product. Second, the real-time data processing system gathered customers’ browsing history and transaction data as it occurred. In addition, the authors utilized the association rule to extract useful information from customer behavior, so it may be used by the managers to efficiently enhance the service quality.FindingsFirst, as the number of customers and DS increases, the proposed system was capable of processing a gigantic amount of input data conveniently. Second, the data set showed that as the number of visit and shopping duration increases, the chance of products being purchased also increased. Third, by combining purchasing and browsing data from customers, the association rules from the frequent transaction pattern were achieved. Thus, the products will have a high possibility to be purchased if they are used as recommendations.Research limitations/implicationsThis research empirically supports the theory of association rule that frequent patterns, correlations or causal relationship found in various kinds of databases. The scope of the present study is limited to DSOS, although the findings can be interpreted and generalized in a global business scenario.Practical implicationsThe proposed system is expected to help management in taking decisions such as improving the layout of the DS and providing better product suggestions to the customer.Social implicationsThe proposed system may be utilized to promote green products to the customer, having a positive impact on sustainability.Originality/valueThe key novelty of the present study lies in system development based on big data technology to handle the enormous amounts of data as well as analyzing the customer behavior in real time in the DSOS. The real-time data processing based on big data technology (such as NoSQL MongoDB and Apache Kafka) is used to handle the vast amount of customer behavior data. In addition, the present study proposed association rule to extract useful information from customer behavior. These results can be used for promotion as well as relevant product recommendations to DSOS customers. Besides in today’s changing retail environment, analyzing the customer behavior in real time in DSOS helps to attract and retain customers more efficiently and effectively, and retailers can get a competitive advantage over their competitors.

scholarly journals Financing entrepreneurship in times of crisis: Exploring the impact of COVID-19 on the market for entrepreneurial finance in the United Kingdom

2020 ◽  
Vol 38 (5) ◽  
pp. 380-390 ◽  
Author(s):  
Ross Brown ◽  
Augusto Rocha ◽  
Marc Cowling
Keyword(s):  
United Kingdom ◽  
Real Time ◽  
Entrepreneurial Finance ◽  
Time Data ◽  
Face To Face ◽  
Policy Interventions ◽  
The United Kingdom ◽  
Real Time Data ◽  
Start Ups ◽  
The Impact

This commentary explores the manner in which the current COVID-19 crisis is affecting key sources of entrepreneurial finance in the United Kingdom. We posit that the unique relational nature of entrepreneurial finance may make it highly susceptible to such a shock owing to the need for face-to-face interaction between investors and entrepreneurs. The article explores this conjecture by scrutinising a real-time data source of equity investments. Our findings suggest that the volume of new equity transactions in the United Kingdom has declined markedly since the outbreak of the COVID-19 pandemic. It appears that seed finance is the main type of entrepreneurial finance most acutely affected by the crisis, which typically goes to the most nascent entrepreneurial start-ups facing the greatest obstacles obtaining finance. Policy makers can utilise these real-time data sources to help inform their strategic policy interventions to assist the firms most affected by crisis events.

Cassandra-based data repository design for food supply chain traceability

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sandeep Kumar Singh ◽  
Mamata Jenamani
Keyword(s):  
Supply Chain ◽  
Real Time ◽  
Radio Frequency Identification ◽  
Query Language ◽  
Data Repository ◽  
Time Data ◽  
Content Type ◽  
The Real ◽  
Traceability System ◽  
Real Time Data

Purpose The purpose of this paper is to design a supply chain database schema for Cassandra to store real-time data generated by Radio Frequency IDentification technology in a traceability system. Design/methodology/approach The real-time data generated in such traceability systems are of high frequency and volume, making it difficult to handle by traditional relational database technologies. To overcome this difficulty, a NoSQL database repository based on Casandra is proposed. The efficacy of the proposed schema is compared with two such databases, document-based MongoDB and column family-based Cassandra, which are suitable for storing traceability data. Findings The proposed Cassandra-based data repository outperforms the traditional Structured Query Language-based and MongoDB system from the literature in terms of concurrent reading, and works at par with respect to writing and updating of tracing queries. Originality/value The proposed schema is able to store the real-time data generated in a supply chain with low latency. To test the performance of the Cassandra-based data repository, a test-bed is designed in the lab and supply chain operations of Indian Public Distribution System are simulated to generate data.

scholarly journals Construction worker productivity in hot and humid weather conditions

2019 ◽  
Vol 27 (1) ◽  
pp. 83-108
Author(s):  
Ammar Saeed Mohammed Moohialdin ◽  
Fiona Lamari ◽  
Marc Miska ◽  
Bambang Trigunarsyah
Keyword(s):  
Real Time ◽  
Construction Industry ◽  
Productivity Loss ◽  
Weather Conditions ◽  
Research Field ◽  
Future Research ◽  
Time Data ◽  
Worker Productivity ◽  
Content Type ◽  
The Impact

Purpose The purpose of this paper shows the effect of hot and humid weather conditions (HHWCs) on workers that has resulted in considerable loss in the construction industry, especially during the hottest periods due to decline in worker productivity (WP). Until the last few decades, there is very limited research on construction WP in HHWCs. Nevertheless, these studies have sparked interests on seeking for the most appropriate methods to assess the impact of HHWCs on construction workers. Design/methodology/approach This paper begins by reviewing the current measuring methods on WP in HHWCs, follows by presenting the potential impact of HHWCs on WP. The paper highlights the methodological deficiencies, which consequently provides a platform for scholars and practitioners to direct future research to resolve the significant productivity loss due to global warming. This paper highlights the need to identify the limitations and advantages of the current methods to formulate a framework of new approaches to measure the WP in HHWCs. Findings Results show that the methods used in providing real-time response on the effects of HHWCs on WP in construction at project, task and crew levels are limited. An integration of nonintrusive real-time monitoring system and local weather measurement with real-time data synchronisation and analysis is required to produce suitable information to determine worker health- and safety-related decisions in HHWCs. Originality/value The comprehensive literature review makes an original contribution to WP measurements filed in HHWCs in the construction industry. Results of this review provide researchers and practitioners with an insight into challenges associated with the measurements methods and solving practical site measurements issues. The findings will also enable the researchers and practitioners to bridge the identified research gaps in this research field and enhance the ability to provide accurate measures in HHWCs. The proposed research framework may promote potential improvements in the productivity measurements methods, which support researchers and practitioners in developing new innovative methods in HHWCs with the integration of the most recent monitoring technologies.

scholarly journals Internet of Things based Customer Relationship Management – A Research Perspective

2018 ◽  
Vol 7 (2.7) ◽  
pp. 444 ◽  
Author(s):  
Samir Yerpude ◽  
Dr Tarun Kumar Singhal
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Mobile Internet ◽  
Customer Relationship ◽  
Time Data ◽  
Crm System ◽  
Real Time Data ◽  
The Impact

Objectives: To study the impact of Internet of things (IoT) on the Customer Relationship Management process and evaluate the benefits in terms of customer satisfaction and customer retention. Methods: An extensive literature review was conducting wherein the constructs of CRM and IoT are studied. Various preliminary information on IoT and CRM system along with the components of Digital enablers have been evaluated. References from research papers, journals, Internet sites, statistical data sites and books were used to collate the relevant content on the subject. The study of all the relevant scenarios where there is a possible impact of IoT origin real time data on CRM was undertaken. Findings: Customer demands are continuously evolving and it is very relevant for all the organizations to align and keep pace with the change. Organizations need to be customer centric and agile to the changing market scenarios. Evaluation of the trends in mobile internet vs desktop internet was also conducted to validate the findings. Application: The usage of real time data emerging out of the IoT landscape has become a reality with the data transmitted over the Internet and consumed by the CRM system. It improves the control on the customer relationship function helping the organizations to operate within healthy and sustained profit  

scholarly journals Sensors and tribological systems: applications for industry 4.0

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Saquib Rouf ◽  
Ankush Raina ◽  
Mir Irfan Ul Haq ◽  
Nida Naveed
Keyword(s):  
Real Time ◽  
Industry 4.0 ◽  
Developing Economies ◽  
Industrial Applications ◽  
Vital Role ◽  
Critical Parameters ◽  
Time Data ◽  
Content Type ◽  
Real Time Data

Purpose The involvement of wear, friction and lubrication in engineering systems and industrial applications makes it imperative to study the various aspects of tribology in relation with advanced technologies and concepts. The concept of Industry 4.0 and its implementation further faces a lot of barriers, particularly in developing economies. Real-time and reliable data is an important enabler for the implementation of the concept of Industry 4.0. For availability of reliable and real-time data about various tribological systems is crucial in applying the various concepts of Industry 4.0. This paper aims to attempt to highlight the role of sensors related to friction, wear and lubrication in implementing Industry 4.0 in various tribology-related industries and equipment. Design/methodology/approach A through literature review has been done to study the interrelationships between the availability of tribology-related data and implementation of Industry 4.0 are also discussed. Relevant and recent research papers from prominent databases have been included. A detailed overview about the various types of sensors used in generating tribological data is also presented. Some studies related to the application of machine learning and artificial intelligence (AI) are also included in the paper. A discussion on fault diagnosis and cyber physical systems in connection with tribology has also been included. Findings Industry 4.0 and tribology are interconnected through various means and the various pillars of Industry 4.0 such as big data, AI can effectively be implemented in various tribological systems. Data is an important parameter in the effective application of concepts of Industry 4.0 in the tribological environment. Sensors have a vital role to play in the implementation of Industry 4.0 in tribological systems. Determining the machine health, carrying out maintenance in off-shore and remote mechanical systems is possible by applying online-real-time data acquisition. Originality/value The paper tries to relate the pillars of Industry 4.0 with various aspects of tribology. The paper is a first of its kind wherein the interdisciplinary field of tribology has been linked with Industry 4.0. The paper also highlights the role of sensors in generating tribological data related to the critical parameters, such as wear rate, coefficient of friction, surface roughness which is critical in implementing the various pillars of Industry 4.0.

scholarly journals Metamorphosis of COVID-19 Pandemic

2020 ◽  
Author(s):  
Farhan Saif
Keyword(s):  
Real Time ◽  
Growth Model ◽  
Exponential Growth ◽  
Impact Point ◽  
Time Data ◽  
Slowing Down ◽  
Real Time Data ◽  
The Usa ◽  
Mathematical Techniques ◽  
The Impact

We show phase-wise growth of COVID 19 pandemic and explain it by comparing real time data with Discrete Generalized Growth model and Discrete Generalized Richard Model. The comparison of COVID 19 is made for China, Italy, Japan and the USA. The mathematical techniques makes it possible to calculate the rate of exponential growth of active cases, estimates the size of the outbreak, and measures the deviation from the exponential growth indicating slowing down effect. The phase-wise pandemic evolution following the real time data of active cases defines the impact-point when the preventive steps, taken to eradicate the pandemic, becomes effective. The study is important to devise the measures to handle emerging threat of similar COVID-19 outbreaks in other countries, especially in the absence of a medicine.

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