Given the emerging nature of integrating Blockchain Technology (“BCT”) into several business fields concerning the interaction between companies and their customers, this study aims to investigate the applications of BCT in marketing through an accurate procedure of locating, selecting and analyzing existing companies using BCT in marketing. A sample that consists of 800 companies was identified using web-scraping methods. The data set was collected from ICO websites as well as from an existing, older landscape of applications. The data set was then intensively analyzed in order to be categorized into five fields of marketing technology. Advertising and ecommerce outgrew the other fields of social & relationship, content & experience and data in absolute numbers, revealing the focus of practitioners in the past as well as gaps for the future. The authors provided future directions for researchers on and development of tools to systematically generate knowledge and improve the application of BCT and the work of practitioners in marketing.

Applications of Blockchain Technology in marketing systematic review of marketing technology companies

Vehicle crash rates caused by distracted driving have been rising in recent years. Hence, safety while driving on roads is today a crucial concern across the world. Some of the reasons due to which drivers may lose attention include the use of mobile phones, speaking with passengers, and reaching behind to grab something while driving. There are various types of distractions, out of which we focus on manual ones based on the posture of the driver. This work presents a review on open problems and challenges concerning the vision‐based distraction recognition of drivers. Types of experimental environments are described; and image modalities, public and custom datasets and body parts inspected during distraction recognition are explored. Besides, a comparative review of different works on vision‐based recognition of driver's manual distractions and limitations for each dataset is presented. Main approaches of vision‐based manual distractive driving recognition can be categorized into conventional and deep learning methods. These approaches are compared and classified based on whether using temporal information or not. Finally, we give some suggestions for improvement, and look forward to future development directions of the vision‐based recognition of driver's manual distractions.

Computer vision-based recognition of driver distraction: A review

Distracted or drowsy driving is unsafe driving behavior responsible for thousands of crashes every year. Studying driver behavior has challenges associated with observing drivers in their natural environment. The naturalistic driving study (NDS) has become the most sought-after approach, since it eliminates the bias of a controlled setup, allowing researchers to understand drivers’ behavior in real-world scenarios. Video recordings collected in NDS research are incredibly insightful in identifying driver errors. Computer vision techniques have been used to autonomously analyze video data and classify drivers’ behavior. While computer vision scientists focus on image analytics, NDS researchers are interested in the factors impacting driver behavior. This survey paper makes a concerted effort to serve both communities by comprehensively reviewing studies, describing their data collection, computer vision techniques implemented, and performance in classifying driver behavior. The scope is limited to studies employing at least one camera observing the driver inside a vehicle. Based on their objective, papers have been classified as detecting low-level (e.g. head orientation) or high-level (e.g. distraction detection) driver information. Papers have been further classified based on the datasets they employ. In addition to twelve public datasets, many private datasets have also been identified, and their data collection design is discussed to highlight any impact on model performance. Across each task, algorithms employed and their performance are discussed to establish a baseline. A comparison of different frameworks for NDS video data analytics throws light on the existing gaps in the state-of-the-art that can be addressed by future computer vision research. 

A Survey on Driver Behavior Analysis From In-Vehicle Cameras

Abstract Platform ecosystems have captured a variety of markets, enabling coordination, transactions, and value co-creation between independent actors. A focal platform constitutes the central nexus of e-commerce ecosystems and fosters the interaction among ecosystem participants through their boundary resources. Standardizing these interfaces simplifies ecosystem entry for developers and increases the number of participants propelling the network effects, and thus the overall value of the ecosystem. Currently, there is a lack of prescriptive design knowledge guiding platform owners in designing successful e-commerce ecosystems. Addressing this issue, we followed a dual approach, reporting on a systematic literature review in which we identified design requirements and complemented these with a multiple-case study on selected e-commerce ecosystems. Aggregating the requirements resulted in six meta-requirements and 19 design principles that foster the standardization of focal e-commerce platforms. Our design principles simplify the development of complements and enable multi-homing for developers due to possible standardization across ecosystems. 

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Developing design principles to standardize e-commerce ecosystems

A loyalty program is a type of incentive to reward customers’ perceived value and enhance their purchasing behavior. The key to the success of a loyalty program is to allow customers to more actively participate in the program. One possible solution is to allow customers to sell out idle loyalty points and buy in the points that they need. On the basis of a call auction, this study designs a peer-to-peer exchange mechanism for customers to realize the above trade. In addition, a blockchain-based system is developed to support the issuance, redemption, and exchange of loyalty points. In this study, Hyperledger Fabric is adopted as the underlying blockchain technology because it has some features that are beneficial to a cross-organizational coalition loyalty program. This study also proposes a feasible multi-host deployment scheme for the Hyperledger Fabric blockchain network that is suitable for our application scenario. Finally, some implementation results are given to demonstrate the system process from the perspective of the application layer. The mechanism proposed in this study is helpful to improve the likelihood of successfully exchanging points, thus accelerating the circulation and use of loyalty points.

/pdf/a-loyalty-system-incorporated-with-blockchain-and-call-1x2st40g.pdf

A Loyalty System Incorporated with Blockchain and Call Auction

Loyalty Programs are reward programs offered by a company to customers who frequently make purchases. However, there are major issues with traditional loyalty programs like low redemption rates, expiring of points, customer acquisition costs, and the nuisance of keeping track of multiple loyalty programs. Blockchain provides a solution to a number of these issues. This paper explores the issues of the current loyalty programs and how Blockchain technology can be used to tackle the underlying issues of traditional loyalty programs. Finally, we propose our own Blockchain-based universal loyalty platform, which aims at making loyalty programs more beneficial for customers, thereby increasing customer retention rates as well as more convenient for companies to have their own loyalty program. This system aims to integrate individual loyalty programs into a unified platform that will allow transfer and exchange of loyalty points between users and promote co-branding among various companies located worldwide.

Blockchain-based Universal Loyalty Platform

There has always been a safety issue while driving and this issue is of utmost importance with the increasing amount of accidents due to car crashing or distracted driving. As per the CDC motor vehicle safety division, one in five car accidents occurs due to distracted driving. Unfortunately, this led to over 425,000 people getting injured and around 3,000 people being killed due to distracted driving. This paper aims to monitor the behaviour of the driver and look for any kind of distraction. The model classifies the distractions into ten classes like talking on phone using left/right hand, interacting with the passenger, using the dashboard, drinking and reaching behind. Different architectures of Convolutional Neural Networks (CNN) like AlexNet and Inception-v3 are used which give promising results. The input to the model is the set of dashboard images with the help of which the behaviour of the driver is supervised for any distractions.

Automated System for Detecting Distracted Driver

The issue of counterfeit drugs is prevalent worldwide. Developing countries are particularly affected by this problem. The financial loss incurred due to counterfeit drugs is in billions. The main reason for this distribution of counterfeit drugs is the current fragmented supply chain system in pharmaceutical industry. Drugs change hands from several entities of the supply chain like manufacturer, stockist, retailer, etc. It becomes difficult to keep track of the drug in supply chain at any given point of time. In this paper, we review all the technologies that can be used and all the literature available and work done on this issue. In our proposed solution, we discuss how using a distributed ledger we can add nodes to the blockchain network and monitor all the transactions on the blockchain, thus introducing traceability, security, and transparency.

System to Fight Counterfeit Drugs

Serverless computing is one of the most recent additions to the long list of services provided by cloud computing. Improving upon the attributes of scalability, affordability, and granularity present in earlier services offered by cloud computing, serverless computing is the next major development. Function as a Service has further modularized applications and enabled the individual execution of functions using triggers which has led to reduced costs, improved scaling of applications and almost no configuration expenses. Several cloud computing providers have developed their serverless computing services, each having its advantages and disadvantages. This paper aims to describe the need for serverless computing, its working, its economics, applications, its pros and cons in the current state, and the developments over existing technologies. Various serverless computing providers and their services have also been compared and studied.

Serverless Computing and the Emergence of Function-as-a-Service

Today, E-Commerce has become the largest revenue generation industry, letting seller sell everything from a pen to plane to the customers across the globe. Over an E-commerce platform where user and vendor merely interact with each other, the trust is undeniably the most important factor for users to perform transactions online. But at the same time it can't be assessed directly using some pre-defined metrics. It has to be perceived from various indicators. Trustworthiness is a measure of any system as a whole provides users, service providers and communities, an assurance and advice to select a particular ecommerce platform that helps in minimizing a risk of fraud. In this paper, we are soliciting a feedback evaluation system that will act as a decision support system, which can assist users to measure the trustworthiness and usefulness of the user opinion about a product or a service by evaluating the trust degree.

Harshal Dalvi

Papers

Blockchain-based Universal Loyalty Platform

Automated System for Detecting Distracted Driver

System to Fight Counterfeit Drugs

Serverless Computing and the Emergence of Function-as-a-Service

Trustworthiness evaluation system in E-Commerce context