Mobile Consumer Scanning Technology: A Replacement for Interorganizational Information Systems for Demand Information Learning in Supply Chains?

Recently, firms in supply chains have begun to deploy popular mobile apps (e.g., WeChat) into their supply chain practices to improve demand visibility. These efforts rely on consumers to scan the products they purchase using these apps, which we refer to as consumer scanning technology (CST). CST can be an alternative to the conventional interorganizational information technology (IOIT) that relies on collaboration between supply chain firms. This paper develops a theoretical model to examine the value of CST to learn supply chain (demand) information and the impact of CST on IOIT. Using an extensive simulation analysis based on real-world data from a manufacturer that has implemented a CST program, we show that the value of CST to a manufacturer can be substantial and provide insights into how market conditions affect the value.

Mobile data collection apps

The expansion of the mobile consumer market in the last decade has resulted in the widespread availability of affordable, multifunctional tablets, and smartphones with a range of uses. Whether for scientific research or conservation practice, these devices provide a means of digital data collection that is an increasingly time- and cost-effective alternative to traditional methods. This chapter discusses recent advances in mobile data collection, especially with cloud storage, including the advantages and limitations of this emerging approach. It will also review current hardware and software options for conservation data collection, focusing on devices and apps with high customisability, and provide an overview of how these systems may be applied in conservation science. As a case study, it will examine the transition from paper to digital data collection at a primate conservation project at the Issa Valley, Tanzania. And finally, it will identify gaps and precautions in current applications of mobile data collection and suggest what lies ahead for digital data collection in conservation.

The Impact Analysis of Features and Perceived Quality on Consumer Satisfaction of Samsung Mobile Phones in Makassar City

This study aims to determine whether Features and Perceived quality affect Samsung Mobile Consumer satisfaction in Makassar City. The population in this study are consumers who use Samsung mobile phones in Makassar City. Based on the research results, the regression equation is obtained as follows Y = 5.308 +0.389 X1 + 0.321 X2. Based on statistical data analysis, each indicator in this study is valid and the variables are reliable. In testing the classical assumptions, the regression model is multicolonierity free, heteroscedasticity does not occur, and is normally distributed. The results of hypothesis testing, namely the T test results prove that all independent variables, namely Features and Perceived Quality, have a positive effect on Consumer satisfaction on Samsung Mobile Phones in Makassar City and the F test shows that features and perceived quality simultaneously have a significant effect on Consumer satisfaction, with a calculated F value of 13.976 with a significance of 0.000 <0.05. The coefficient of determination R square is 0.536. This means that 53.6% of consumer satisfaction is influenced by the features and perceived quality variables, the remaining 45.3% is influenced by other variables. The coefficient of determination for R square is 0.536. This means that 53.6% of consumer satisfaction is influenced by the features and perceived quality variables, the remaining 45.3% is influenced by other variables. The coefficient of determination for R square is 0.536. This means that 53.6% of consumer satisfaction is influenced by the features variable and perceived quality, the remaining 45.3% is influenced by other variables.

A Decoding-Complexity and Rate-Controlled Video-Coding Algorithm for HEVC

Video playback on mobile consumer electronic (CE) devices is plagued by fluctuations in the network bandwidth and by limitations in processing and energy availability at the individual devices. Seen as a potential solution, the state-of-the-art adaptive streaming mechanisms address the first aspect, yet the efficient control of the decoding-complexity and the energy use when decoding the video remain unaddressed. The quality of experience (QoE) of the end-users’ experiences, however, depends on the capability to adapt the bit streams to both these constraints (i.e., network bandwidth and device’s energy availability). As a solution, this paper proposes an encoding framework that is capable of generating video bit streams with arbitrary bit rates and decoding-complexity levels using a decoding-complexity–rate–distortion model. The proposed algorithm allocates rate and decoding-complexity levels across frames and coding tree units (CTUs) and adaptively derives the CTU-level coding parameters to achieve their imposed targets with minimal distortion. The experimental results reveal that the proposed algorithm can achieve the target bit rate and the decoding-complexity with 0.4% and 1.78% average errors, respectively, for multiple bit rate and decoding-complexity levels. The proposed algorithm also demonstrates a stable frame-wise rate and decoding-complexity control capability when achieving a decoding-complexity reduction of 10.11 (%/dB). The resultant decoding-complexity reduction translates into an overall energy-consumption reduction of up to 10.52 (%/dB) for a 1 dB peak signal-to-noise ratio (PSNR) quality loss compared to the HM 16.0 encoded bit streams.

Clustering consumers' shopping journeys: eye tracking fashion m-retail

PurposeDespite the rapid adoption of smartphones among digital fashion consumers, their attitude to retailers' mobile apps and websites is one of increasing dissatisfaction. This suggests that understanding how mobile consumers use smartphones for fashion shopping is important in developing digital shopping platforms that fulfil consumer' expectations.Design/methodology/approachFor this research, mobile eye-tracking technology was employed in order to develop unique shopping journeys for 30 consumers, using fashion retailers' websites on smartphones, documenting their differences and similarities in browsing and purchasing behaviour.FindingsBased on scan path visualisations and observed shopping experiences, three prominent mobile shopping journeys and shopper types were identified: “directed by retailer's website”, “efficient self-selected journey” and “challenging shopper”. These prominent behaviour patterns were used to characterise mixed cluster behaviours; three distinct mixed clusters were identified, namely, “extended self-selected journey”, “challenging shoppers directed by retailer's website” and “focused challenging shopper”.Research limitations/implicationsThis research argues that mobile consumers can be segmented based on their activities and behaviours on the mobile website. Knowing the prominent shopping behaviour types any other complex behaviour patterns can be identified, analysed and described.Practical implicationsThe findings of this research can be used in developing personalised shopping experiences on smartphones by feeding these shopper types into retailers' digital marketing strategy and artificial intelligence (AI) systems.Originality/valueThis paper contributes to consumer behaviour literature by proposing a novel mobile consumer segmentation approach based on detailed shopping journey analysis using mobile eye-tracking technology.

Digital Campus

Mobile is everywhere, changing the way we work, play, socialize, and learn. Students nowadays are immersed in a digital culture driven by mobile consumer experiences across a range of devices, from wearable to phones, tablets and virtual-reality platforms. Digital Campus is a website application that is in University Tun Hussien Onn Pagoh Campus. The purpose of this website is to search all locations located in Pagoh Campus such as laboratory, admin office, dean office, cafe and classroom. Digital campus website is embedded with Open Street Map. It is open databased licensing and it is a collaborative mapping. This website is developed using Joomla 3.8.13 with PHP version, 5.6.25. There are 204 locations were plotted using this link https://digitalcampus.uthm.edu.my/index.php. Digital campus based on Open Street Map can helps students, admin staff, lectures and visitors to find location in very easy way by using digital platform. Thus, this application facilities can optimization of administration work and promote university management to higher performances.