Relevant bibliographies by topics / Consumer behavior – data processing

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Consumer behavior – data processing'

Author: Grafiati
Published: 26 July 2024

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Journal articles on the topic "Consumer behavior – data processing"

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Baozhu Feng, Han Nie,. "Optimization of Online Advertising Decision Combination Based on the Attribution of Consumer Behavior Big Data." Journal of Electrical Systems 20, no. 2 (April 4, 2024): 1981–87. http://dx.doi.org/10.52783/jes.1645.

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In the field of internet advertising, associated products based on various big data algorithms are widely presented on consumers’ browsing pages in order to increase their purchasing interest. With the help of computer big data processing, online advertisers can better grasp the shopping needs of consumers. This study proposes a chance constrained programming optimization model (CC-PR) for budget allocation of advertisers in a dynamic and complex consumer behavior context, based on the premise of analyzing consumers’ shopping journey from the perspective of the attribution of consumer behavior effects. It models different online behaviors generated by consumers’ interest transfer at different shopping stages. The characteristic of this method is to simulate changes in consumer psychology, overcome the difficulty of predicting multi-stage behavior of consumers, and extract a model of dynamic generalizable laws of consumer psychology. It dynamically adjusts advertising strategies based on changes in consumer tendencies at different stages. In order to verify the effectiveness of the proposed strategy, this paper introduces batch data from large shopping platforms and compares our method with the baseline method through experiments. The results show that the CC-PR method proposed in this paper has superior performance. A large amount of consumer behavior data can help advertisers make more accurate advertising decisions, and the method proposed in this article is also a good example of the application of computer big data processing in the field of online advertising.
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Liu, Shengchen. "Analysis of implementation for big data techniques in consumer behavior." Applied and Computational Engineering 53, no. 1 (March 28, 2024): 166–72. http://dx.doi.org/10.54254/2755-2721/53/20241342.

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Consumer behavior can refer to both the purchasing behavior of consumers towards goods and the decision-making behavior of consumers when choosing goods, the research in this field involves psychology, marketing, and other disciplines. As a popular data mining and processing technology, big data technology has powerful information analysis capabilities. Therefore, with this in mind, applying big data technology to consumer behavior research can greatly improve the efficiency. This study focuses on the implementation for big data techniques in consumer behavior, including application scenarios, application methods, as well as application efficiency. The AIDMA, AISAS, and SOR models are suitable for using big data analysis technology. Big data technology helps enterprises influence external factors in consumer behavior factors, thereby influencing consumer decision-making behavior through internal perception factors. Overall, these results aim to collect and summarize relevant knowledge about consumer behavior and big data for future scholars to investigate and explore the implementation schemes.
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Yang, Wenjun, and Jia Guo. "Consumers’ Purchase Behavior Preference in E-Commerce Platform Based on Data Mining Algorithm." International Journal of Circuits, Systems and Signal Processing 16 (January 14, 2022): 603–9. http://dx.doi.org/10.46300/9106.2022.16.75.

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E-commerce platform can recommend products to users by analyzing consumers’ purchase behavior preference. In the clustering process, the existing methods of purchasing behavior preference analysis are easy to fall into the local optimal problem, which makes the results of preference analysis inaccurate. Therefore, this paper proposes a method of consumer purchasing behavior preference analysis on e-commerce platform based on data mining algorithm. Create e-commerce platform user portrait template with consumer data records, select attribute variables and set value range. This paper uses data mining algorithm to extract the purchase behavior characteristics of user portrait template, takes the characteristics as the clustering analysis object, designs the clustering algorithm of consumer purchase behavior, and grasps the common points of group behavior. On this basis, the model of consumer purchase behavior preference is established to predict and evaluate the behavior preference. The experimental results show that the accuracy rate of this method is 91.74%, the recall rate is 88.67%, and the F1 value is 90.17%, which are higher than the existing methods, and can provide consumers with more satisfactory product information push.
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Géci, Andrej, Ľudmila Nagyová, Stanislav Mokrý, and Jana Rybanská. "Investigation of consumer behaviour at selected market commodity." Potravinarstvo Slovak Journal of Food Sciences 13, no. 1 (December 28, 2019): 925–32. http://dx.doi.org/10.5219/1230.

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Consumer behavior is an unexplored area of life for all buyers and sellers alike. Knowledge of consumer behavior brings better market orientation and more consistent establishment of individual products in consumers' shopping baskets. In examining this behavior, the authors of the paper focus on questions such as why, how, where, when, and how much consumers are willing and able to buy at a particular market price. The behavior in question is influenced by several factors (cultural, social, psychological and personality) that influence the final consumer decisions. The main objective of the present document is to evaluate consumer behavior, their purchasing preferences and also to make decisions when buying a particular food product – tea. The primary data were obtained through a questionnaire survey carried out on a sample of 640 respondents. The questionnaire was divided into two basic parts - the demographic part and the part dealing with consumer behavior at the selected market commodity. In the questionnaire processing the respondents were filtered based on their answers. The research has shown that more than 78% of respondents consume tea while women consume it more (50%). Flavor (56.3%) proved to be the most important factor in the selection of a particular tea. Almost half of the respondents reported consuming cut tea (46.5%). In the present document, assumptions were formulated which serve for a deeper analysis of the issue. The relevance of the formulated assumptions was verified by the XLSTAT statistical software. Data were evaluated by qualitative statistics – Chi-square test of good compliance, Fisher's exact test, Mann Whitney test and Friedman test.
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López-Gómez, Rafael, Laura Panizo, and María-del-Mar Gallardo. "Flextory: Flexible Software Factory of IoT Data Consumers." Sensors 24, no. 8 (April 16, 2024): 2550. http://dx.doi.org/10.3390/s24082550.

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The success of the Internet of Things (IoT) has driven the development, among others, of many different software architectures for producing, processing, and analyzing heterogeneous data. In many cases, IoT applications share common features, such as the use of a platform or middleware, also known as message broker, that collects and manages data traffic between endpoints. However, in general, data processing is very dependent on the case study (sensors that send temperature data, drones that send images, etc.). Thus, the applications responsible for receiving and processing data, which we call consumers, have to be built ad hoc, since some of their elements have to be specially configured to solve specific needs of the case study. This paper presents Flextory, a software factory tool to make it easier for IoT developers to automatically construct configurable consumer applications, which we call FLEX-consumers. Flextory guides developers through the process of generating Java consumers by selecting some desired features such as, for instance, the particular communication protocol to be used. This way, the developer only has to concentrate on designing the algorithm to process the data. In short, the use of Flextory will result in consumer applications with configurable behavior, namely FLEX-consumers, that can connect to a messaging server (for example RabbitMQ) and process the received messages.
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Ghosh, Tathagata, and Venu Gopal Rao. "Consumer Confusion in Mobile Application Buying." International Journal of E-Business Research 10, no. 4 (October 2014): 52–70. http://dx.doi.org/10.4018/ijebr.2014100104.

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This study aims to examine effects of consumer confusion proneness on two consumer behavior variables, word of mouth and buying decision, in a confusion prone product market like smartphone and mobile applications (app). Additionally, the study examines the moderating influence of a personality trait variable pertinent to information processing – need for cognition, on the afore-mentioned relationships. Data were collected via a survey from 254 post-graduate students and analyzed with the help of structural equation modeling with a partial least square (PLS) approach using SmartPLS 2.0. Research findings indicate that the dimensions of consumer confusion proneness behave differently in terms of their linkages with consumer behavior. Further, as expected, consumers' need for cognition also play a major moderating role which helps to fathom the concept of consume confusion in the context of mobile and smartphone applications. Academic and managerial implications as well as scope for future research are also discussed.
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Simonović, Zoran, and Nikola Ćurčić. "Consumer behavior related to buying wines on the retail market in the City of Niš." Strategic Management 26, no. 4 (2021): 65–71. http://dx.doi.org/10.5937/straman2104065s.

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The subject matter of the paper is research in consumer behavior in connection with the purchase of wines of both domestic and foreign brands on the market of the City of Niš. The paper is structured so as to perceive the issue from several aspects. In the first place, our wish was to determine whether the respondents are regular wine consumers, and which sorts of wine they consume, and how many of them are not wine regular consumers. An effort was made to determine consumer habits via survey research. The examination method that implied personal examination using a questionnaire was applied. The suitable sample included 230 respondents from the territory of the City of Niš. The research was conducted in November 2019. While processing the data, it was noticed that a larger number of the respondents were regular wine consumers, namely consuming specific brands. The respondents mainly preferred domestic producers' wines. The results obtained can be observed at the level of the strategic marketing of the offering companies. The research study provides an insight in connection with the different aspects of the consumer behavior of those who consume wine, both at the general level and in connection with the choice of brands.
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Schulz, Lee L., Ted C. Schroeder, and Katharine L. White. "Value of Beef Steak Branding: Hedonic Analysis of Retail Scanner Data." Agricultural and Resource Economics Review 41, no. 2 (August 2012): 260–73. http://dx.doi.org/10.1017/s1068280500003397.

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Consumers rely on experience and credence attributes when purchasing beef from retailers. It is essential for all beef industry sectors to recognize the complexity of consumer buying behavior. A hedonic model is estimated to determine if there are incentives to brand beef steaks, the types of brands that entertain price premiums, and the level of existing premiums. Most branded steaks garnered premiums along with organic claims, religious processing claims, and premium cuts. Factors influencing brand value were new brands targeting emerging consumer trends, brands with regional prominence, and brands positioned as special label, program/breed specific production, and store labels.
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Singh, Richa. "Asses the Behaviour of Consumers for Enhancing Claims Processing and Fraud Detection in Insurance Through Blockchain Technology." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (April 28, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem32349.

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The rapid advancement of blockchain technology presents a transformative opportunity for the insurance industry, particularly in claims processing and fraud detection. This paper explores the impact of blockchain technology on consumer behavior and its potential to enhance the efficiency and security of insurance operations. We begin by providing a comprehensive overview of blockchain technology, outlining its decentralized nature, immutability, and transparency. We then delve into the current challenges faced by the insurance industry, including prolonged claims processes and prevalent fraudulent activities. Through a mixed-methods approach, incorporating both quantitative surveys and qualitative interviews, we analyze how consumers perceive and interact with blockchain-based insurance solutions. The findings suggest that while there is general awareness about blockchain, its understanding remains limited among consumers. However, our study indicates a positive correlation between blockchain adoption and increased trust in insurance processes, driven by the technology's inherent characteristics of security and data integrity. Furthermore, we discuss several blockchain implementations, such as smart contracts that automate claims adjudication, and decentralized applications that ensure transparency and reduce opportunities for fraud. Finally, we propose a framework for insurance companies to effectively integrate blockchain technology, emphasizing consumer education, stakeholder engagement, and regulatory compliance. This study not only highlights the transformative potential of blockchain in reshaping consumer behavior and operational processes in insurance but also provides strategic insights for stakeholders aiming to harness these innovations for improved service delivery and fraud mitigation. The study titled "Assess the behavior of consumers for enhancing claims processing and fraud detection in insurance through blockchain technology" aims to explore how blockchain can revolutionize the insurance sector by influencing consumer behavior, improving claims processing, and enhancing fraud detection mechanisms. The focus of this research involves several key areas. This study explores the behavior of consumers in the context of enhancing claims processing and fraud detection in the insurance sector through the adoption of blockchain technology. Blockchain, known for its decentralized and transparent nature, holds promise for revolutionizing traditional insurance processes by improving transparency, efficiency, and security. However, successful integration relies heavily on consumer acceptance and adoption. This research aims to investigate consumer attitudes, perceptions, and intentions towards blockchain-based insurance solutions. Drawing upon established theories of technology acceptance and consumer behavior, the study examines factors influencing consumer trust, privacy concerns, and perceived benefits of blockchain in insurance. Additionally, demographic variations in consumer behavior and the impact of cultural and regulatory factors are explored. Through a comprehensive literature review and empirical research methods such as surveys and interviews, this study seeks to provide insights into how insurers can effectively engage consumers, address concerns, and leverage blockchain technology to enhance claims processing efficiency and fraud detection capabilities. The findings aim to inform strategic decision-making for insurance companies, regulators, and policymakers, facilitating the successful adoption and implementation of blockchain solutions in the insurance industry.
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N. H. Rachmani, A. Apriantini, and L. Cyrilla E. N. S. D. "Analisis Perilaku Konsumen Usia Muda di Kota Bogor dalam Mengonsumsi Susu dan Produk Olahannya di Masa Pandemi Covid 19." Jurnal Ilmu Produksi dan Teknologi Hasil Peternakan 10, no. 1 (January 31, 2022): 15–20. http://dx.doi.org/10.29244/jipthp.10.1.15-20.

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During the COVID-19 pandemic, people are started to consume foods that are highly nutritious to increased their immunity, one of them is by consuming milk and its processed products like kefir, yogurt. Milk is one of the foods favored by the community because it is rich in health benefits. The demand for this product is also increased every year. Consumer behavior was always changing along with consumer needs that follow the conditions and developments of the times. Consumers will choose products with good quality and ful fill what their needs, as well as dairy products. This study aims to analyze consumer decisions in consuming milk and its processed products in the Bogor city area during the covid 19 pandemic. The research was carried out with the stages of determining respondents, identify factors that influence consumer behavior, collecting and analyzing data. Respondents are people who live in Bogor City with an age range of 20 to 30 years purposively as many as 108 respondents. Data processing and analysis was carried out using the Spearman rank correlation test to determine the relationship between knowledge and situational variables on the decision to consume milk and its processed products. The results showed that the level of knowledge and situational had a significant and direct relationship with consumer decisions (P<0.01). This shows that the higher level of consumer knowledge of products with supportive conditions, the consumer’s decision to consume milk and its processed products is also getting stronger.
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Dissertations / Theses on the topic "Consumer behavior – data processing"

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Yanamandram, Venkata Krishna Kumar. "Retention of dissatisfied business-to-business services customers." Thesis, The University of Sydney, 2008. https://hdl.handle.net/2123/28954.

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The strategic importance of customer retention and the costs associated with customer switching behaviour have resulted in researchers devoting considerable attention to investigating the switching and/or staying behaviour of customers. However, relatively little attention has been devoted to investigating (1) the important deterrents to discontinuing the relationship in a single model in the business-to-business (B-to-B) service sector, and (2) the mediating factors under the condition of dissatisfaction in the B-to-B services sector. The fastest growth in services marketing today is in business markets, making this an important area of study with significance for marketers, particularly in terms of the development of customer retention. A two-stage research design, with qualitative and quantitative phases, was employed in the current research using a single key informant approach. The qualitative study utilised an indepth interview technique in which key informants representing 21 businesses provided interviews. The quantitative study employed a cross-sectional email URL embedded Web survey of key informants, which resulted in a final sample size of 376 responses. The simultaneous nature of the multiple relationships between key constructs was analysed using structural equation modelling. The research demonstrated that distributive justice, procedural justice, interactional justice, satisfaction with complaint—handling, benefit-loss costs, economic-loss costs, learning costs, sunk costs, interpersonal relationships, dependence and calculative commitment are the direct and indirect factors that influence the likelihood of dissatisfied customers remaining in a relationship. A key finding is the mediating role of both dependence and calculative commitment on the relationship between specific firmlevel investments and repurchase intentions. This thesis makes four contributions. Firstly, it uses a business services context, which is an under—researched area for this research problem. Secondly, it connects determinant factors to an outcome variable in explaining the tendency among B—to-B services customers, who have complained and considered switching, to remain involved in a relationship that is dissatisfying overall. Thirdly, it examines the effects of antecedents on calculative commitment that have previously not been examined, thus making a conceptual and empirical contribution. Finally, it offers managerial implications for the offending service firms as well as for those service firms that are attempting to attract dissatisfied prospective switchers of the offending firm.
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Mestre, María del Rosario. "Bayesian predictive models of user intention." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708641.

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Winn, David. "An analysis of neural networks and time series techniques for demand forecasting." Thesis, Rhodes University, 2007. http://hdl.handle.net/10962/d1004362.

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This research examines the plausibility of developing demand forecasting techniques which are consistently and accurately able to predict demand. Time Series Techniques and Artificial Neural Networks are both investigated. Deodorant sales in South Africa are specifically studied in this thesis. Marketing techniques which are used to influence consumer buyer behaviour are considered, and these factors are integrated into the forecasting models wherever possible. The results of this research suggest that Artificial Neural Networks can be developed which consistently outperform industry forecasting targets as well as Time Series forecasts, suggesting that producers could reduce costs by adopting this more effective method.
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Kadambi, Rupasri. "Analysis of data mining techniques for customer segmentation and predictive modeling a case study /." Diss., Online access via UMI:, 2005.

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Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Dept. of Systems Science and Industrial Engineering, 2005.
Includes bibliographical references.
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Yan, Ping. "SPATIAL-TEMPORAL DATA ANALYTICS AND CONSUMER SHOPPING BEHAVIOR MODELING." Diss., The University of Arizona, 2010. http://hdl.handle.net/10150/195232.

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RFID technologies are being recently adopted in the retail space tracking consumer in-store movements. The RFID-collected data are location sensitive and constantly updated as a consumer moves inside a store. By capturing the entire shopping process including the movement path rather than analyzing merely the shopping basket at check-out, the RFID-collected data provide unique and exciting opportunities to study consumer purchase behavior and thus lead to actionable marketing applications.This dissertation research focuses on (a) advancing the representation and management of the RFID-collected shopping path data; (b) analyzing, modeling and predicting customer shopping activities with a spatial pattern discovery approach and a dynamic probabilistic modeling based methodology to enable advanced spatial business intelligence. The spatial pattern discovery approach identifies similar consumers based on a similarity metric between consumer shopping paths. The direct applications of this approach include a novel consumer segmentation methodology and an in-store real-time product recommendation algorithm. A hierarchical decision-theoretic model based on dynamic Bayesian networks (DBN) is developed to model consumer in-store shopping activities. This model can be used to predict a shopper's purchase goal in real time, infer her shopping actions, and estimate the exact product she is viewing at a time. We develop an approximate inference algorithm based on particle filters and a learning procedure based on the Expectation-Maximization (EM) algorithm to perform filtering and prediction for the network model. The developed models are tested on a real RFID-collected shopping trip dataset with promising results in terms of prediction accuracies of consumer purchase interests.This dissertation contributes to the marketing and information systems literature in several areas. First, it provides empirical insights about the correlation between spatial movement patterns and consumer purchase interests. Such correlation is demonstrated with in-store shopping data, but can be generalized to other marketing contexts such as store visit decisions by consumers and location and category management decisions by a retailer. Second, our study shows the possibility of utilizing consumer in-store movement to predict consumer purchase. The predictive models we developed have the potential to become the base of an intelligent shopping environment where store managers customize marketing efforts to provide location-aware recommendations to consumers as they travel through the store.
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Sun, J. (Jia). "Exploring the factors affecting online consumer behavior in China:case: Taobao." Master's thesis, University of Oulu, 2018. http://urn.fi/URN:NBN:fi:oulu-201811093003.

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With the development of electronic commerce, the number of online consumers increase a lot. Consumers are willing to purchase products online since it has great advantages such as saving time, creating convenience and offering more choices. As the biggest and most innovative retail e-commerce market in the world, China possesses 17% online retailing sales from total retail sales in 2017. As the largest e-commerce site in China, Taobao takes over 80% of the e-commerce market share. The purpose of this thesis is to do in-depth exploration of the factors that affecting online consumer behavior in China. Taobao is selected as the case of online shopping platform. A systematic literature review was conducted to investigate the existing articles related to the factors that affecting online consumer behavior when people do shopping on Taobao. Then a qualitative study was designed to examine the factors in depth. An in-depth interview was organized among 10 Taobao users in order to extent the validity of the research. Qualitative content analysis was used as data analysis method and Nvivo was selected as the analyzing tool. Five categories were classified in both SLR and in-depth interview. The commodity factor was examined as one of the most influential factors. It consists of product quality, price, styles and brands. Factors related sellers including seller’s service quality, seller’s website quality, seller’s commercial credits and online reputation are considered as key factors as well. Social commerce activities such as social media sharing, online reviews, online rating and online forum have significant impacts on online consumer behavior. In addition, web quality and functionality of Taobao and some consumer personal factors including consumer’s mood and perceived risks also play an important role. Consumers’ own requirements, the relationship among Taobao and other software applications and the anti-counterfeiting mechanism of Taobao were three new codes found during the research. In conclusion, commodity factors, factors related to sellers, social commerce activities, web quality and functionality of Taobao and consumer personal factors were found as five categories of factors that affecting online consumer behavior when do shopping on Taobao. Quality of products was considered as the most important one. Three pieces of new codes were added by this study. The limitation of the study was discussed and the direction of future research was pointed out.
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Shen, Hao. "The effects of procedural and declarative knowledge in consumer information processing /." View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?MARK%202008%20SHEN.

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Farney, Ryan M. "The Influence Exposure Has on Consumer Behavior." Scholarship @ Claremont, 2016. http://scholarship.claremont.edu/cmc_theses/1251.

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Advertising has been around for thousands of years and has proven to be a valuable asset to company revenue. The methods used in advertising have been examined closely more recently, specifically from a psychological standpoint. The human brain reacts to advertisements in different ways. Low and high involvement advertisements stimulate the brain in the subconscious and conscious state effectively. While each of these advertising methods are useful, complex messaging techniques seem to stimulate recall more effectively than simple messaging. In the age of digital advertising, sponsors look to put new resources to use to ensure paid advertisements are doing their job. With data mining and location based services available, advertisers are getting more in touch with the consumer than ever before. The bombardment of advertising is changing the consumer’s eye for low and high involvement and the advertising industry will be forced to adjust yet again.
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Ketron, Seth Christopher. "Size Framing: Conceptualization and Applications in Consumer Behavior." Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc1011747/.

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Size information is vital in many consumer contexts, but currently, no central framework or conceptual model exists for a thorough understanding of the underlying process of how consumers interpret size information and form size judgments. Thus, the purpose of this three-paper dissertation is to introduce such a framework, discuss future research directions based on that framework, and pursue a few of these directions in the second and third papers, both of which focus on a vanity sizing context. The resulting work and findings illustrate the process through which consumers go in forming size judgments and collectively present both scholars and practitioners with a common basis for future study and implementation of findings in contexts in which size information is salient.
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Yuan, Yuan. "Bayesian Conjoint Analyses with Multi-Category Consumer Panel Data." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin162766827512258.

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Books on the topic "Consumer behavior – data processing"

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Naumov, Vladimir. Consumer behavior. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1014653.

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The book describes the basic issues concerning consumer behavior on the basis of the simulation of the decision-making process on buying behavior of customers in the sales area of the store and shopping Internet sites. The classification of models of consumer behavior, based on research in the area of economic, social and psychological theories and empirical evidence regarding decision-making by consumers when purchasing the goods, including online stores. Methods of qualitative and quantitative research of consumer behavior, fundamentals of statistical processing of empirical data. Attention is paid to the processes of consumers ' perception of brands (brands) and advertising messages, the basic rules for the display of goods (merchandising) and its impact on consumer decision, recommendations on the use of psychology of consumer behavior in personal sales. Presents an integrated model of consumer behavior in the Internet environment, the process of perception of the visitor of the company, the factors influencing consumer choice of goods online. Is intended for preparation of bachelors in directions of preparation 38.03.02 "Management", 38.03.06 "trading business" and can be used for training of bachelors in direction of training 43.03.01 "Service", and will also be useful for professionals working in the field of marketing, distribution and sales.
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Konsumentenemotionen: Messung durch computergestützte Verfahren : eine empirische Validierung nicht-verbaler Methoden. Würzburg: Physica-Verlag, 1985.

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Consumer information systems and relationship management: Design, implementation, and use. Hershey, PA: Business Science Reference, 2013.

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Finlay, Steven. Credit scoring, response modeling, and insurance rating: A practical guide to forecasting consumer behavior. 2nd ed. Houndmills, Basingstoke, Hampshire: Palgrave Macmillan, 2012.

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Zuboff, Shoshana. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. New York, USA: Public Affairs, 2019.

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Moe, Wendy. Capturing evolving visit behavior in clickstream data. Cambridge, MA: Marketing Science Institute, 2001.

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M, Worcester Robert, and Downham John, eds. Consumer market research handbook. 3rd ed. Amsterdam: North-Holland, 1986.

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Smith, Andrew. Consumer Behaviour and Analytics. Taylor & Francis Group, 2019.

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Smith, Andrew. Consumer Behaviour and Analytics. Taylor & Francis Group, 2019.

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Smith, Andrew. Consumer Behaviour and Analytics. Taylor & Francis Group, 2019.

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Book chapters on the topic "Consumer behavior – data processing"

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Brockmann, Karl Ludwig, Jens Hemmelskamp, and Olav Hohmeyer. "Definitions, Database and Data Processing." In Certified Tropical Timber and Consumer Behaviour, 42–45. Heidelberg: Physica-Verlag HD, 1996. http://dx.doi.org/10.1007/978-3-642-51735-8_6.

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Spenrath, Yorick, Marwan Hassani, and Boudewijn F. van Dongen. "Online Prediction of Aggregated Retailer Consumer Behaviour." In Lecture Notes in Business Information Processing, 211–23. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_16.

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AbstractPredicting the behaviour of consumers provides valuable information for retailers, such as the expected spend of a consumer or the total turnover of the retailer. The ability to make predictions on an individual level is useful, as it allows retailers to accurately perform targeted marketing. However, with the expected large number of consumers and their diverse behaviour, making accurate predictions on an individual consumer level is difficult. In this paper we present a framework that focuses on this trade-off in an online setting. By making predictions on a larger number of consumers at a time, we improve the predictive accuracy but at the cost of usefulness, as we can say less about the individual consumers. The framework is developed in an online setting, where we update the prediction model and make new predictions over time. We show the existence of the trade-off in an experimental evaluation on a real-world dataset consisting of 39 weeks of transaction data.
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Wang, Yi, Qixin Chen, and Chongqing Kang. "Electricity Consumer Behavior Model." In Smart Meter Data Analytics, 37–57. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2624-4_2.

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Konyalioglu, Fatma Irem. "Consumer Behavior in the Metaverse." In Studies in Big Data, 161–75. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4641-9_11.

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Currie, Brianna A., Alexandra D. French, and M. Ali Ülkü. "Sustainability, Big Data, and Consumer Behavior." In Big Data Analytics in Supply Chain Management, 109–32. First edition. | Boca Raton : CRC Press, 2021.: CRC Press, 2020. http://dx.doi.org/10.1201/9780367816384-7.

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Walter, Sheryl L., Scott E. Seibert, Daniel Goering, and Ernest H. O’Boyle. "ORIGINAL PAPER: A Tale of Two Sample Sources: Do Results from Online Panel Data and Conventional Data Converge?" In Key Topics in Consumer Behavior, 75–102. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19910-3_7.

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Kamleitner, Bernadette, and Vincent-Wayne Mitchell. "Can Consumers Experience Ownership for Their Personal Data?From Issues of Scope and Invisibility to Agents Handling Our Digital Blueprints." In Psychological Ownership and Consumer Behavior, 91–118. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77158-8_6.

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Anshori, Mohamad Yusak, Denis Fidita Karya, Rizki Amalia Elfita, Laila Alfi Sahrin, and Mira Nirmala Gita. "Domestic Consumption: Relative Product Quality Ethnocentrism, and Domestic Product Belief." In Proceedings of the 19th International Symposium on Management (INSYMA 2022), 349–55. Dordrecht: Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-008-4_45.

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Abstract The increasing number of cosmetics imports in Indonesia has resulted in the domestic cosmetic industry has to compete with foreign products. This study aims to determine consumer behavior in using local products using cognitive (quality evaluation), normative (social and personal norms relating to the country of origin), and affective (symbolic and emotional values of the country of origin) in shaping consumer preferences. This research is quantitative. Data collection was carried out via a questionnaire distributed to 400 respondents with a population of Indonesian consumers who used cosmetics. The analysis technique used Smart PLS. The results show that relative product quality, domestic product trust, consumer ethnocentrism, and patriotism have a significant effect on domestic consumption. This research also shows that patriotism and conservatism influence consumer ethnocentrism and cosmopolitanism have an inverse influence on consumer ethnocentrism.
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Torelli, Carlos J., Jie (Doreen) Shen, and Maria A. Rodas. "Culture and Consumer Behavior." In The Oxford Handbook of Cross-Cultural Organizational Behavior, 561–82. Oxford University Press, 2024. http://dx.doi.org/10.1093/oxfordhb/9780190085384.013.22.

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Abstract Culture is a fundamental driver of consumer behavior. This chapter reviews the three most common approaches for incorporating culture into frameworks of consumer behavior and describes key findings about: (1) the effect of culture on consumers’ attention and information processing, (2) the persuasiveness of culturally matched advertising messages, (3) the contexts in which culture is more likely to impact consumer behavior, and (4) the behavior of bicultural consumers. The chapter concludes with a discussion of a future research agenda that focuses on the study of the tightness–looseness (T-L) distinction for refining cultural predictions, the further integration of the different approaches to cultural research for understanding a variety of consumer phenomena in a globalized world, the adoption of a polycultural approach for explaining the behavior of multicultural consumers with knowledge about two or more cultures, and the potential insights from new methodologies borrowed from neuroscience, machine learning, and big data analyses.
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Wang, Chen-Ya, Yi-Chun Lin, Hsia-Ching Chang, and Seng-Cho T. Chou. "Consumer Sentiment in Tweets and Coupon Information-Sharing Behavior." In Information Diffusion Management and Knowledge Sharing, 823–42. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0417-8.ch041.

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The authors aim to explore the correlation between coupon information-sharing behavior and consumer sentiment by analyzing tweets. They used Twitter application programming interface to retrieve users' tweets, and took a machine learning approach for sentiment analysis. After the data pre-processing procedure, the authors then examined the correlation between sentiments in tweets and coupon information sharing. More than half of the most active users showed that their coupon information-sharing behavior correlated to both positive and negative sentiments. The results also showed that the response, coupon information sharing, for positive/negative sentiment had no significant time shifting pattern for most of the users. This study preliminary verifies the assumption that there is a correlation between users' sentiments in tweets and coupon information-sharing behavior, and indicates some interesting findings. The authors' findings may shed light on whether sentiment plays a role in social media communication concerning the sharing of coupon information.
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Conference papers on the topic "Consumer behavior – data processing"

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Sharifian, Nastaran, and Kevin Burke. "Processing k-Inflated Poisson-Binomial Distributed Longitudinal Data in Consumer Behavior." In 2023 IEEE Smart World Congress (SWC). IEEE, 2023. http://dx.doi.org/10.1109/swc57546.2023.10449241.

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Batraga, Anda, Jelena Salkovska, Katrina Kellerte, and Liene Kaibe. "pportunities and limitations for using big data in the marketing industry." In 24th International Scientific Conference. “Economic Science for Rural Development 2023”. Latvia University of Life Sciences and Technologies. Faculty of Economics and Social Development, 2023. http://dx.doi.org/10.22616/esrd.2023.57.033.

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The use of big data in marketing in Latvia is a relatively new and unexplored field, therefore the most important information about the possibilities of using big data in marketing and the limitations related to it has not been collected so far. In order for company management and marketing specialists to be able to manage the risks arising from the use of big data and the implementation of privacy policies, it is necessary to identify them, assess the main causes and analyse the impact of risks on consumer behavior. Despite the rapid development of technology, many companies avoid the use of big data, and the interest of consumers in the use and processing of their data is also growing. The research objective is to evaluate the scientific and practical problems of big data use and privacy, to identify the main actualities of big data use in marketing, the advantages and limitations of use, as well as to investigate consumer attitudes towards the use of big data. In order to attain the objective of the research, a literature review of public databases, literature and research articles is performed followed by interviews of experts representing big data analytics in Latvia and consumer survey. As a result of the research, it was concluded that consumers' knowledge of big data use and processing technologies is relatively low, therefore there is a low interest in privacy compliance and violations, and the size of the company does not affect the awareness of the importance of using big data in marketing, making business decisions and conducting company consumer analysis.
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Glova, Bogdan, and Ivan Mudryk. "Application of Deep Learning in Neuromarketing Studies of the Effects of Unconscious Reactions on Consumer Behavior." In 2020 IEEE Third International Conference on Data Stream Mining & Processing (DSMP). IEEE, 2020. http://dx.doi.org/10.1109/dsmp47368.2020.9204192.

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Wang, Yan, and Shengxiang She. "Prediction of consumers' pro-environmental behavior based on artificial intelligence." In International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021), edited by Feng Wu, Jinping Liu, and Yanping Chen. SPIE, 2022. http://dx.doi.org/10.1117/12.2631547.

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Singh, Shashi Pal, Ajai Kumar, Neetu Yadav, and Rachna Awasthi. "Data Mining: Consumer Behavior Analysis." In 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). IEEE, 2018. http://dx.doi.org/10.1109/rteict42901.2018.9012300.

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Soltani, Nasim Yahya, and Georgios B. Giannakis. "Online learning of electric vehicle consumers' charging behavior with missing data." In 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2014. http://dx.doi.org/10.1109/globalsip.2014.7032115.

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Perciun, Rodica. "Economia circulară și recuperarea deșeurilor din plastic prin reciclare în Moldova." In International Scientific-Practical Conference "Economic growth in the conditions of globalization". National Institute for Economic Research, 2023. http://dx.doi.org/10.36004/nier.cecg.2023.17.1.

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In the Republic of Moldova, the situation in the field of waste management is similar to countries with an emerging economy because the generation of waste is influenced by numerous factors, such as: the types of economic activity, the difficult macro-financial situation, low incomes, consumer behavior, the degree of responsibility, the level of awareness of the problem of all actors involved in this process. Currently, we can see that the Republic of Moldova is increasingly affected by pollution, including due to plastic waste. The study carried out outlined a synthesis picture on the use of plastic products and plastic packaging, as well as the perception of subjects vis-à-vis the current legislation in the field of waste and plastic products, and created the reflection of society on public policy challenges on plastic waste, which will be addressed in the near future in national circular economy legislation. The purpose of this study was to empirically investigate the level of use of plastic products and the generation of plastic waste, as well as to determine the perception and behavior of the end consumer regarding the circular economy. To achieve the proposed objectives, general scientific methods were applied: analysis, synthesis, history and logic, critical analysis of materials, clustering, but also analysis methods, such as survey, interview, statistical processing of empirical data and official data, graph method, indicators, comparison, grouping, etc. This paper was supported by the State Program 20.80009.0807.22 Development of the mechanism for the formation of the circular economy in the Republic of Moldova.
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Eksin, Ceyhun, Hakan Delic, and Alejandro Ribeiro. "Rational consumer behavior models in smart pricing." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178555.

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Luo, Zhe. "The Consumer Behavior Analysis under Large Data Environment." In Proceedings of the 2017 5th International Education, Economics, Social Science, Arts, Sports and Management Engineering Conference (IEESASM 2017). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/ieesasm-17.2018.69.

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Kuramoto, Kaoru, Yosuke Kurihara, and Satoshi Kumagai. "CONSUMER PURCHASING BEHAVIOR ANALYSIS USING CVS POS DATA." In 53rd International Academic Conference, Dubai. International Institute of Social and Economic Sciences, 2020. http://dx.doi.org/10.20472/iac.2020.053.010.

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Reports on the topic "Consumer behavior – data processing"

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Gross, David, and Nicholas Souleles. Do Liquidity Constraints and Interest Rates Matter for Consumer Behavior? Evidence from Credit Card Data. Cambridge, MA: National Bureau of Economic Research, June 2001. http://dx.doi.org/10.3386/w8314.

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Bachas, Natalie, Peter Ganong, Pascal Noel, Joseph Vavra, Arlene Wong, Diana Farrell, and Fiona Greig. Initial Impacts of the Pandemic on Consumer Behavior: Evidence from Linked Income, Spending, and Savings Data. Cambridge, MA: National Bureau of Economic Research, July 2020. http://dx.doi.org/10.3386/w27617.

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Bhatt, Parth, Curtis Edson, and Ann MacLean. Image Processing in Dense Forest Areas using Unmanned Aerial System (UAS). Michigan Technological University, September 2022. http://dx.doi.org/10.37099/mtu.dc.michigantech-p/16366.

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Imagery collected via Unmanned Aerial System (UAS) platforms has become popular in recent years due to improvements in a Digital Single-Lens Reflex (DSLR) camera (centimeter and sub-centimeter), lower operation costs as compared to human piloted aircraft, and the ability to collect data over areas with limited ground access. Many different application (e.g., forestry, agriculture, geology, archaeology) are already using and utilizing the advantages of UAS data. Although, there are numerous UAS image processing workflows, for each application the approach can be different. In this study, we developed a processing workflow of UAS imagery collected in a dense forest (e.g., coniferous/deciduous forest and contiguous wetlands) area allowing users to process large datasets with acceptable mosaicking and georeferencing errors. Imagery was acquired with near-infrared (NIR) and red, green, blue (RGB) cameras with no ground control points. Image quality of two different UAS collection platforms were observed. Agisoft Metashape, a photogrammetric suite, which uses SfM (Structure from Motion) techniques, was used to process the imagery. The results showed that an UAS having a consumer grade Global Navigation Satellite System (GNSS) onboard had better image alignment than an UAS with lower quality GNSS.
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Cairo, Jessica, Iulia Gherman, and Paul Cook. The effects of consumer freezing of food on its use-by date. Food Standards Agency, July 2021. http://dx.doi.org/10.46756/sci.fsa.ret874.

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The current Food Standards Agency consumer guidance states that consumers can freeze pre-packed food right up to the “use-by” date and, once food has been defrosted, it should be consumed within 24 hours. This strategic review has collated relevant data to determine whether there is an increased risk in relation to freezing ready-to-eat and non-ready-to-eat foods on the use-by date compared to the day before the use-by date. The review has focused on how the shelf-life of a food is determined and the effects of freezing, thawing and refrigeration on foodborne pathogens, including Bacillus spp., Campylobacter spp., Clostridium botulinum, Clostridium perfringens, Listeria monocytogenes, Salmonella, pathogenic Escherichia coli and Shigella spp. In the UK, food business operators are responsible for setting the safe shelf-life of a food which, in practice, should take into consideration the consumer habits, as well as the factors affecting shelf-life, such as food product characteristics, food processing techniques, transport, retail and domestic food storage temperatures, and type of packaging. Some countries, such as Ireland, New Zealand and Canada specifically recommend including safety margins within shelf lives. This is used to maintain brand integrity because it ensures that the food is consumed in its optimum condition. The FSA has collaborated with other organisations in the production of several guidance documents; however, there is no explicit requirement for the consideration of a margin of safety when setting shelf-life. There is also no legal requirement in the UK to consider a safety margin when setting shelf-life. According to regulations, pathogens should not be present in sufficient levels to cause foodborne illness on the use-by date, as food should still be safe to eat on that day. Given that these requirements are met, the risk assessed in this report arises from the processes of freezing, thawing and subsequent refrigerated storage for a further 24 hours, and the potential for these to increase pathogen levels. In this review, it was found that there is a risk of additional growth of certain pathogens during the refrigerated storage period although the impact of freezing and thawing on the extent of this growth was not readily evident. This risk would relate specifically to ready-to-eat foods as cooking of non-ready-to-eat foods after defrosting would eliminate pathogens. This report explores the potential issues related to consumer freezing on the use-by date and identifies additional information or research required to understand the risks involved. Overall, there is little evidence to suggest a significant change in risk between consumers freezing ready-to-eat food on the use-by date compared to freezing the food on the day before the use-by date. Specific areas that merit further research include the risks due to low temperature survival and growth of L. monocytogenes. There is also a lack of research on the effects of freezing, defrosting and refrigeration on the growth and toxin production of non-proteolytic C. botulinum, and the growth of Salmonella during domestic freezing and thawing. Finally, more information on how food business operators set shelf-life would enable a better understanding of the process and the extent of the safety margin when determining shelf-life of ready-to-eat and non-ready-to-eat foods.
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Leavy, Michelle B., Danielle Cooke, Sarah Hajjar, Erik Bikelman, Bailey Egan, Diana Clarke, Debbie Gibson, Barbara Casanova, and Richard Gliklich. Outcome Measure Harmonization and Data Infrastructure for Patient-Centered Outcomes Research in Depression: Report on Registry Configuration. Agency for Healthcare Research and Quality (AHRQ), November 2020. http://dx.doi.org/10.23970/ahrqepcregistryoutcome.

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Background: Major depressive disorder is a common mental disorder. Many pressing questions regarding depression treatment and outcomes exist, and new, efficient research approaches are necessary to address them. The primary objective of this project is to demonstrate the feasibility and value of capturing the harmonized depression outcome measures in the clinical workflow and submitting these data to different registries. Secondary objectives include demonstrating the feasibility of using these data for patient-centered outcomes research and developing a toolkit to support registries interested in sharing data with external researchers. Methods: The harmonized outcome measures for depression were developed through a multi-stakeholder, consensus-based process supported by AHRQ. For this implementation effort, the PRIME Registry, sponsored by the American Board of Family Medicine, and PsychPRO, sponsored by the American Psychiatric Association, each recruited 10 pilot sites from existing registry sites, added the harmonized measures to the registry platform, and submitted the project for institutional review board review Results: The process of preparing each registry to calculate the harmonized measures produced three major findings. First, some clarifications were necessary to make the harmonized definitions operational. Second, some data necessary for the measures are not routinely captured in structured form (e.g., PHQ-9 item 9, adverse events, suicide ideation and behavior, and mortality data). Finally, capture of the PHQ-9 requires operational and technical modifications. The next phase of this project will focus collection of the baseline and follow-up PHQ-9s, as well as other supporting clinical documentation. In parallel to the data collection process, the project team will examine the feasibility of using natural language processing to extract information on PHQ-9 scores, adverse events, and suicidal behaviors from unstructured data. Conclusion: This pilot project represents the first practical implementation of the harmonized outcome measures for depression. Initial results indicate that it is feasible to calculate the measures within the two patient registries, although some challenges were encountered related to the harmonized definition specifications, the availability of the necessary data, and the clinical workflow for collecting the PHQ-9. The ongoing data collection period, combined with an evaluation of the utility of natural language processing for these measures, will produce more information about the practical challenges, value, and burden of using the harmonized measures in the primary care and mental health setting. These findings will be useful to inform future implementations of the harmonized depression outcome measures.
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Schaur, Georg, Christian Volpe Martincus, and Jerónimo Carballo. Trust No One?: Security and International Trade. Inter-American Development Bank, June 2016. http://dx.doi.org/10.18235/0011739.

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Security concerns in a context of increasingly segmented supply chains haveled to stricter border control measures, which may potentially negatively affect international trade. Customs around the world have therefore implemented security-motivated certification programs to facilitate licit trade. These programs offer trustworthy trading firms, i.e., Authorized Economic Operators (AEOs), several advantages in the administrative processing of their shipments including less frequent physical inspections and expedited customs clearance. In this study we focus on Mexico's AEO Program NEEC. In particular, we evaluate the impact of this program by primarily carrying out differences-in-differences estimations on highly disaggregated firm-level data that cover the entire universe of export and import transactions of the country over the period 2009-2014. Estimation results suggest that NEEC has been associated with less physical inspections and shorter clearance times and has thereby favored increased firms' exports. Effects seem to be stronger on the frequency of shipments and on consumer goods, industrial inputs, and capital goods.
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Zeballos, Eliana, and Wilson Sinclair. Estimating the state-level food expenditure series. Washington, D.C.: USDA Economic Research Service, April 2023. http://dx.doi.org/10.32747/2023.8023696.ers.

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The USDA, Economic Research Service's (ERS) Food Expenditure Series (FES) is a comprehensive measure of the total value of food acquired in the United States over time. FES provides users with data to evaluate changes in food spending and its composition; however, FES is limited to the national level. This report presents the methodology and data used to generate food expenditure estimates at the State level. The State-level FES follows a similar methodology used in the national level but with a different underlying dataset and benchmarked to the national-level estimates. The national-level estimates are based primarily on food sales reported in the U.S. Department of Commerce, Bureau of the Census' Economic Census, which is published every 5 years, and uses three annual surveys to interpolate between years and extrapolate lagged data forward. The State-level FES estimates are based primarily on sales reported in the National Establishment Time Series Database. The database provides time-series data at the establishment level across all sectors, including grocery stores and food service outlets. The State-level FES can be used by government agencies, academics, the public, and other stakeholders to understand differences in consumer food acquisitions and spending behavior at a more granular level.
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Avis, William. Drivers, Barriers and Opportunities of E-waste Management in Africa. Institute of Development Studies (IDS), December 2021. http://dx.doi.org/10.19088/k4d.2022.016.

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Population growth, increasing prosperity and changing consumer habits globally are increasing demand for consumer electronics. Further to this, rapid changes in technology, falling prices and consumer appetite for better products have exacerbated e-waste management challenges and seen millions of tons of electronic devices become obsolete. This rapid literature review collates evidence from academic, policy focussed and grey literature on e-waste management in Africa. This report provides an overview of constitutes e-waste, the environmental and health impacts of e-waste, of the barriers to effective e-waste management, the opportunities associated with effective e-waste management and of the limited literature available that estimate future volumes of e-waste. Africa generated a total of 2.9 million Mt of e-waste, or 2.5 kg per capita, the lowest regional rate in the world. Africa’s e-waste is the product of Local and imported Sources of Used Electronic and Electrical Equipment (UEEE). Challenges in e-waste management in Africa are exacerbated by a lack of awareness, environmental legislation and limited financial resources. Proper disposal of e-waste requires training and investment in recycling and management technology as improper processing can have severe environmental and health effects. In Africa, thirteen countries have been identified as having a national e-waste legislation/policy.. The main barriers to effective e-waste management include: Insufficient legislative frameworks and government agencies’ lack of capacity to enforce regulations, Infrastructure, Operating standards and transparency, illegal imports, Security, Data gaps, Trust, Informality and Costs. Aspirations associated with energy transition and net zero are laudable, products associated with these goals can become major contributors to the e-waste challenge. The necessary wind turbines, solar panels, electric car batteries, and other "green" technologies require vast amounts of resources. Further to this, at the end of their lifetime, they can pose environmental hazards. An example of e-waste associated with energy transitions can be gleaned from the solar power sector. Different types of solar power cells need to undergo different treatments (mechanical, thermal, chemical) depending on type to recover the valuable metals contained. Similar issues apply to waste associated with other energy transition technologies. Although e-waste contains toxic and hazardous metals such as barium and mercury among others, it also contains non-ferrous metals such as copper, aluminium and precious metals such as gold and copper, which if recycled could have a value exceeding 55 billion euros. There thus exists an opportunity to convert existing e-waste challenges into an economic opportunity.
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Beavers, Calvin, Chad Day, Austin Krietemeyer, Scott Peterson, Yushin Ahn, and Xiaojun Li. Mapping of Pavement Conditions Using Smartphone/Tablet LiDAR Case Study: Sensor Performance Comparison. Mineta Transportation Institute, July 2024. http://dx.doi.org/10.31979/mti.2024.2224.

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Poor road conditions affect millions of drivers, and assessing the condition of paved surfaces is a critical step towards repairing them. This project explores the feasibility of using the Apple iPad Pro LiDAR sensor as a cost-effective tool for assessing the damage and condition of paved surfaces. Our research aims to provide accurate and precise measurements using readily available consumer devices and compare the results to state-of-the-art equipment. This investigation involved visual inspection, identification, and classification of pavement distresses, followed by a comparison of the iPad and iPhone LiDAR data with a survey-grade terrestrial laser scanner. The project revealed several limitations of the iPad Pro-based LiDAR approach. The level of detail captured in the scans was relatively low, with a best-case resolution of 1 cm and an inability to detect smaller cracks and shallow potholes. Longer scans (in terms of both time and distance) led to geometric anomalies in the surface models. Colorized scans provided some visual contrast, aiding in the identification of damage, particularly on moderately damaged concrete surfaces. The potential sources of error were identified, including the performance of the Inertial Measurement Unit (IMU), the limitations of the LiDAR sensor itself, and the opaque nature of onboard data processing within the 3D Scanner App. Suggestions for improvement included the use of gimbal stabilizers to enhance scan quality and the exploration of more intensive PC-based processing for raw data analysis. Hardware advancements by Apple and software enhancements by app developers were also highlighted as potential areas for future improvement. While the project revealed limitations and challenges, the authors acknowledge the possibility of future hardware upgrades, augmented reality advancements, and improvements in sensor accuracy and processing. However, based on this project’s findings, the iPad Pro LiDAR approach currently falls short of providing the necessary resolution and accuracy required for comprehensive roadway damage assessment. Results indicate that additional developments are necessary to address the identified limitations and make this method a viable and cost-effective solution for roadway surface evaluation.
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Groeneveld. L51690 Evaluation of Modern X-70 HFER Line Pipe. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), May 1992. http://dx.doi.org/10.55274/r0010316.

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In recent years, several pipe mills have produced heavy-wall, large diameter pipe from plates of microalloyed steels that were controlled-rolled to develop properties to meet the API 5LX Grade 70 or Grade 80 requirements and that were electric resistance welded (ERW) using advanced techniques. The use of advanced electric seam-welding practices on the steels produced using advanced steel making and processing methods offers the possibility of obtaining high strength line pipe at a lower cost than pipes produced from similar steels but with double submerged arc seam welds. The present study was undertaken to evaluate the properties of the pipe body and the ERW seam-weld region of a recently produced heavy-wall X70 line pipe. This report describes the evaluation of that pipe. The mechanical properties of the weld zone from this pipe were evaluated and reported under Task 18-89. Those data are included in this report. A high-frequency electric resistance welded (HFERW) X70 line pipe was evaluated to assess the strength properties, fracture behavior, thermal stability, and susceptibility to environmentally induced degradation that may affect its behavior in gas transmission service. A 20-inch-(508 mm)-diameter by 0.500-inch-(12.7 mm)-wall high-frequency, electric-resistance-welded (HFERW) X70 line pipe was evaluated to assess the strength properties, fracture behavior, thermal stability, and susceptibility to environmentally induced degradation that may affect its behavior in gas transmission service. The steel from which the pipe was produced was processed using advanced steel-making practices that resulted in a low sulfur content and was microalloyed with Cb, V, and Ti. The steel was controlled rolled with 75 percent of the rolling reduction being accomplished at temperatures below 1418 F (770 C); the finishing temperature was 1328 F (720 C) and the steel was hot coiled after finishing. The seam weld was produced by high frequency electric resistance welding and the seam weld region was post-weld normalized.
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