Use Of Transaction Data Research Articles

The impact of the COVID-19 pandemic on grocery shopper behaviour: Analysis of shopper behaviour change using store transaction data.

The systemic shock of coronavirus (COVID‐19) and its impact on the global economy has been unprecedented with grocery shopper behaviour changing dramatically through various stages of the pandemic. COVID‐19 has caused unusual market conditions, with significant changes to grocery shopper behaviour that need to be understood to allow for appreciation of shopper behaviour change and retail planning implications during future systemic shocks. The aim of this study was therefore to understand grocery‐shopping behaviour during COVID‐19. Specific objectives were to investigate changes to grocery sale patterns by basket size, composition and category, as well as during specific time periods of the pandemic. The use of transaction data using a range of market basket indicators (e.g., value, size, product mix), revealed profound changes that indicate the challenge shoppers faced navigating a new ‘normal grocery shop’ and the pressure on retailers to analyse consumption changes in order to prioritise demand planning. While the use of this data and analysis approach is an important contribution to consumer behaviour research, our focus was on the bigger patterns observed through the data pertaining to changes in shopper behaviour during systemic shocks. A key contribution of this paper is how the use of transaction data from grocery retail provides a nuanced understanding of how grocery shoppers responded leading up to and during the pandemic. For example, we found that grocery shoppers purchased more than just ‘daily staples’ to stock‐up during the pandemic, with increased awareness of health and wellbeing an important aspect.

Users’ Experiences with the Use of Transaction Data to Estimate Consumption-Based Emissions in a Carbon Calculator

With global greenhouse gas (GHG) emissions ever increasing, we are currently seeing a renewed interest in carbon footprint calculators (or carbon calculators for short). While carbon calculators have traditionally calculated emissions based on user input about e.g., food, heating, and traveling, a new development in this area is the use of transaction data to also estimate emissions based on consumption. Such carbon calculators should be able to provide users with more accurate estimations, easier input possibilities, and an incentive to continue using them. In this paper, we present the results from a survey sent to the users of such a novel carbon calculator, called Svalna. Svalna offers users the possibility to connect their bank account. The transaction data are then coupled with Environmental Extended Multi Regional Input Output data (EE-MRIO) for Swedish conditions which are used to determine a continuous overview of the user’s greenhouse gas emissions from consumption. The aim of the survey was to (a) understand whether people are willing to connect their bank account, (b) whether they trust the calculations of their emissions, and (c) whether they think the use of Svalna has an effect on their behaviour. Furthermore, we wanted to know how Svalna could be improved. While the results of the survey showed that many users were willing to connect their bank account, a rather large part of the users perceived safety risks in doing so. The users also showed an only average level of trust in the correctness of the estimated greenhouse gas emissions. A lack of trust was attributed to experiencing technical problems but also to not knowing how the emissions were calculated and because the calculator could not capture all details of the user’s life. However, many users still indicated that the use of Svalna had helped them to initiate action to reduce their emissions. In order to improve Svalna, the users wanted to be able to provide more details, e.g., by scanning receipts and get better options for dealing with a shared economy. We conclude this paper by discussing some opportunities and challenges for the use of transaction data in carbon footprint calculators.

Algorithm to Measure Daily Bus Passenger Miles Using Electronic Farebox Data for National Transit Database Section 15 Reporting

New York City Transit (NYCT) implemented an automated algorithm to estimate daily bus unlinked trips, infer passenger miles, and compute average trip lengths by route with the use of transaction data from an entry-only automated fare collection (AFC) system. Total onboard miles are inferred from symmetries in bus passengers’ daily activity patterns. NYCT's algorithm uses rigorously tested engineering assumptions to detect common data errors caused by mechanical failures, imperfect driver-farebox interactions, and operational reality and applies statistically measured adjustment factors to correct or interpolate for missing passengers from non-AFC boardings and malfunctions. Surveys revealed that under typical operating conditions, non-AFC passengers and farebox data transmission errors accounted for 12% and 5.5% of missing ridership, respectively. The fault-tolerant algorithm uses non-geographic transaction data from an AFC system without automated vehicle locator functionality and directly computes aggregate passenger miles by inferring origin locations from transaction time stamps with scheduled average speed assumptions and without assigning each passenger's precise destination. NYCT focused on fully automatic, production-ready algorithms by rejecting alternatives that required excessive coding effort, processor time, difficult-to-obtain data, or manual intervention in favor of logical inference, statistical estimation, and symmetry. Meticulous parallel testing demonstrated that resultant average trip lengths were stable across days and correlate well with manually collected stop-by-stop ridership data. Annual passenger miles were within -1% to 4% of the National Transit Database (NTD) ±10% sample data and were approved by FTA for NTD Section 15 submission.

Hedonic models, internet‐based technologies, and the provision of online property appraisal

PurposeThe objective of this paper is to propose the use of transaction data, hedonic model and internet‐based technologies to provide potential home buyers and sellers with instant and online property appraisal services by real estate‐related firms.Design/methodology/approachHedonic price model is performed to estimate the price index of individual housing estate. The values of the coefficients of the attributes may change in response to a changing environment. A professional judgment should be given by appraisers on time intervals to re‐run the hedonic price models, if needed. Once the algorithms have been updated, authenticated users can connect to the system via a secure socket layer. They can browse and search the valuation reports by entering search criteria of premises into the system that filters the results instantly according to the request of users, and a detailed asset valuation report will be displayed in the browser.FindingsThis sort of online services is believed to save consumers' time, by offering timely property appraisal services that facilitate property transactions.Originality/valueCurrently, local large property developers, agencies and surveying firms are still relying on manual processes of providing selected transaction data via the web to their prospective customers. The system proposed and developed as described in this paper is an automatic and self‐learning system that produces reliable and authentic results based on large quantity of historical transaction records. The professional judgments of appraisers add flavor to the final valuation of properties. As well as providing information to individual buyers/sellers, the system is most suitable to investors who own a large portfolio of properties as the total capitalization of the portfolio can be computed easily and quickly. REITs are examples of these portfolio owners.

Dealer Bid‐Ask Quotes and Transaction Prices: An Empirical Study of Some AMEX Options

ABSTRACTThis paper, utilizing dealer's “trading book” information, presents some empirical evidence supporting the validity of a dealer pricing model. It shows that much of the transaction prices variation may be explained by the specialist's optimal determination of his bid and ask quotes. Furthermore, it demonstrates that the dealer's bid‐ask spread is an important explanatory variable in the observed transaction return. Finally, it indicates that the dealer's inventory level may affect his quotes and thus the transaction prices and order arrivals. The paper provides insights into the relationship between transaction prices and equilibrium prices, which will permit more extensive use of transaction data in empirical investigations. It also provides a better understanding of optimal dealer pricing strategies, suggesting that the proposed empirical model may be used to evaluate a dealer's trading performance.