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The purpose of this study is to determine the effect of travel agencies' reservation systems and online transaction capacities on their competitiveness. With the developments in technology and the integration of these developments in all business areas, travel agencies have also had to benefit from information and communication technologies. In the research, the primary data were used, and the data were collected by the questionnaire method. The convenience sampling method was used, and data were collected from a total of 566 travel agency managers in Istanbul. According to the results of the research, reservation systems and online transactions capacities of travel agencies affect their competitiveness. Reservation systems and online transactions capacities of travel agencies affect their competitiveness in specific issues such as cost, service quality and market share. Although appropriate and enlightening results were obtained regarding travel agencies technological capacity and competitiveness, research was carried out on the perceptions and evaluations of the managers in this study. In this context, in future studies, studies can be conducted on the relationships between measurable quantitative variables such as the online transactions of travel agencies and the capacity and transaction volume of reservation systems and competition structures. The study reveals that the traditional office travel agencies should be more successful than their competitors in terms of reservation systems and online transaction capacities to gain affordable cost, quality service and more market share.

The purpose of this study was to investigate the factors determining the behavioral intention of using food delivery apps (FDAs) during COVID-19 pandemics, under a case study of Bangkok, Thailand. The study was necessitated by the increased use of FDAs during the lockdown; online transactions were considered important in preventing the spread of the virus. The study used quantitative techniques involving structural equation model (SEM) to evaluate the effects of exogenous variables on endogenous variables. Primary data were collected from people who had installed and used FDAs. The findings indicated that performance expectancy, effort expectancy, social influence, timeliness, task technology fit, perceived trust, and perceived safety significantly influence the behavioral intention to use (BIU) to use food delivery apps during the COVID-19 pandemic. To this end, effort should be intensified to study and understand FDAs as it pertains to safety and usage.

Health-care database is a collection of health-care data organized for storage, accessibility, and retrieval. Health-care database serves to replace the paper documents, filing cabinets of old, and file folders, which will be more convenient and immediate. At present, health-care system is in a transition from a patient care perspective toward information-based medicine requiring data from various sources such as laboratory tests, genetic tests, medical images, family histories, and patient medical records, which need to be easily stored, integrated, and analyzed. The most commonly used database in health care is the online transaction processing (OLTP) database among different types of databases available. OLTP database is the one that is a single computer application runs on, and the main strength of it is quick and real-time transaction processing. Electronic health record is a prime example of the OLTP, and practitioners enter routine clinical and laboratory data into them during usual practice as a record of the patient's care. Data can be stored externally in a secure place and can be backed up to prevent data loss with these health-care databases. The back-end data can therefore become more accurate and standardized because of the front-end software providing tip text and enforce data. Furthermore, the health-care database allows the rapid processing of classical transactions such as laboratory results and payment claims as the data are computerized.

Managerial responses to online customer reviews not only affect customers who receive the responses but may also influence subsequent customers who observe the responses. This externality arises because of the public nature of online interactions. However, prior studies were mainly in offline settings where such externality rarely exists. In this study, we assess the magnitude of such externality. Using a difference-in-difference-in-differences framework and matched hotels across two large travel agencies, we find that managerial responses indeed have a significant and positive impact on the volume of subsequent customer reviews. The impact on the review valence is not evident, which can be attributed to the unique design of identity disclosure in our research context. Furthermore, our results suggest nuances that were not known in the prior literature. For example, responding to positive and negative reviews may have different effects on future reviews, and managers should provide detailed responses to negative reviews but brief ones to positive reviews. Our results offer managerial implications to service providers on how to improve customer engagement in the interconnected online environment. The online appendix is available at https://doi.org/10.1287/isre.2018.0781 .

Investigations of online transaction data often face the problem that entries for identical products cannot be identified as such. There is, for example, typically no unique product identifier in online auctions; retailers make their offers at price comparison sites hardly comparable and online stores often use different identifiers for virtually equal products. Existing studies typically use data sets that are restricted to one or only a few products in order to avoid product heterogeneity if a unique product identifier is not available. We propose the Ambiguous Identifier Clustering Technique (AICT) that identifies online transaction data that refer to virtually the same product. Based on a data set of eBay auctions, we demonstrate that AICT clusters online transactions for identical products with high accuracy. We further show how researchers benefit from AICT and the reduced product heterogeneity when analyzing data with econometric models.

An extensive literature in economics and finance has documented home bias , the tendency that transactions are more likely to occur between parties in the same geographical area rather than outside. Using data from a large online crowdfunding marketplace and employing a quasi-experimental design, we find evidence that home bias still exists in this virtual marketplace for financial products. Furthermore, through a series of empirical tests, we show that rationality-based explanations cannot fully explain such behavior and that behavioral reasons at least partially drive this remarkable phenomenon. As crowdfunding becomes an alternative and increasingly appealing channel for financing, a better understanding of home bias in this new context provides important managerial, practical, and policy implications. This paper was accepted by Lee Fleming, entrepreneurship and innovation .

We propose information-directed sampling —a new approach to online optimization problems in which a decision maker must balance between exploration and exploitation while learning from partial feedback. Each action is sampled in a manner that minimizes the ratio between squared expected single-period regret and a measure of information gain: the mutual information between the optimal action and the next observation. We establish an expected regret bound for information-directed sampling that applies across a very general class of models and scales with the entropy of the optimal action distribution. We illustrate through simple analytic examples how information-directed sampling accounts for kinds of information that alternative approaches do not adequately address and that this can lead to dramatic performance gains. For the widely studied Bernoulli, Gaussian, and linear bandit problems, we demonstrate state-of-the-art simulation performance. The electronic companion is available at https://doi.org/10.1287/opre.2017.1663 .

The PANDA experiment has been designed to incorporate software triggers and online data processing. Although PANDA may not surpass the largest experiments in terms of raw data rates, designing and developing the processing pipeline and software platform for this purpose is still a challenge. Given the uncertain timeline for PANDA and the constantly evolving landscape of computing hardware, our attention is directed toward ensuring the futureproofness of the solutions we develop. The PandaR2 is a concept for a framework handling online data processing in heterogeneous and distributed HPC environments. It utilizes the SYCL programming model as the primary technology for parallelization and offloading. Being a new and standalone entity, PandaR2 also interfaces with the PANDA’s original ROOT-based simulation and analysis framework - PandaRoot, connecting the best of both worlds. This contribution aims to present an overview of the PandaR2 SYCL-centric architecture. We will share experiences with SYCL during the codebase design process, particularly highlighting its portability across various hardware platforms and compilers. Additionally, we will showcase the performance results of the initial algorithms implemented in PandaR2.

The digital economy, driven by information and communication technologies (ICT), has profoundly transformed in recent decades. The digitalization of society has given rise to an economic environment in which information, connectivity, and innovation play fundamental roles. In this context, a technology that has emerged as a fundamental pillar of the digital economy is the chain of blocks, commonly known as blockchain. Blockchain is a technology that has revolutionized the way online data and transactions are managed and shared. Through its ability to create secure, transparent, and decentralized ledgers, blockchain has paved the way for the digital economy, facilitating trust in digital transactions and enabling various applications ranging from cryptocurrencies to supply chain management and intellectual property. This study will delve into blockchain and its influence on the digital economy. It will explore how this technology has reshaped how companies interact, how consumers access services, and how new business models are developed in a constantly evolving digital environment. Additionally, the challenges and opportunities that blockchain presents in the context of the digital economy will be analyzed, and how it is helping to shape the future of business and society in general. As the exploration of blockchain and its impact on the digital economy progresses, it becomes evident how these two forces converge, generating a promising digital landscape full of significant opportunities and transformations. This phenomenon is consistently supported by a growing body of research and analysis, which underlines the growing influence of blockchain on the global economy. The dynamic interplay between these two spheres, blockchain and the digital economy, is constantly evolving and offers an exciting glimpse into the future regarding innovation and disruption across a wide range of sectors. As a result, significant opportunities are looming for those seeking to understand and capitalize on these emerging trends. Throughout this study, the current trends and most intriguing perspectives that shape this landscape will be broken down, offering a deeper insight into how blockchain and the digital economy are shaping an extraordinary digital future.