dataesr/scientific-paragraphs-categorization huggingface dataset:
Only the hard discounters, retailers 1 and 7, do not offer online services. Thus, we do not distinguish the solo click and drive and the attached click and drive for the online distribution channel, but we know the main kind of click and drive chosen for each retailer. The Kantar dataset provides information about the city where the panelists live for each household, and the TradeDimension dataset provides the localization of the grocery stores, so it permits me to calculate the distances between their town center and the nearest grocery store 2. Table  7  in the Appendix provides the average distances between the household's residence and the closest grocery store for each retailer. Distance is also considered for online trips because about 94% are done through a click and drive store, so consumers have to go to the store to pick up their order for most online trips.
Demand Results Using a simulated maximum likelihood method, I estimated a random coefficients logit model on the whole sample of 937,488 shopping trips done in the last 34 weeks of 2017. I consider the purchase sample as a cross-sectional dataset; each purchase is independent of the other. 2015 ) also find a positive impact of store experience on store choice decisions. The online store experience coefficient is higher than the offline store experience coefficient. This suggests that the preference for known retailers is stronger online than in brick-andmortar stores. The offline fixed effect is lower than the online one. In addition, if the consumer is hungry during a physical experience in a traditional store or is tempted by a food product by touch or smell, he is likely to make impulse purchases and be less price sensitive.
Retailers that have mainly opened solo click and drive stores get higher market shares and offline downstream margins and, therefore, higher profits with e-commerce. In contrast, most retailers that have chosen an attached strategy obtain fewer market shares and profits. However, there is only a correlation between the online click and drive strategy and the impact of e-commerce on profit. Decisions regarding online distribution strategy are exogenous. It would be interesting to assess the causal effect of retailers' click and drive strategy choices on their profits by endogenizing this choice in the supply model. However, it would also require differentiating for each retailer, purchases from independent warehouses (solo strategy) to the attached or shared warehouse (attached strategy), but the kantar dataset does not allow this distinction.
For enterprises, ERP systems integrate organizations' applications at the informational and operational level to utilize and automate many back-office functions related to technology and information services by collecting, storing, managing, and interpreting business activity data. Over the years, these ERP systems offer increasingly improved enterprise application integration by providing the most integrated suite of applications to perform standard business functions  [4]. At its most basic level, ERP software integrates these various functions into one complete system to streamline processes and information across the entire organization. The central feature of all ERP systems is a shared database that supports multiple functions used by different business units. Today, traditional on-premises ERP systems are gradually being eclipsed by the growing demand for cloud-based ERP systems  [5]  due to their lower costs and faster deployment.
Moreover, the interoperability approach is applied through the technological barrier and specifically on the level of data by ensuring database connection and remote access. The execution of EOS has been designed to respect the most recent interoperability approaches  [13]  to help the improvement of federated and unified methodologies of enterprise interoperability. This respect of interoperability is set from conceptual level down to technical level, where as one of the first implementations, the High-Level Architecture (HLA)  [14]  has been utilized to quickly create an HLA-based interface for achieving federated enterprise interoperability  [15]. Further, interoperability is becoming increasingly critical, but paradoxically, it is not yet fully anticipated, monitored, and accompanied in an effective way to recover from incompatibility problems or failures.
GreneOS Application Architecture GreneOS follows the EOS philosophy by eliminating the need to use multiple applications for teams of users and processes replacing most of this disparate applications/software with one unified platform. GreneOS can be considered as one of the most recent implementations of the EOS metamodel. GreneOS connects all employees, customers, vendors, drones, machinery, robots, facilities, and third-party resources to one single platform, making sure that 100% of an enterprise's internal and external resources converse on a single unified platform. This results in a manifold increase in efficiency, transparency, and actionable business intelligence in real-time, launching an end-to-end transformation where GreneOS itself becomes the enterprise's value chain driver. Figure  5  presents the poles that build GreneOS at the application level.
The viewpoint of GreneOS is that decisions are as good as your data. As described in Figure  5 , GreneOS intends to bring 100% of the enterprise workforce (human and robotic) onto the platform from the delivery executive to the CEO, with no limit on the number of users and a transaction-based pricing model. GreneOS is an intelligent AI-powered platform that continuously learns and becomes smarter each day with machine learning, in turn making your enterprise hyperefficient and more productive (expect 10 times of × increase in efficiency). As recommended by the EOS, GreneOS is not a one-size-fits-all application. It is customized and clinically put together for each customer to seamlessly fit into their culture, needs, and challenges. We do not believe in cookie-cutter solutions. Unlike traditional enterprise software, GreneOS can be rapidly deployed within 6-8 weeks thanks to our transformational zero-code development environment that uses a drag-and-drop interface.
 The Interoperability Management component of the EOS plays the same role as the Integration/Migration Engines of the GreneOS; they are presented as sets of features that provide necessary mapping between heterogeneous resources to make them interoperable through the EOS and GreneOS. Figure  7  illustrates and presents an EOS application layer as workflow where the enterprise operations are executed and generated through the EOS internal components  [23] , noting that this architecture is designed using the HLA technology during the year 2017. The final objective is executing this workflow within the GreneOS technical architecture. At first, each decision-maker accesses the General-Purpose and Vertical software interfaces to request a new job from the day-to-day list of activities and operations.
EOS Workflow to Orchestrate GreneOS Technical Architectures The matching between both technical architectures (GreneOS and EOS) is based on the functionalities, features, and services of each component. The General-Purpose Vertical Software of the EOS and the core applications (Media, iOS, Android, Web) of GreneOS play the same role as computing resources that request a new job from the day-to-day list of activities and operations. The software sends related commands and data to communicate with the EOS or GreneOS The ERM module of the EOS containing the Resource Handling and Resource Controlling components is matched with the Ordered Messaging System of GreneOS; they have the role to check the availability of the resources and preassign them when available, assign agents, and ensure the resource optimization and the resource mapping by selecting the appropriate resources after matching the capabilities required and by taking into consideration the time, performance, and priority Both the EIM component (Information Library and Object) of the EOS and the Persistent/Analytics/Time/Search Databases of GreneOS must manage the exchanged messages and information between resources, acquire the object states, and specify the information object required The IM component of the EOS plays the same role as the Consumer Group of Gre-neOS: it requests from the Resource Management to assign the specified resources/employees/assets and invokes the EIM/Databases to acquire the information and data.
Use-case EOS Workflow with GreneOS GreneOS can handle a workflow process initiated from a discussion with a chatbot presenting different journey options of resources users. The use-case example is described in Figure  9. The implementation of "Business Manager requires a new job" is being detailed with the interfaces of GreneOS. Figure  9  presents how the user chat interface on the smartphone with the workflow part. It starts from the client apps that send requests which are received by controllers on the server-side. Upon receiving requests, the controllers convert the request to a standard message format while incorporating the required metadata to identify the endpoint of the request. Then, the converted messages are produced to the messaging system. At that point, according to Figure  8  descriptions, consumers retrieve the produced messages on the other side of the messaging system.
ESB and SOA The Enterprise Service Bus (ESB) has been defined since  [6, 26]  as a set of tools that guarantee the confidence of data exchanges between the sources and targets of an information system. Thus, it is implemented to allow heterogeneous applications to connect (connectivity) and share/exchange information (interoperability) through a software architecture. Recent works  [27]  show that the ESB methods, approaches, and technologies have now reached a good maturity of functionality in combination with selected tools to achieve a good integration of information systems. A recent systematic literature study  [28]  explained that ESB remains the best promising way for the integration of business applications in distributed and diverse environments. Nevertheless, ESB works by a data exchange structure that is mostly standardized and, rather than making direct interfaces between applications, these applications subscribe to the messages available in the ESB that interest them  [29].
In the frame of the EOS, the main component of an ESB is still a route that allows information to circulate between applications (an "application bus") and whose purpose is to guarantee the routing of data as well as its persistence  [30]. However, in addition, we assume that the bus connection can be achieved with connectors (interoperability with legacy  [31]  implemented with low code obtained again from discussion with users. So, the EOS supports data transport in a more flexible way, being supported by ontologies and semantics with the ability to route and deliver large volumes of data in real time more easily. The ESB is a part of SOA for business integration, which is an avenue of research. The research proposed by  [32]  addresses this integration by proposing a conceptual SOA model for achieving different levels of enterprise integration.
This study explores a new methodology called community-aided brand concept map (CA-BCM) for eliciting brand association networks using data collected from technology-enabled online communities. NVivo software is used to qualitatively code these data to 1) uncover how users associate themselves with a brand and 2) use content analysis to quantify each of these associations, leading to the elicitation of a comprehensive network of brand associations and their relationships. Using the example of a movie brand, the new tool CA-BCM effectively uncovers 1) the core, secondary, and tertiary brand associations and 2) classifies the brand associations into strong, favorable, and unique associations. Finally, 3) using these brand association classification data and the brand awareness level, customer brand equity is measured.
e. , without researcher's intervention so that biases can be minimized. A solution that meets the above-mentioned data-related requirements is user-generated data (UGD) or user comments, which are created by consumers when they participate in discussions in technology-enabled online communities. While UGC itself is a selfreport, it is created voluntarily by the users without any researcher or marketer influence or interference, and UGC exists irrespective of whether this data is used for marketing purposes or not. This voluntarity of UGC makes it unique and different from the involuntary or marketer/ researcher-induced self-reports which are often subjected to interviewer bias, measurement bias, etc. Brand associations are purely subjective, and such associations reside in the mind of consumers and include their opinions, feelings, preferences, attitude, attachments, dislikes, aspirations, etc.
For the purpose of explaining this new methodology, a movie-based online community is chosen. Thereafter, user comments are downloaded for any brand of interest (e. g. , the movie brand The Princess and the Frog) from the chosen community for which the brand association network needs to be elicited. The number of user comments generated varies from one brand to another (or movie to another) depending on whether it is a well-known brand (a strong brand) or not. The user comments for the chosen brand are then coded using NVivo software, and the coding process is guided by the branding theories and literature. It is important to note that each user comment may be composed of one or (mostly) several brand associations, and the current study considers all the associations that are present in each user comment and are coded using NVivo.
Thus, the current study takes a more in-depth look into the valence of each user comment. Once all plausible brand associations are identified through coding using NVivo software, the next step is to categorize these coded brand associations into strong or weak, favorable or unfavorable, and unique brand associations. Some of these associations are grouped under a larger construct to form the core brand associations (first-order brand associations). Any other associations that are attached directly to the core brand associations are classified as secondary brand associations, and similarly, tertiary brand associations are associations that are attached to the secondary associations. NVivo helps to display the 'diversity' (or the variety of brand associations), and according to  Keller (2013) , the strength of brand associations is determined by the diversity of brand associations.
, 2002; Punj, 2013; Risselada et al. , 2015; Schreier et al. , 2007). Online discussions related to upcoming and currently released movies create a time-series and cross-sectional archive of user-generated data that is easily accessible and publicly available  (Dhar and Chang, 2009; Jensen et al. , 2014; Khim-Yong et al. , 2013; Smith et al. , 2012). Prior studies have shown that in the movie sector, the behavior demonstrated in an online community is a good representation of market behavior  (Divakaran, 2012) ; hence, the current paper argues that the CA-BCM elicited in this study is a good representation of the mass market. This argument finds support in the current study, which is presented later in this paper. In this study, data were collected from only one movie-based online community.
As soon as the Walt Disney Pictures (a production studio) preannounced the upcoming movie "The Princess and the Frog", it created a lot of excitement among community members of www. clevver. com, who then started participating in online discussions not only during the movie's prerelease period but also throughout its theatrical run. Such online discussions leave behind a trail of user-generated data (UGD), which is collected and then analyzed using analytical coding software such as NVivo to identify consumers' brand associations. Each user comment is composed of one or several brand associations that are identified during the coding process and coded accordingly. Thus, this coding process investigates each user's comments qualitatively and more in-depth for their valence by identifying one or several brand associations from each user's comments, and this process is guided by the branding literature.
69), "The Road" (3. 34), and "Ninja Assassin" (3. 61). This external validity provided by the box office revenues and average rating provides support for the initial assumption made in this paper that the brand association network elicited using online community data represents the overall market. The CA-BCM methodology was further validated by two studies. In the first study (i. e. , Study 2A or Section 3. 1), customer brand equity is measured using the strength, favorability, and uniqueness of brand associations identified using the CA-BCM method and the brand awareness level for each movie. In the second study (i. e. , Study 2B or Section 3. 2), Pearson correlation is used to provide external validity to the CA-BCM technique by testing the relationship between customer brand equity and market performance (box office sales).
Data analysis Once the user comments are downloaded for each of the selected movies identified in Table  1a , the CA-BCM technique is then applied to elicit the different types of brand associations to determine their strength, favorability, and uniqueness by following the various steps illustrated in the previous section (i. e. , in Section 2). For each of these movies, the number of members who participated in the online discussion was taken as a measure of the relative awareness level of each movie in the online community. Data analysis was performed using the software solution QSR NVIVO 10, in which data were imported, structured, coded, and linked. Statistical software IBM SPSS statistics 27 is used to analyze the relationship between customer brand equity and box office sales by performing Pearson's correlational analysis (Table  2 ).
This study, however, minimized these issues by relying on user-generated data collected from online communities instead of using self-reports. Unlike self-reports, which are used in current BCM elicitation methods, user-generated data are unobtrusive, more elaborate, less time-consuming, and less costly to collect. Issues associated with self-reporting do not rise with user-generated data because researchers do not intervene or influence the creation of voluntarily contributed online postings. This is because UGC is publicly available and collected, and they are coded using NVIVO. While UGC itself is a self-report, it is created voluntarily by the users without any researcher or marketer influence or interference, and UGC exists irrespective of whether this data is used for marketing purposes or not.
In the CA-BCM method, coding of the user comments was done manually, which makes it easier to detect such ironic comments in the context of the ongoing discussions; however, this manual coding is helpful when the volume of UGC is not too large, unlike big data. In the future, with the availability of big data, this manual coding method can be replaced or supplemented by integrating automatic ironic processing methods that are recently identified in the literature  (Chia et al. , 2021; Farías et al. , 2020; Reyes and Rosso, 2012; Zhang et al. , 2019). Fourth, using CA-BCM method a brand map can be generated even before a product is launched in the market and before consumption, due to the dynamic nature of the data being used. Prerelease elicitation of brand association networks is applicable to products and industries where new-product announcements are prevalent, which helps to generate much excitement among potential users or customers who then share their opinions and feelings in online forums as user comments.
The interviewers asked the participants to use the app for as long as they wanted to learn more about the cereals. They did not urge the participants to buy the cereals. The participants were instructed to walk unaccompanied to the cereals shelf where we provided a tablet with the ARPI (about five meters from the entrance on the main floor, see the map of the hypermarket in Web-Appendix B). The ARPI's instructions and usage were, therefore, separated in time and space. The shoppers registered in the AR app with an anonymous code, ensuring that participants used the app only once. They logged in with their code and then used the ARPI to inform themselves about the target products in the absence of an interviewer. This ensured that they could do their shopping and use the ARPI at their own pace.
β = standardized coefficients. 1 0: male, 1: female, 2 0: no tablet ownership, 1: tablet ownership M afternoon =. 48), while on Saturdays the pattern is flipped (M morning =. 39 vs. M afternoon =. 13). Sample We gathered data of 271 shoppers who used the ARPI. For contrasting the ARPI to traditional means of informing consumers, we asked 153 additional shoppers to use a booklet providing the same information. Within both groups, we randomly assigned participants to one of the four treatments. 6 The exclusion of shoppers who used the ARPI app for less than three seconds resulted in 250 ARPI users. 7 The excluded participants do not differ from the sample in terms of gender (χ 2 (1) = 1. 28, p =. 258) or age (t(415) =. 24, p =. 814). The age of the participants ranged from 16 to 82 years, with a mean of 39.
08, ρ =. 88) and sustainability attitude (M = 4. 75, ρ =. 89). Table  11  in Appendix 5 provides the sources, wording of items, and psychometric properties. We gathered data of 310 U. S. consumers via Prolific. Their age ranged from 18 to 82 years, with a mean of 36. 8 years (SD = 12. 7); 41. 6% are male, 58. 1% female, and 0. 3% diverse.
77). Extending the main study's mediators, we added scales for presence (M = 4. 43, α =. 87) adopted from  Huang and Liao (2015) , novelty (M = 5. 26, α =. 82), utilitarian benefits (M = 4. 42, α =. 80) and hedonic benefit (M = 4. 23, α =. 92) taken from  Voss et al. (2003). As controls, we again measured the self-reported product category knowledge (M = 4. 75, ρ =. 84) and sustainability attitude (M = 5. 10, ρ =. 81). Usage time was tracked (M = 67. 47 s). Table  11  in Appendix 5 provides the sources, wording of items, and psychometric properties. We gathered data of 420 consumers via Prolific (M age = 30. 0 years, SD = 9. 9; 57. 9% of the participants are men, 41. 2% female, and 1. 0% diverse; 52. 6% have a university degree). Results First, we contrasted the four experimental conditions against the control group without a treatment  (group [5] ).
The price per bottle ranged from €0. 29 to €2. 79 for craft beer. Bottle sizes ranged from 0. 2 to 1. 0 l. The study covered a wide range of beer types, including non-alcoholic beer and beer-mixed drinks. We collected the data over three weeks. As in the main study, we balanced the treatments across daytime slots and weekdays. Interviewers approached shoppers in the hypermarket's beer section (opening claim: "Find out more about your beer!"). Most shoppers agreed to participate, and the interviewers briefly showed them the app's basic functionality; shoppers used the app without an interviewer present to ensure that the shoppers made their choice at their own pace. To determine the ARPI effect, we contrasted the ARPI users with the scanner data of the entire population, i. e. , all other consumers who bought beer during the time that we ran the study, as well as about one week before and two weeks later (n = 10,657).