Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

The becoming of social media: the role of rating, ranking and performativity in organizational reputation-making

This thesis explores the concept of reputation-making with the aim of explaining how the rise of user-generated content websites has influenced organizational reputationmaking practices in the travel sector. The findings are based upon a corpus of data including: a field study at the offices of the largest travel user-generated website operator, TripAdvisor and an adaptation of virtual ethnography called “netnography”. Rating and ranking of hotels on social media websites has not only disturbed the established reputation-making practices of professionals in the travel sector and contributed to a significant redirection of reservation revenue but has performative consequences for tourist encounters. In other words, it is argued that if key assumptions underpinning the rating and ranking of travel change, the enactment of travel itself is reconfiguring and this has important implications for how reputationmaking occurs. The reconfigurations documented in the study are theorized using the lens of Process Theory. Originally inspired by philosophers such as Bergson and Whitehead and adopted in the work of organizational theorists such as Tsoukas, Chia, Langley, and Nayak, the choice of Process Theory to inform the conduct of this study resonates with key streams of existing reputation research that view it as a dynamic phenomenon. Core concepts within Process Theory, such as “becoming” enable further investigation into the precise nature of this dynamism by focusing on relations as always fluid and on the move. The challenge, even for literature that acknowledges phenomena as dynamic, is how to temporarily pause the flow for the purpose of analysis and thereby approach becoming without disturbing its inherent nature. This is taken up in the first analysis chapter which uses the notion of place to illustrate and analyze reputation-making using the process of becoming. The chapter argues the importance of recognizing the temporary pauses produced by rating and ranking mechanisms as generative rather than merely reductive algorithmically produced representations. In this way, we get closer to understanding the performativity of phenomena such as TripAdvisor and produce fundamental insights informing organizational reputation-making. It is argued that the organizational devices through which travellers’ engage with the places they visit are not only “making” reputations but are also making formative differences to the practice of travelling. In the second analysis chapter, a key issue associated with these changes - the intensification in focus on service – is explored further and in-depth examination of the field data is used to highlight ways in which TripAdvisor amplifies attention given to the specific characteristics of practices when they are performed. This provides evidence to ground Tsoukas and Chia’s (2002) proposal that organizational change is achieved through ‘microscopic changes’ thus reinforcing the processual nature of change. In so doing, key insights are generated to inform organizational reputation-making. Returning to the tenet of becoming in the third analysis chapter, the “circle of (il)legitimacy” embraces processual principles - for the nature of the circle is to have no beginning or end – but acknowledges the cumulative outcome of configuring practices for hoteliers through a discussion of key issues emerging in the travel sector. The relationship between reputation-making and legitimation is highlighted with examples of the additional processes through which reputation can now be made vulnerable within multiple jurisdictional contexts. The thesis concludes with the assertion that if we aim to understand the phenomenon of reputation-making, we have to develop a more nuanced and sophisticated way to conceptualize its formativeness. It is suggested that this extends beyond snap shot assessments or post-hoc crisis management to on-going maintenance of its emergence and development as well as processual changes across time and space

EXPLORING BRAND POSITIONING AND HOTEL PERSONA TROUGH WOM AND CONTENT BY TEXT ANALYSIS

Hotel management will concentrate on addressing difficult challenges and finding new ways for hotels to succeed in today's changing market world. Online customer reviews can be used to reflect the degree of differentiation between hotel brands and understanding the hotel industry's market structure through text analytics. The purpose of this study is to discover and demonstrate how customer online reviews (WOM and content), in the hospitality industry using text analysis can be used to explore brand positioning of hotel persona. This study gathered 4.215 online reviews one of hotel at Bandung city from year of 2015 – 2019, the methodology used the approach of text classification, quantitative analysis of text. This study found the category of visitors who stay the most are comes from family category and romantic vacation category, while the target visitors expected by the hotel are from business traveler’s category. Most customer state the word "family" when mentioning the hotel. Children, family, pool, zoo are the words most often discussed in customer reviews. This study findings can be used as an insight into what are the things that generate a satisfying experience and strengthen brand positioning of the hotel. In the future, it would be interesting to gathered data from multiple e-commerce application, and combining ontology-learning-based text mining and psychometric techniques to translate online hotel’s reviews into a hotel’s positioning map, capturing the relationship between product of hotel and reviewer effectively. Keywords: online review, brand positioning, text analytics, customer perceived value, hotel person

A social network approach in semantic web services selection using follow the leader behavior

Automatic discovery of web services is a crucial task for e-Business communities. Locating and selecting "the best" web service from a vast number of similar services that matches the user's requirements and preferences is a cognitive challenge and requires the use of an intelligent decision making framework. This paper develops a flexible ontological architecture and framework for Semantic Web Service Selection that exploits Goldbaum's innovative "Follow the Leader" model originally designed as an analytic tool for studying social network behavior and evolution. The framework proposes two new ontologies integrated in a recommender system, which guides a user to select the best service that matches their requirements and preferences. We test and evaluate several behaviors of market leader scenarios using a simulation agent. ©2009 IEEE

A Comparative Study of Sentiment Analysis Methods for Detecting Fake Reviews in E-Commerce

The popularity of the e-commerce system has increased, especially under the COVID scenario. Consumer product reviews from the past have had a significant impact on influencing consumers' purchasing decisions. Fake reviews—those written by humans and computers that engage in dishonest behavior—are consequently generated to increase product sales. The fake reviews hurt consumers and are dishonest. The goal of this research is to examine and evaluate the performance of various methods for identifying fake reviews. The well-known and widely-used Amazon Review Data (2018) dataset was used for this research. The first 10 product categories on Amazon.com with favorable feedback will be provided in the data section. After that, perform fundamental data preparation procedures such as special character trimming, bag of words, TF-IDF, etc. The models are trained to create a dataset for detecting fake reviews. This research compares the performance of four different models: GPT-2, NBSVM, BiLSTM, and RoBERTa. The hyperparameters of the models are also tuned to find the optimal values. The research concludes that the RoBERTa model performs the best overall, with an accuracy of 97%. GPT-2 has an overall accuracy of 82%, NBSVM has an overall accuracy of 95%, and BiLSTM has an overall accuracy of 92%. The research also calculates the Area Under the Curve (AUC) for each model and finds that RoBERTa has an AUC of 0.9976, NBSVM has an AUC of 0.9888, BiLSTM has an AUC of 0.9753, and GPT-2 has an AUC of 0.9226. It can be observed that the RoBERTa model has the highest AUC value, which is close to 1. Therefore, it can be concluded that this model provides the most accurate prediction for detecting fake reviews, which is the main focus of this research. Doi: 10.28991/HIJ-2023-04-02-08 Full Text: PD