A double interaction-based financing group decisionmaking framework considering uncertain information and inconsistent assessment

Financing group decision-making (FGDM), which is an important stage of project financing, has unique characteristics: large investments and long payback horizons. Its evaluation results are likely to be distorted if we ignore the uncertain information and inconsistent assessment during the decision-making process. In this study, we propose a double interaction-based FGDM framework under uncertain information and inconsistent assessment. We modify the weight setting of evidence reasoning and aggregation method of probabilistic linguistic term sets to process the above two issues. The proposed framework is applied in a detailed case study analysis to display its effectiveness and stability. We expect the double interaction-based group decision-making framework under uncertain information and inconsistent assessment to be a useful tool to understand FGDM processes

A multidimensional decision with nested probabilistic linguistic term sets and its application in corporate investment

With the rapid development of information, decision making problems in various fields have presented multidimensional, complex and uncertain characteristics. Nested probabilistic-numerical linguistic term set (NPNLTS) is an effective tool to describe complex information due to the nested structure and diverse variables. This paper extends the concept of NPNLTS, and defines an improved form, i.e., nested probabilistic linguistic term set (NPLTS), and then proposes a novel VIKOR method with nested probabilistic linguistic information to solve the model. Within the context of empirical corporate finance, a case study related to corporate investment decision is presented and handled by the novel VIKOR method. After that, comparative analysis is carried out considering other decision-making methods, decision coefficient in VIKOR, and weights of attributes. As a result, the proposed method not only provides a rational and effective solution, but also reveals the rule in the case when decision coefficient and weights of attributes change, respectively. Finally, we discuss the proposed method from the theoretical and application aspects with a view to guiding future research. To a certain extent, this study provides a new decision environment to deal with multidimensional problems

Wasserstein distance-based probabilistic linguistic TODIM method with application to the evaluation of sustainable rural tourism potential

The evaluation of sustainable rural tourism potential is a key work in sustainable rural tourism development. Due to the complexity of the rural tourism development situation and the limited cognition of people, most of the assessment problems for sustainable rural tourism potential are highly uncertain, which brings challenges to the characterisation and measurement of evaluation information. Besides, decision-makers (DMs) usually do not exhibit complete rationality in the practical evaluation process. To tackle such problems, this paper proposes a new behaviour multi-attribute group decision-making (MAGDM) method with probabilistic linguistic terms sets (PLTSs) by integrating Wasserstein distance measure into TODIM (an acronym in Portuguese of interactive and multicriteria decision making) method. Firstly, a new Wasserstein-based distance measure with PLTSs is defined, and some properties of the proposed distance are developed. Secondly, based on the correlation coefficient among attributes and standard deviation of each attribute, an attribute weight determination method (called PL-CRITIC method) is proposed. Subsequently, a Wasserstein distance-based probabilistic linguistic TODIM method is developed. Finally, the proposed method is applied to the evaluation of sustainable rural tourism potential, along with sensitivity and comparative analyses, as a means of illustrating the effectiveness and advantages of the new method

Financial information extraction using pre-defined and user-definable templates in the Lolita system

Financial operators have today access to an extremely large amount of data, both quantitative and qualitative, real-time or historical and can use this information to support their decision-making process. Quantitative data are largely processed by automatic computer programs, often based on artificial intelligence techniques, that produce quantitative analysis, such as historical price analysis or technical analysis of price behaviour. Differently, little progress has been made in the processing of qualitative data, which mainly consists of financial news articles from financial newspapers or on-line news providers. As a result the financial market players are overloaded with qualitative information which is potentially extremely useful but, due to the lack of time, is often ignored. The goal of this work is to reduce the qualitative data-overload of the financial operators. The research involves the identification of the information in the source financial articles which is relevant for the financial operators' investment decision making process and to implement the associated templates in the LOLITA system. The system should process a large number of source articles and extract specific templates according to the relevant information located in the source articles. The project also involves the design and implementation in LOLITA of a user- definable template interface for allowing the users to easily design new templates using sentences in natural language. This allows user-defined information extraction from source texts. This differs from most of existing information extraction systems which require the developers to code the templates directly in the system. The results of the research have shown that the system performed well in the extraction of financial templates from source articles which would allow the financial operator to reduce his qualitative data-overload. The results have also shown that the user-definable template interface is a viable approach to user-defined information extraction. A trade-off has been identified between the ease of use of the user-definable template interface and the loss of performance compared to hand- coded templates

Towards an interaction-based approach to entrepreneurship : Understanding the co-creation of new value

The co-creation of new value requires entrepreneurs to have insight into a new direction that might turn out to meet desires or needs that could not have been known before. Yet, entrepreneurs are just the beginning, because the co-creation of new value depends on the consumer environment. For entrepreneurs this means a request to interact with contemporary consumers who pursue new consumption experiences. Accordingly, it is evident that entrepreneurs should have a clear understanding of consumers and their social contexts, because collaboration between entrepreneurs and consumers has become the core of business. However, entrepreneurship scholarship has thus far paid only cursory attention to consumers, and scholarly interest has largely neglected the interactions between entrepreneurs and consumers. Unfortunately, this has led to a limited understanding of the essence of entrepreneurship, and, thus, to a limited understanding of where the new value truly emerges from. Therefore, the main aim of this dissertation is to suggest an interaction-based approach to entrepreneurship research. While conducting this research, I focus on the complex phenomenon of the co-creation of new value. I elaborate a theoretical framework of the co-creation of new value by synthesizing different theoretical debates. Using this theoretical framework, I provide novel insights into decision making, action and context, the key elements that must be taken into account to comprehensively understand the complex and dynamic co-creation of new value. Furthermore, this dissertation empirically provides some abstractions of reality to illuminate some new insights on whence new value truly emerges and how it is co-created. Based on the acquired theoretical knowledge and empirical studies, I have summarized my key findings into three subpropositions. First, I argue that when aiming to co-create new value, entrepreneurs capture relevant knowledge about their consumers by making sense of the multilayered consumer environment. Second, I claim that interaction practices, which involve multiple actors, construct legitimacy that at times enables and at others constrains entrepreneurial efforts and the co-creation of new value. Third, I state that consumers constitute the multilayered consumer environment that works as a context for the co-creation of new value by situating themselves in relation to the social environment and their situational self. These three subpropositions collectively illustrate that the co-creation of new value is a highly interactive event. Therefore, my main proposition, which answers the main research question and fulfills the main aim of this dissertation, is that, when co-creating new value, entrepreneurs can tap into the consumer environment by adjusting their sensemaking, judgment, and practices for the socially situated interplay of decision making, action, and context. Overall, I believe that, with this dissertation, I have been able to gain new insights on whence new value truly emerges and how it is co-created. Furthermore, with this dissertation I also foster some novel ways to break away from the process perspective and to capture time-sensitive descriptors of ongoing actions and the new value that is pursued. Thus, I consider that my propositions bend some boundaries of the existing entrepreneurship research and make some important contributions to the field of entrepreneurship. Moreover, I am certain that my findings provide some topical and practical knowledge for entrepreneurs and entrepreneurially minded managers, the advisers within the institutions who support entrepreneurs, and also for entrepreneurial education

Business-oriented Analysis of a Social Network of University Students

Despites the great interest caused by social networks in Business Science, their analysis is rarely performed both in a global and systematic way in this field: most authors focus on parts of the studied network, or on a few nodes considered individually. This could be explained by the fact that practical extraction of social networks is a difficult and costly task, since the specific relational data it requires are often difficult to access and thereby expensive. One may ask if equivalent information could be extracted from less expensive individual data, i.e. data concerning single individuals instead of several ones. In this work, we try to tackle this problem through group detection. We gather both types of data from a population of students, and estimate groups separately using individual and relational data, leading to sets of clusters and communities, respectively. We found out there is no strong overlapping between them, meaning both types of data do not convey the same information in this specific context, and can therefore be considered as complementary. However, a link, even if weak, exists and appears when we identify the most discriminant attributes relatively to the communities. Implications in Business Science include community prediction using individual data.Social Networks; Business Science; Cluster Analysis; Community Detection; Community Comparison; Individual Data; Relational Data

Observing Users - Designing clarity a case study on the user-centred design of a cross-language information retrieval system

This paper presents a case study of the development of an interface to a novel and complex form of document retrieval: searching for texts written in foreign languages based on native language queries. Although the underlying technology for achieving such a search is relatively well understood, the appropriate interface design is not. A study involving users (with such searching needs) from the start of the design process is described covering initial examination of user needs and tasks; preliminary design and testing of interface components; building, testing, and further refining an interface; before finally conducting usability tests of the system. Lessons are learned at every stage of the process leading to a much more informed view of how such an interface should be built

Discovering the value of unstructured data in business settings

With the increasing amount of unstructured data in business settings, the analysis of unstructured data is reshaping business practices in many industries. The implementation of unstructured data analysis will eventually have dominant presence in all department of organisations thus contributing to the organisations. This dissertation focuses the most widely utilised unstructured data-textual data within the organisation. A variety of techniques has been applied in three studies to discover the information within the unstructured textual data. Study I proposed a dynamic model that incorporates values from topic membership, an outcome variable from Latent Dirichlet Allocation (a probabilistic topic model), with sentiment analysis for rating prediction. A variety of machine learning algorithms are employed to validate the model. Study II focused on the exploration of online reviews from customers in the OFD domain. In addition, this study examines the outcomes of franchising in the service sector from the customer’s perspective. This study identifies key issues during the processes of producing and delivering product/services from service providers to customers in service industries using a large-scale dataset. Study III extends the data scope to the firm-level data. Latent signals are discovered from companies’ self-descriptions. In addition, the association between the signals and the organisation context of the entrepreneurship is also examined, which could display the heterogeneity of various signals across different organisation context

Green supplier selection based on CODAS method in probabilistic uncertain linguistic environment

Probabilistic uncertain linguistic sets (PULTSs) have widely been used in MADM or MAGDM. The CODAS method, which is a novel MADM or MAGDM tool, aims to acquire the optimal choice which have the largest Euclidean & Hamming distances from the NIS. This paper designs the probabilistic uncertain linguistic CODAS (PUL-CODAS) method with sine entropy weight. Finally, a numerical example for green supplier selection is given and the obtained results are compared with some existing models. First published online 05 February 202