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The purpose of this paper is to apply the Takagi-Sugeno (T-S) fuzzy model techniques in order to treat and classify textual data sets with and without noise. A comparative study is done in order to select the most accurate T-S algorithm in the textual data sets.

From a survey about what has been termed the “Tunisian Revolution,” the authors collect a textual data set from a questionnaire targeted at students. Five clustering algorithms are mainly applied: the Gath-Geva (G-G) algorithm, the modified G-G algorithm, the fuzzy c-means algorithm and the kernel fuzzy c-means algorithm. The authors examine the performances of the four clustering algorithms and select the most reliable one to cluster textual data.

The proposed methodology was to cluster textual data based on the T-S fuzzy model. On one hand, the results obtained using the T-S models are in the form of numerical relationships between selected keywords and the rest of words constituting a text. Consequently, it allows the authors to interpret these results not only qualitatively but also quantitatively. On the other hand, the proposed method is applied for clustering text taking into account the noise.

The originality comes from the fact that the authors validate some economical results based on textual data, even if they have not been written by experts in the linguistic fields. In addition, the results obtained in this study are easy and simple to interpret by the analysts.

The auditing and accounting profession must provide appropriate disclosure of the going concern status of an entity, especially when that status is threatened. Auditors have an obligation to consider the wider legal environment of an entity, including all relevant case law, when they perform any such audit. Despite this obligation, the auditing profession appears to violate important legal principles. The auditor’s approach to the going concern status of an entity is contained in the South African Auditing Standard, SAAS 570 “Going Concern”. The South African legal framework’s approach to this issue emerges from the Supreme Court case Philotex (Pty) Ltd v Snyman. This article explores the fundamental disagreement between the auditor’s approach to the going concern problem and that adopted in terms of the wider South African legal framework.

This study explores identity signaling used by an emerging class of knowledge celebrities in China – Knowledge Wanghong – who sell knowledge products on online platforms. Because identity signaling may involve constructing unique online identities and controlling over product-related and seller-related characteristics, the purpose of this study is two-fold: (1) to uncover different online identities of knowledge celebrities; and (2) to examine the extent to which the online identity type is associated with their product-related characteristics, seller-related characteristics and sales performance.

A unique data set was collected from a Chinese leading pay-for-knowledge platform – Zhihu – which featured the online profiles of tens of thousands of knowledge celebrities. Online identity types were derived from their self-edited content using Latent Dirichlet Allocation (LDA) topic modeling. Thereafter, their product-related characteristics, seller-related characteristics and respective sales performance were analyzed across different identity types using analysis of variance (ANOVA) and multiple-group linear regression.

Knowledge celebrities are clustered into four distinctive online identities: Mentor, Broker, Storyteller and Geek. Product-related characteristics, sell-related characteristics and sales performance varied across four different identities. Additionally, the online identity type moderated the relationships among their product-related characteristics, sell-related characteristics and sales performance.

As emerging-phenomenon-based research, this study extends related literature by using the notion of identity signaling to analyze a peculiar group of online celebrities who are setting an important trend in the pay-for-knowledge model in China.

This study aims to explore the effects of political information and anger on the public's cognitive processing and foreign policy preferences concerning third‐party interventions in ethnic conflict.

The study employs an experimental design, wherein the authors manipulate policy‐specific information by generating ad hoc political information related to ethnic conflict. The statistical methods of analysis are logistic regression and analysis of covariance.

The results demonstrate that both political information and anger have a significant impact on an individual's cognitive processing and policy preferences regarding ethnic conflict interventions. Specifically, political information increases one's proclivity to choose non‐military policy options, whereas anger instigates support for aggressive policies. Both factors result in faster decision making with lower amounts of information accessed. However, the interaction of political information and anger is not significant. The study also finds that policy‐specific information – rather than general political information – influences the public's policy preferences.

This study confronts and advances the debate over whether political information is significant in influencing the public's foreign policy preferences and, if so, whether such an effect is the product of general or domain‐specific information. It also addresses an under‐studied topic – the emotive repercussions of ethnic conflicts among potential third‐party interveners. In addition, it tackles the argument over whether political information immunizes people against (or sensitizes them to) the effects of anger on their cognitive processing and foreign policy preferences. The study also introduces a novel approach for examining political information through an experimental manipulation of policy‐specific information.

Patent offices and other stakeholders in the patent domain need to classify patent applications according to a standardized classification scheme. The purpose of this paper is to examine the novelty of an application it can then be compared to previously granted patents in the same class. Automatic classification would be highly beneficial, because of the large volume of patents and the domain-specific knowledge needed to accomplish this costly manual task. However, a challenge for the automation is patent-specific language use, such as special vocabulary and phrases.

To account for this language use, the authors present domain-specific pre-trained word embeddings for the patent domain. The authors train the model on a very large data set of more than 5m patents and evaluate it at the task of patent classification. To this end, the authors propose a deep learning approach based on gated recurrent units for automatic patent classification built on the trained word embeddings.

Experiments on a standardized evaluation data set show that the approach increases average precision for patent classification by 17 percent compared to state-of-the-art approaches. In this paper, the authors further investigate the model’s strengths and weaknesses. An extensive error analysis reveals that the learned embeddings indeed mirror patent-specific language use. The imbalanced training data and underrepresented classes are the most difficult remaining challenge.

The proposed approach fulfills the need for domain-specific word embeddings for downstream tasks in the patent domain, such as patent classification or patent analysis.

The purpose of this study is to explore the customer shopping experience in the innovative technology setting. Specifically, the purpose is to understand how do innovative technologies influence the customer shopping experience?

This qualitative, explorative study has characteristics of a phenomenological research strategy. The data were collected from four focus groups and ten in-depth interviews with consumers. Abductive approach with an implementation of content analysis was used as a method of analysis.

The results show that there are three critical factors in customer's shopping experience in the context of innovative technologies; (1) channel choice, (2) value dimensions related to convenience and enjoyment, and (3) social interaction. All factors are highly intertwined and influence each other.

This study contributes to customer experience literature by offering a framework for understanding customer shopping experiences in the innovative technology setting. These findings have important implications for retail managers seeking to enhance customer experience and achieve a competitive advantage by utilizing innovative technology.

The introduction of the book is aimed at providing the reader with a comprehensive analytical framework on the purpose and content of sustainable development analysis as a wicked problem in policymaking. The UN's 2030 Agenda is an ambitious and far-reaching initiative that encompasses 17 broad goals and 169 targets, which may be too general and potentially conflicting. Translating this agenda into practice is a challenging and possibly frustrating task that requires a pragmatic and methodologically structured approach. Accordingly, the introduction is organized around five key concepts that favor a translation into practice: the definition of problems and solutions, the policymaking of Sustainable Development Goals (SDGs), the wicked nature of policy problems in a sustainable development perspective, the specific kinds of capacity the policymakers must get to accomplish any task in the field of sustainable development, and, lastly, the type of policy design allocating ends and means for solving the problems. In particular, the theoretical framework supports the reader in understanding the wicked nature of sustainable development policies and the additional capacities policymakers must have in order to be able to design effective and coherent strategies. After a detailed presentation of each of the 12 chapters divided into two parts (six chapters in a section dedicated to the analysis of wicked sustainable development policies and six chapters dedicated to the analysis of the capacity of institutional instruments in resolving wickedness), the introduction anticipates the reader the rationale of the book.

The purpose of this paper is to develop a model for assessing the audit evidence of the going-concern (GC) assumptions underlying the preparation of financial statements.

This research analyses 678 audit opinions of Italian listed firms from 2007 to 2016 and uses a multiple linear discriminant analysis to create a GC score, which includes variables suggested by the international standards on auditing (ISA) 570 and by literature on GC.

The model provides three cut-off scores which can orient auditors towards issuing the most appropriate GC audit opinions (unmodified opinion, unmodified opinion, which includes emphases of matter, qualified opinion or disclaimer of opinion).

The development of the model is mainly based on public data and does not assess confidential information that is not disclosed in audit opinions.

This model can enable auditors to identify the most appropriate GC opinion and align auditor’s opinions in similar circumstances, thereby reducing their reliance on discretion and increasing the reliability of their judgement with a higher degree of accuracy. Moreover, this research lists additional events or conditions that may individually or collectively cast significant doubt on GC assumptions.

This study goes beyond the traditional decision-making process, apparently binary in nature, between “continuity” and “failure” or between “unmodified” and “modified” opinions. It is conceived to detect the different degrees of uncertainty that affect GC evaluations to orient auditors’ professional judgements.

Drawing inspiration from the organizational information processing theory, the technology acceptance model (TAM) and the theory of motivation, this study aims to examine the acceptance of private and public blockchain technology-based collaboration among supply chain practitioners.

A total of 257 samples were collected through a survey from supply chain practitioners. The study used parallel mediators of perceived usefulness (extrinsic motivation) and perceived ease of use (intrinsic motivation) to measure behavioral intention to use.

The results reveal that partial mediation exists between blockchain-based collaboration (private and public) and behavioral intention to use. For perceived usefulness, a stronger mediating effect was found between private blockchain-based collaboration and behavioral intention to use. For perceived ease of use, a stronger mediating effect was found between public blockchain-based collaboration and behavioral intention to use.

By integrating insights from the organizational information processing theory, the TAM and the theory of motivation, this study provides an in-depth understanding of how the distinct features of information processing in blockchain technology-based collaboration influence the supply chain practitioners’ to accept it. The novelty and results of the study expand the existing literature and pave the way for future research.