Unpacking the relationship between environmental profile and financial profile; literature review toward methodological best practice

Despite a large body of empirical studies examining the relationship between Environmental Profile (EP) and Financial Profile (FP), the results are still inconsistent. The majority of research exploring EP-FP relationships have focused on the results and overlooked the precedence of data in the process. To understand where the field currently stands, the purpose of this paper is twofold. Firstly, it offers a comprehensive definition and conceptualisation of both EP and FP as both are multidimensional constructs. Secondly, it identifies several critical issues in theorising and testing these relationships for which current practice falls short. These focus on construct clarity by highlighting the inconsistency in measuring constructs, sampling errors, different research design, and sample characteristics. This paper analyses 98 empirical studies using data as the key element in each study, and to outline what is known and unknown about the EP-FP relationship from methodological points of view. The outcomes then made related to the comparability and replicability of studies. The results indicate how methodological artefacts such as characteristics of data sources and a data sample, data quality, data collection method as well as analysis method directly influence the results. In addition, comparability and replicability of results are strongly influenced by data source and data type. Moreover, the findings of this review reveal that the main analysis trend is the impact of EP on FP, particularly examining the linear relationship between environmental performance-output-based and accounting-based variables as EP and FP measurement respectively

The Reason behind the Rating: Text Mining of Online Hotel Reviews

Online customer reviews are a significant marketing resource for products and service business, often serving as a key reference point for potential customers. This study analyses a set of hotel reviews to find out what aspects of hotel experience are taken into consideration by customers when rating a hotel. We use text mining of hotel reviews to identify main features referred to by customers and determine their correlation with customer satisfaction or dissatisfaction. Our data set comprises reviews of ten hotels located in the vicinity of "Khao San Road" in the centre of Bangkok in Thailand, a popular holiday destination known as the centre of the "backpacking universe". The results highlight several main aspects affecting customer satisfaction, with friendly and helpful staff being the most influential one, whilst negative experiences with complementary services provided by the hotel, such as pool and Wi-Fi have the strongest impact on customer dissatisfaction. Other factors impacting customer satisfaction include cleanliness, room and bathroom interior, sleep quality and location. Our findings can be used to design effective feedback gathering and social media monitoring systems, and they also underpin a set of managerial implications regarding managing and marketing customer experiences in the hotel industry

Tangential Touch between the Free and the Fixed Boundary in a Semilinear Free Boundary Problem in Two Dimensions

The main result of this paper concerns the behavior of a free boundary arising from a minimization problem, close to the fixed boundary in two dimensions

Social Network Evolution:The Case of UK Companies Before and After Brexit

The Brexit referendum has impacted both the UK and the EU economies in several ways. The uncertainty around Brexit highlighted the importance of a relationships network between directors of companies to access information and resources that are necessary for optimal decision making. It is difficult to develop informed business and economy policies without a deep understanding of the magnitude of Brexit on business-to-business relationships with EU-based firms. This study aims to analyze the impact of the passage of the Brexit referendum on the evolution of board interlock networks. The study uses network analysis to measure the evolution of UK-EU directors’ relationships over the Brexit period, predominantly between the 2010 and 2020 period. The study models the structural changes in dynamic networks by converting this evolving network into static graphs on yearly basis. The analysis indicates that links formation in the UK is affected negatively by the Brexit referendum. It also has a negative impact on forming a new link with potential companies’ directors in the EU, but it shows a rising tendency for shared affiliation bias analysis. Interestingly, the contradicted trend in 2007, the number of directors’ connection in consumer service and food & drug sectors was decreasing in the UK while rocketing in the EU

Now and Next: A Maturity Model to Guide Analytics Growth

Maturity models concerning the analytics function within organisations cover several characteristics such as data, leadership support, processes, data management, governance, technology and people. Existing models, however, focus on diagnosis rather than guiding future developments, and overlook the importance of IT/Analytics-Business alignment. This paper presents a maturity framework which addresses these two shortcomings and distinguishes between two aspects of maturity – a “state” aspect which is used to access the present situation in the company, and a “management” aspect which analyses existing processes to establish the next stage of the company’s growth in the analytics area. The framework’s utility has been demonstrated through obtaining feedback from a number of managers, who have praised the ability of the framework to integrate diagnosis of the current situation with guidance on the next steps necessary to develop analytics maturit

Guiding the integration of analytics in business operations through a maturity framework

The analytics function is growing in importance as the digitisation of business operations and markets leads to the generation of ever-increasing amounts of data. Analysing this data in a manner aligned with company priorities and structures can generate value through supporting effective decision-making, rapid product innovation, supply chain visibility and other aspects of intra- and inter-company operations. To guide the growth we derive a novel maturity framework focused on driving the Analytics-Business alignment, covering a number of diverse organisational facets such as data, leadership support, processes, data management, governance, technology and people. It differentiates itself by using a firm theoretical foundation and providing guidance for analytics capability development instead of simply diagnosing the existing maturity level. To guide development, it distinguishes between two aspects of maturity – a “state” aspect, which is used to assess the present situation in an organisation, and a “management” aspect, which evaluates management attitude in order to establish the next stage of analytics growth. The framework has been implemented in a web-based tool and its utility has been demonstrated by obtaining feedback from 64 managers from a variety of sectors, who have praised its ability to integrate diagnosis of the current situation with guidance on the next steps necessary to develop analytics maturity

Features in extractive supervised single-document summarization: case of Persian news

Text summarization has been one of the most challenging areas of research in NLP. Much effort has been made to overcome this challenge by using either abstractive or extractive methods. Extractive methods are preferable due to their simplicity compared with the more elaborate abstractive methods. In extractive supervised single document approaches, the system will not generate sentences. Instead, via supervised learning, it learns how to score sentences within the document based on some textual features and subsequently selects those with the highest rank. Therefore, the core objective is ranking, which enormously depends on the document structure and context. These dependencies have been unnoticed by many state-of-the-art solutions. In this work, document-related features such as topic and relative length are integrated into the vectors of every sentence to enhance the quality of summaries. Our experiment results show that the system takes contextual and structural patterns into account, which will increase the precision of the learned model. Consequently, our method will produce more comprehensive and concise summaries