Text mining and dimension reduction method application into exploring isomorphic pressures in corporate communication on textual tweet data about sustainability in the energy sector

The study analyses the isomorphism pressures within the context of sustainability by exploring the Twitter communication in the energy sector. Recently, there can be observed the increasing focus on interactive and communicative construction of an institution to understand how the organizations sustain the institutional pressures. The rhetorical commitments that create narrative dynamics in organizational communication are central to institutional diffusion and change. Social Media, Twitter, in particular, has been demonstrated as the new opportunity to explore the linguistic dimension in corporate communications. We propose the use of Social Media linguistic data (tweets with their hashtags and keywords) and the triangulated method (text mining, web mining, and linguistic and content analysis) to examine the tweets´ trends in each company. Based on the institutional theory of organizational communication, the paper examines the relation between the idea of sustainability and isomorphism that leads to the adoption of similar models and attitudes among the organizations. It applies the text mining and correspondence methods within the R software. The energy sector tweets in English (from 2016) were treated by the text mining processes of the statistical linguistic analysis in the R tool. Text mining, involving the linguistic, statistical, and the machine learning techniques reveals and visualizes the latent structures of the content in an unstructured or weakly structured text data in a given collection of documents.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Change Mining in Adaptive Process Management Systems

The wide-spread adoption of process-aware information systems has resulted in a bulk of computerized information about real-world processes. This data can be utilized for process performance analysis as well as for process improvement. In this context process mining offers promising perspectives. So far, existing mining techniques have been applied to operational processes, i.e., knowledge is extracted from execution logs (process discovery), or execution logs are compared with some a-priori process model (conformance checking). However, execution logs only constitute one kind of data gathered during process enactment. In particular, adaptive processes provide additional information about process changes (e.g., ad-hoc changes of single process instances) which can be used to enable organizational learning. In this paper we present an approach for mining change logs in adaptive process management systems. The change process discovered through process mining provides an aggregated overview of all changes that happened so far. This, in turn, can serve as basis for all kinds of process improvement actions, e.g., it may trigger process redesign or better control mechanisms

How to improve business process performance using process mining

Due to the increased use of information systems by organizations to support process execution, detailed information on the implementation of business processes is being recorded. This fact enables using process mining projects as a powerful tool for improving performance. Process Mining is a relative young research discipline that sits between data science on the one hand and process modelling and analysis on the other hand. Process mining allows gaining knowledge of the organization’s actual business processes by extracting data from existing information systems mediums such as event logs, transaction logs etc. The purpose of this presentation is to demonstrate how a process for conducting process mining projects was designed, developed and applied in some organizational units. The process was implemented through nine research steps, inspired by the V-model, where elements on the right-hand side aim to answer questions presented in steps on the left-hand side. In the first two steps, the research problem and the research objectives were defined. A literature review was performed in step 3. In the fourth step, requirements for the process were identified and implemented. In step 5, a running example was carried out to test the process. Verification and validation of the process were performed in step 6 and step 7. Step 8 covered the discussion of results. The last step concludes the research, including checking if the research problem was solved. Organizations seeking for performance improvement can now benefit of a process that explicitly states which process mining tools, techniques and algorithms to be used in process mining projects

Discovering Organizational Correlations from Twitter

Organizational relationships are usually very complex in real life. It is difficult or impossible to directly measure such correlations among different organizations, because important information is usually not publicly available (e.g., the correlations of terrorist organizations). Nowadays, an increasing amount of organizational information can be posted online by individuals and spread instantly through Twitter. Such information can be crucial for detecting organizational correlations. In this paper, we study the problem of discovering correlations among organizations from Twitter. Mining organizational correlations is a very challenging task due to the following reasons: a) Data in Twitter occurs as large volumes of mixed information. The most relevant information about organizations is often buried. Thus, the organizational correlations can be scattered in multiple places, represented by different forms; b) Making use of information from Twitter collectively and judiciously is difficult because of the multiple representations of organizational correlations that are extracted. In order to address these issues, we propose multi-CG (multiple Correlation Graphs based model), an unsupervised framework that can learn a consensus of correlations among organizations based on multiple representations extracted from Twitter, which is more accurate and robust than correlations based on a single representation. Empirical study shows that the consensus graph extracted from Twitter can capture the organizational correlations effectively.Comment: 11 pages, 4 figure

Influence of Firms’ Staff and Skills on the Organizational Performance: A Case of the Salt Mining Industry in Tanzania

This study examined the influence of firms’ staff and firms’ skills on the organizational performance of salt mining industry in Coast Region of Tanzania where four salt companies were involved in the study. The study was quantitative, employing a survey design with a sample size of 100 employees obtained conveniently from a pool of 1010 employees from the four selected salt mining companies. The study employed primary data obtained through questionnaire distributed to the sampled respondents. Data was analysed descriptively and with inferential statistics with the help of SPSS to generate frequency tables and multiple regression analysis output. The findings of the study revealed that firms’ staff and firms’ skills influenced the organizational performance of salt mining industry. The study recommends to policy makers and managers of companies in the salt mining industry to reinforce policies, rules and regulations that will ensure low personnel turnover rate. They should appointment employees from diverse backgrounds, recruitment of skilled employees and provide opportunities for advancement. The study also recommends that policy makers should make sure that at organizational level, salt mining companies have career development plans that will enhance skills to employees

The use of big data and data mining in the investigation of criminal offences

The aim of this study was to determine the features and prospects of using Big Data and Data Mining in criminal proceedings. The research involved the methods of a systematic approach, descriptive analysis, systematic sampling, formal legal approach and forecasting. The object of using Big Data and Data Mining are various crimes, the common features of which are the seriousness and complexity of the investigation. The common tools of Big Data and Data Mining in crime investigation and crime forecasting as interrelated tasks were identified. The creation of databases is the result of the processing of data sources by Data Mining methods, each being distinguished by the specifics of use. The main risks of implementing Big Data and Data Mining are violations of human rights and freedoms. Improving the use of Big Data and Data Mining requires standardization of procedures with strict adherence to the fundamental ethical, organizational and procedural rules. The use of Big Data and Data Mining is a forensic innovation in the investigation of serious crimes and the creation of an evidence base for criminal justice. The prospects for widespread use of these methods involve the standardization of procedures based on ethical, organizational and procedural principles. It is appropriate to outline these procedures in framework practical recommendations, emphasizing the responsibility of officials in case of violation of the specified principles. The area of further research is the improvement of innovative technologies and legal regulation of their application

The Influence of Company Strategy towards Enterprise Risk Management, Organizational Culture, Supply Chain Management and Company Performance

This research was focused on Company Strategy as the exogenous variables, Enterprise Risk Management, Organizational Culture, Supply Chain Management and Company Performance as the endogenous variables on Coal Mining Companies at East and South Kalimantan. This research used explanatory research, by using the Enterprise Risk Management instruments from KPMG Australia (2001), strategy quality instrument from Tilles (1983), Organizational Culture instruments from Hofstede (1994), and Supply Chain Management instrument from Partiwi (2009). ROA and ROE of the coal mining companies were collected from a public company as a secondary data. The effect of those variables on five coal mining companies in East and South Kalimantan was tested by Partial Least Square (PLS). The total samples were 20 out of 96 coal mining companies in East and South Kalimantan Province. The research Showed that Company Strategy has a significant effect on Enterprise Risk Management, Company Strategy has a significant effect on Organizational Culture, Company Strategy has an insignificant effect on Supply Chain Management, Company Strategy has a significant effect on Company Performance, Enterprise Risk Management has a significant effect on Supply Chain Management, Enterprise Risk Management has a significant effect on Company Performance, Organizational Culture has a significant effect on Supply Chain Management, Organizational Culture has a significant effect on Company Performance, and Supply Chain Management has an insignificant effect on Company Performance. Based on the limited research, it is found that: (1) model fit is not sufficient because lack of data collected and (2) business environment on ROA and ROE of the coal mining companies was not analyzed. The world coal demands declined in 2008 and 2012, and this has made the company performance declining at those periods. In conclusion, this research showed that empirical studies support the importance of Company Strategy, Enterprise Risk Management, Organizational Culture, Supply Chain Management and Company Performance on Coal Mining Companies at East and South Kalimantan. Keywords: Company Strategy, Enterprise Risk Management, Organizational Culture, Supply Chain Management and Company Performanc

Applications and Challenges of Task Mining: A Literature Review

Task mining is a technological innovation that combines current developments in process mining and data mining. Using task mining, the interactions of workers with their workstations can be recorded, processed, and linked with the business data of the organization. The approach can provide a holistic picture of the business processes and related tasks. Currently, there is no overview of application scenarios and the challenges of task mining. In our work, we reflect application scenarios as well as technological, legal, and organizational challenges of task mining using a structured literature review. The application areas include discovery of automation potentials, monitoring, as well as optimization of business processes. The challenges include the cleansing, collection, data protection, explainability, merging, organization, processing, and segmentation of task mining data

Process mining meets GDPR compliance:the right to be forgotten as a use case

In a bid to ensure privacy of personal data of data subjects, the General Data Protection Regulation(GDPR) entails stringent obligations on organizations and businesses qualifying as data controllers and data processors. The regulation additionally bestow data subjects certain rights over their personal data, right to be forgotten generally being perceived the landmark. Fulfilling the GDPR’s obligatory requirements and provisioning of the data subject’s rights implicates considerable changes to the existing (pre-GDPR era) business and organizational processes. Being a non-trivial task, several technical as well as procedural challenges are associated. The case for organizations having intertwined or cascaded business processes and business processes stretched over multiple organizations is even more complicated. Process mining discipline has been found highly effective in automatically discovering, conformance/compliance analysis, and enhancement of business processes, organizational workflows, healthcare procedures/guidelines to name a few. Process mining techniques therefore have a great potential to assist and guide the transformation of pre-GDPR era (presumably GDPR non-compliant) business or organizational processes into GDPR-compliant processes, and afterwards monitor the compliance during execution. In addition to the current state of the art offline process mining techniques, stable online conformance checking and online model repair techniques needs to be developed for ensuring compliance to the regulation. We are highlighting the challenges associated with implementation of the right to be forgotten, and the GDPR in general.</p