A systemic and cybernetic perspective on causality, big data and social networks in tourism

Kybernetes ◽  
2019 ◽  
Vol 48 (2) ◽  
pp. 287-297
Author(s):  
Miguel Lloret-Climent ◽  
Andrés Montoyo ◽  
Yoan Gutierrez ◽  
Rafael Muñoz Guillena ◽  
Kristian Alonso
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Real Data ◽  
Current Data ◽  
Set Covering ◽  
Invariant Sets ◽  
Content Type ◽  
Tourist Flows ◽  
Relevant Variables ◽  
Orbit Analysis

PurposeThe purpose of this paper is to propose a mathematical model to determine invariant sets, set covering, orbits and, in particular, attractors in the set of tourism variables. Analysis was carried out based on an algorithm and applying an interpretation of chaos theory developed in the context of General Systems Theory and Big Data.Design/methodology/approachTourism is one of the most digitalized sectors of the economy, and social networks are an important source of data for information gathering. However, the high levels of redundant information on the Web and the appearance of contradictory opinions and facts produce undesirable effects that must be cross-checked against real data. This paper sets out the causal relationships associated with tourist flows to enable the formulation of appropriate strategies.FindingsThe results can be applied to numerous cases, for example, in the analysis of tourist flows, these findings can be used to determine whether the behaviour of certain groups affects that of other groups, as well as analysing tourist behaviour in terms of the most relevant variables.Originality/valueThe technique presented here breaks with the usual treatment of the tourism topics. Unlike statistical analyses that merely provide information on current data, the authors use orbit analysis to forecast, if attractors are found, the behaviour of tourist variables in the immediate future.

Rethinking algorithmic regulation

Kybernetes ◽  
2015 ◽  
Vol 44 (6/7) ◽  
pp. 1005-1019 ◽  
Author(s):  
Eden Medina
Keyword(s):  
Big Data ◽  
Computer Network ◽  
Thought Experiment ◽  
Current Data ◽  
Military Coup ◽  
Historical Case Study ◽  
Historical Case ◽  
Content Type ◽  
Original Argument ◽  
Formed Part

Purpose – The history of cybernetics holds important lessons for how we approach present-day problems in such areas as algorithmic regulation and big data. The purpose of this paper is to position Project Cybersyn as a historical form of algorithmic regulation and use this historical case study as a thought experiment for thinking about ways to improve discussions of algorithmic regulation and big data today. Design/methodology/approach – The paper draws from the author’s extensive research on Cybersyn’s history to build an argument for how cybernetic history can enrich current discussions on algorithmic regulation and the use of big data for governance. Findings – The paper identifies five lessons from the Cybersyn history that point to current data challenges and suggests a way forward. These lessons are: first, the state matters; second, older technologies have value; third, privacy protection prevents abuse and preserves human freedom; fourth, algorithmic transparency is important; and finally, thinking in terms of socio-technical systems instead of technology fixes results in better uses of technology. Research limitations/implications – Project Cybersyn was a computer network built by the socialist government of Salvador Allende under the supervision of the British cybernetician Stafford Beer. It formed part of the government’s program for economic nationalization. Work on the project ended when a military coup brought the Allende government to an early end on September 11, 1973. Since we do not know how the system would have functioned in the long term, parts of the argument are necessarily speculative. Practical implications – The paper uses Cybersyn’s history to suggest ways that the Chilean experience with cybernetic thinking might enhance, improve, and highlight shortcomings in current discussions of algorithmic regulation. Originality/value – The paper provides an original argument that connects one of the most ambitious cybernetic projects in history to present day technological challenges in the area of algorithmic regulation.

Increase in prices of farmland in the Czech Republic

2017 ◽  
Vol 35 (3) ◽  
pp. 326-338 ◽  
Author(s):  
Lucie Severová ◽  
Roman Svoboda ◽  
Lenka Kopecká
Keyword(s):  
Czech Republic ◽  
Agricultural Sector ◽  
Practical Implication ◽  
Market Price ◽  
Real Data ◽  
Current Data ◽  
The Czech Republic ◽  
Content Type ◽  
Eu Member States ◽  
Farmland Prices

Purpose The purpose of this paper is to express the development of market price of farmland in the CR and to describe causes and effects of these changes in the price on the ownership of land in the country. Design/methodology/approach Primarily, description methods (especially for describing the creation of investment farmland funds) and a comparative analysis (in the case of an indicator of the farmland prices in the Czech Republic) were used in this paper. Findings The findings show that the situation in the Czech agricultural sector has improved particularly due to increasing subsidies; non-agricultural subjects are showing increased interest and banks are changing their approach to granting loans for the purchase of farmland. The market price of farmland in the Czech Republic has been rising; in 2015, it exceeded CZK162,500 per hectare on average. However, it is still low compared to the old EU member states. Practical implications Practical implication of the paper consists in comparing market prices of farmland with prices at which state sold farmland through PRGLF. Plot owners are often approached about the sale or lease of farmland. However, there are many speculators among those interested in buying, who often focus on those owners who have no idea regarding the value of their plots. Originality/value In the paper, the model of farmland prices was newly used and applied to real data in the Czech Republic from 2004 to 2015. Moreover, current data on investments to farmland were acquired, and an analysis which could be useful for foreign investors was elaborated.

Intention to participate in online commercial experiments by social network’s users

2016 ◽  
Vol 39 (4) ◽  
pp. 378-398 ◽  
Author(s):  
Rebeca Cordero-Gutiérrez ◽  
Libia Santos-Requejo
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Point Of View ◽  
Data Sets ◽  
Content Type ◽  
Gender And Age ◽  
The Social ◽  
Intention To Participate ◽  
Relevant Variables ◽  
Marketing Tool

Purpose The purpose of this paper is to analyze the underlying differences in the intention to participate in online commercial experiments through the social network considering users’ gender and age. Design/methodology/approach The model of this paper uses two relevant variables, trust and attitude, to test the behavioral intention. There were 269 data sets from social network’s users. Factorial analysis and linear regression were used in the analysis of the data obtained to investigate the differences in gender and age in the intention to participate in online commercial experiments. Findings The results of this paper show that there exist differences among women and men, and among youthers and adults. Women and youthers are the most desirable groups to conduct commercial experiments through social networks. Research limitations/implications From the point of view of the academics, this paper increases the knowledge of social network’s possibilities as a marketing tool. Practical implications This study and its conclusions are relevant for entrepreneurs in any field who want to reach their customers through a horizontal social network because they can improve the online experiments’ profit. Entrepreneurs can know and understand their customers better, taking into account their wishes, tastes and interests through when participating in a commercial experiment. Originality/value This paper describes the possibilities that social networks like Facebook offer entrepreneurs to know the intention of users to participate in an online commercial experiment. Moreover, the differences in gender and age allow in adapting the contents of the online commercial experiments to get better results. In addition, this research contributes to the investigation in the possibilities of social networks as marketing tools.

Index of suicide risk in Mexico using Twitter

2019 ◽  
pp. 1-13
Author(s):  
Luz Judith Rodríguez-Esparza ◽  
Diana Barraza-Barraza ◽  
Jesús Salazar-Ibarra ◽  
Rafael Gerardo Vargas-Pasaye
Keyword(s):  
Social Networks ◽  
Analytic Hierarchy Process ◽  
Suicide Risk ◽  
Opinion Mining ◽  
Real Data ◽  
Analytic Hierarchy ◽  
Specialized Care ◽  
The Analytic Hierarchy Process ◽  
Hierarchy Process

Objectives: To identify early suicide risk signs on depressive subjects, so that specialized care can be provided. Various studies have focused on studying expressions on social networks, where users pour their emotions, to determine if they show signs of depression or not. However, they have neglected the quantification of the risk of committing suicide. Therefore, this article proposes a new index for identifying suicide risk in Mexico. Methodology: The proposal index is constructed through opinion mining using Twitter and the Analytic Hierarchy Process. Contribution: Using R statistical package, a study is presented considering real data, making a classification of people according to the obtained index and using information from psychologists. The proposed methodology represents an innovative prevention alternative for suicide.

Big data investments in knowledge and non-knowledge intensive firms: what the market tells us

2017 ◽  
Vol 21 (3) ◽  
pp. 623-639 ◽  
Author(s):  
Tingting Zhang ◽  
William Yu Chung Wang ◽  
David J. Pauleen
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Stock Market ◽  
Value Chain ◽  
Stock Prices ◽  
Economic Value ◽  
Information Value ◽  
Publicly Traded ◽  
Content Type ◽  
Knowledge Intensive

Purpose This paper aims to investigate the value of big data investments by examining the market reaction to company announcements of big data investments and tests the effect for firms that are either knowledge intensive or not. Design/methodology/approach This study is based on an event study using data from two stock markets in China. Findings The stock market sees an overall index increase in stock prices when announcements of big data investments are revealed by grouping all the listed firms included in the sample. Increased stock prices are also the case for non-knowledge intensive firms. However, the stock market does not seem to react to big data investment announcements by testing the knowledge intensive firms along. Research limitations/implications This study contributes to the literature on assessing the economic value of big data investments from the perspective of big data information value chain by taking an unexpected change in stock price as the measure of the financial performance of the investment and by comparing market reactions between knowledge intensive firms and non-knowledge intensive firms. Findings of this study can be used to refine practitioners’ understanding of the economic value of big data investments to different firms and provide guidance to their future investments in knowledge management to maximize the benefits along the big data information value chain. However, findings of study should be interpreted carefully when applying them to companies that are not publicly traded on the stock market or listed on other financial markets. Originality/value Based on the concept of big data information value chain, this study advances research on the economic value of big data investments. Taking the perspective of stock market investors, this study investigates how the stock market reacts to big data investments by comparing the reactions to knowledge-intensive firms and non-knowledge-intensive firms. The results may be particularly interesting to those publicly traded companies that have not previously invested in knowledge management systems. The findings imply that stock investors tend to believe that big data investment could possibly increase the future returns for non-knowledge-intensive firms.

scholarly journals Distinguishing linear and branched evolution given single-cell DNA sequencing data of tumors

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Leah L. Weber ◽  
Mohammed El-Kebir
Keyword(s):  
Dna Sequencing ◽  
Single Cell ◽  
Evolutionary Process ◽  
Treatment Decision ◽  
Real Data ◽  
Current Data ◽  
Fast Method ◽  
Sequencing Data ◽  
Evolutionary Trajectory ◽  
Cancer Types

Abstract Background Cancer arises from an evolutionary process where somatic mutations give rise to clonal expansions. Reconstructing this evolutionary process is useful for treatment decision-making as well as understanding evolutionary patterns across patients and cancer types. In particular, classifying a tumor’s evolutionary process as either linear or branched and understanding what cancer types and which patients have each of these trajectories could provide useful insights for both clinicians and researchers. While comprehensive cancer phylogeny inference from single-cell DNA sequencing data is challenging due to limitations with current sequencing technology and the complexity of the resulting problem, current data might provide sufficient signal to accurately classify a tumor’s evolutionary history as either linear or branched. Results We introduce the Linear Perfect Phylogeny Flipping (LPPF) problem as a means of testing two alternative hypotheses for the pattern of evolution, which we prove to be NP-hard. We develop Phyolin, which uses constraint programming to solve the LPPF problem. Through both in silico experiments and real data application, we demonstrate the performance of our method, outperforming a competing machine learning approach. Conclusion Phyolin is an accurate, easy to use and fast method for classifying an evolutionary trajectory as linear or branched given a tumor’s single-cell DNA sequencing data.

Dave Snowden on KM and big data/analytics: interview with David J. Pauleen

2017 ◽  
Vol 21 (1) ◽  
pp. 12-17 ◽  
Author(s):  
David J. Pauleen
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Theory And Practice ◽  
Content Type ◽  
Face To Face ◽  
The Relationship ◽  
A Company ◽  
Practical Implications ◽  
Scientific Officer

Purpose Dave Snowden has been an important voice in knowledge management over the years. As the founder and chief scientific officer of Cognitive Edge, a company focused on the development of the theory and practice of social complexity, he offers informative views on the relationship between big data/analytics and KM. Design/methodology/approach A face-to-face interview was held with Dave Snowden in May 2015 in Auckland, New Zealand. Findings According to Snowden, analytics in the form of algorithms are imperfect and can only to a small extent capture the reasoning and analytical capabilities of people. For this reason, while big data/analytics can be useful, they are limited and must be used in conjunction with human knowledge and reasoning. Practical implications Snowden offers his views on big data/analytics and how they can be used effectively in real world situations in combination with human reasoning and input, for example in fields from resource management to individual health care. Originality/value Snowden is an innovative thinker. He combines knowledge and experience from many fields and offers original views and understanding of big data/analytics, knowledge and management.

Review of short-text classification

2019 ◽  
Vol 15 (2) ◽  
pp. 155-182 ◽  
Author(s):  
Issa Alsmadi ◽  
Keng Hoon Gan
Keyword(s):  
Social Networks ◽  
Text Classification ◽  
Development Trend ◽  
Practical Reasons ◽  
Significant Implication ◽  
Classification Problems ◽  
Content Type ◽  
Short Text ◽  
Promising Area ◽  
Low Performance

PurposeRapid developments in social networks and their usage in everyday life have caused an explosion in the amount of short electronic documents. Thus, the need to classify this type of document based on their content has a significant implication in many applications. The need to classify these documents in relevant classes according to their text contents should be interested in many practical reasons. Short-text classification is an essential step in many applications, such as spam filtering, sentiment analysis, Twitter personalization, customer review and many other applications related to social networks. Reviews on short text and its application are limited. Thus, this paper aims to discuss the characteristics of short text, its challenges and difficulties in classification. The paper attempt to introduce all stages in principle classification, the technique used in each stage and the possible development trend in each stage.Design/methodology/approachThe paper as a review of the main aspect of short-text classification. The paper is structured based on the classification task stage.FindingsThis paper discusses related issues and approaches to these problems. Further research could be conducted to address the challenges in short texts and avoid poor accuracy in classification. Problems in low performance can be solved by using optimized solutions, such as genetic algorithms that are powerful in enhancing the quality of selected features. Soft computing solution has a fuzzy logic that makes short-text problems a promising area of research.Originality/valueUsing a powerful short-text classification method significantly affects many applications in terms of efficiency enhancement. Current solutions still have low performance, implying the need for improvement. This paper discusses related issues and approaches to these problems.

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