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1 |
Enterprise social network for knowledge sharing in MNCs: Examining the role of knowledge contributors and knowledge seekers for cross-country collaboration
شبکه اجتماعی شرکت اجتماعی برای به اشتراک گذاری دانش در MNCS: بررسی نقش مشارکتکنندگان دانش و دانش آموزان دانش برای همکاری متقابل کشور-2021 Online social networking within a large enterprise, known as enterprise social networking (ESN),
is a critical requirement for social relationships and business-related informal discussions among
its employees. ESN is important for multinational companies (MNCs) where employees work in
different time zones in geographically dispersed locations in multiple continents. The MNCs use
the ESN for their knowledge management and transfer activities among different subsidiaries in
different countries or continents as a part of their strategic internationalization initiatives. ESN is
developed by MNCs using enterprise social software for business or commercial knowledge
management purposes and cross-country collaborations among their subsidiaries. ESN helps
cross-country collaboration in MNCs to organize their internal communication (across different
countries) and business discussions in an international environment. ESN is used mainly by two
groups of employees in the MNCs: knowledge contributors and knowledge seekers. Both groups
are essential for overall knowledge management strategy for creation, dissemination, and con-
sumption of knowledge across countries. In this context, the purpose of this study is to examine
the role of knowledge contributors and knowledge seekers in the MNCs using ESN for cross-
country collaboration. keywords: شبکه اجتماعی شرکت | مشارکت کنندگان دانش | دانش آموزان | mncs | همکاری متقابل کشور | Enterprise social network | Knowledge contributors | Knowledge seekers | MNCs | Cross-country collaboration |
مقاله انگلیسی |
2 |
Investigating consumer preferences on product designs by analyzing opinions from social networks using evidential reasoning
بررسی ترجیحات مصرف کننده در طرح های محصول با تجزیه و تحلیل نظرات شبکه های اجتماعی با استفاده از استدلال مشهود-2020 The rapid growth of e-commerce and social networking sites has created various challenges for the extraction of
user-generated content (UGC). In the era of big data, customer opinions from social media are utilized for
investigating consumer preferences to support product redesigns.
Opinion mining, including the various automatic text classification algorithms using sentiment analysis is a
capable tool to deal with a large amount of comments on the social networking sites. In which, sentiment
analysis is used to determine the contextual polarity within a comment by searching sentimental words.
However, the inconsistency on choosing the sentiment words leads to the inaccurate interpretation of the opinion
strength of sentiment words.
An approach to summarize the UGC from social networking media using fuzzy and ER without the need to
review all the comments is proposed in this paper. The inaccuracy on determination of the polarity of sentiment
words and corresponding opinion strengths is rectified by fuzzy approximation and ER. The result is presented in
ranking therefore the effort for result interpretation significantly reduced.
The incorporation of sentiment analysis with ER to analyze the UGC for product designs is a new attempt in
investigating consumer preferences. The proposed approach is shown to be handy, sufficient, and cost effective
for the product design and re-design, particularly in the preliminary stage.
This project can be further extended by employing alternative fuzzy approximate techniques in the fuzzy-ER
approach to support the sentiment analysis to enhance the accuracy of sentiment values for determining the
distribution assessments of ER. Keywords: Opinion mining | Sentiment analysis | Evidential reasoning | Consumer preferences | Product design |
مقاله انگلیسی |
3 |
Determinants of the management learning performance in ERP context
عوامل تعیین کننده عملکرد یادگیری مدیریت در زمینه ERP-2020 Management learning poses some challenges, firstly students should identify all administration areas and sec- ondly, they should understand the big picture of an organizational context, by integrating all the studied areas. Enterprise Resource Planning (ERP) systems are the backbone of any organization, in terms of information management systems integration. The usage of these systems is important in terms of management in any or- ganization, and ERPs can facilitate the management learning process. The main objectives of this study are to understand if the ERP usage supports management learning, and to identify the main determinants of individual performance. This study presents a success model of ERP usage for learning management context. The model was validated empirically through a survey answered by university management students. The results show that system quality, process quality, and training play a determinant role in the students performance. Keywords: Social networking sites | Human resource management | Selection | Cyberbetting | Mixed-methods | Information science | Social media | Information management | Business | Business management | Strategic management | Psychology | Organizational psychology | Digital media |
مقاله انگلیسی |
4 |
Selecting talent using social networks: A mixed-methods study
انتخاب استعداد با استفاده از شبکه های اجتماعی: یک مطالعه با روش های ترکیبی-2020 Previous studies on the use of Social Networking Sites (SNS) in personnel selection generally focus on examining this phenomenon in the selection process as a whole. However, personnel selection is a macro-process composed of several activities. This paper aims to investigate how human resource professionals use SNS in hiring decisions during the different stages of the selection process. The research uses an explanatory sequential mixed-methods approach. The first study consisted of a questionnaire-based survey of hiring professionals with the intent to describe various aspects of current practice (n ¼ 429). Survey data was analyzed using descriptive and inferential statistics. The second study comprised semi-structured interviews with hiring professionals to provide a more in- depth, richer analysis (n ¼ 24). Interview data was analyzed via qualitative thematic analysis. Results uncovered two types of users. Single-stage users emphasized efficiency concerns, whereas multiple-stage users mentioned to access profiles on an as needed-basis. Participants reported that the patterns of use could be quite complex and dynamic, with selectors revisiting the profile of the same applicant several times for different purposes, or examining profiles of the same applicant in different SNS. The assessment of SNS information is typically non- systematic, but some employers reported using scales, mainly in pre-selection. Evidence emerged of potential adverse effects during the selection process. Overall, this paper contributes to theory and practice by providing a better understanding of the use of SNS across the different stages of personnel selection. To our best knowledge, this is the first mixed-methods study of its kind. Keywords: Social networking sites | Human resource management | Selection | Cyberbetting | Mixed-methods | Information science | Social media | Information management | Business | Business management | Strategic management | Psychology | Organizational psychology | Digital media |
مقاله انگلیسی |
5 |
Probabilistic data structures for big data analytics: A comprehensive review
ساختار داده های احتمالی برای تجزیه و تحلیل داده های بزرگ: یک مرور جامع-2020 An exponential increase in the data generation resources is widely observed in last decade, because
of evolution in technologies such as-cloud computing, IoT, social networking, etc. This enormous
and unlimited growth of data has led to a paradigm shift in storage and retrieval patterns from
traditional data structures to Probabilistic Data Structures (PDS). PDS are a group of data structures
that are extremely useful for Big data and streaming applications in order to avoid high-latency
analytical processes. These data structures use hash functions to compactly represent a set of items
in stream-based computing while providing approximations with error bounds so that well-formed
approximations get built into data collections directly. Compared to traditional data structures, PDS use
much less memory and constant time in processing complex queries. This paper provides a detailed
discussion of various issues which are normally encountered in massive data sets such as-storage,
retrieval, query,etc. Further, role of PDS in solving these issues is also discussed where these data
structures are used as temporary accumulators in query processing. Several variants of existing PDS
along with their application areas have also been explored which give a holistic view of domains
where these data structures can be applied for efficient storage and retrieval of massive data sets.
Mathematical proofs of various parameters considered in the PDS have also been discussed in the
paper. Moreover, the relative comparison of various PDS with respect to various parameters is also
explored. Keywords: Big data | Internet of things (IoT) | Probabilistic data structures | Bloom filter | Quotient filter | Count min sketch | HyperLogLog counter | Min-hash Locality | sensitive hashing |
مقاله انگلیسی |
6 |
A social-semantic recommender system for advertisements
یک سیستم پیشنهادی اجتماعی معنایی برای تبلیغات-2020 Social applications foster the involvement of end users in Web content creation, as a result of
which a new source of vast amounts of data about users and their likes and dislikes has become
available. Having access to users’ contributions to social sites and gaining insights into the
consumers’ needs is of the utmost importance for marketing decision making in general, and to
advertisement recommendation in particular. By analyzing this information, advertisement recommendation
systems can attain a better understanding of the users’ interests and preferences,
thus allowing these solutions to provide more precise ad suggestions. However, in addition to the
already complex challenges that hamper the performance of recommender systems (i.e., data
sparsity, cold-start, diversity, accuracy and scalability), new issues that should be considered
have also emerged from the need to deal with heterogeneous data gathered from disparate
sources. The technologies surrounding Linked Data and the Semantic Web have proved effective
for knowledge management and data integration. In this work, an ontology-based advertisement
recommendation system that leverages the data produced by users in social networking sites is
proposed, and this approach is substantiated by a shared ontology model with which to represent
both users’ profiles and the content of advertisements. Both users and advertisement are represented
by means of vectors generated using natural language processing techniques, which
collect ontological entities from textual content. The ad recommender framework has been extensively
validated in a simulated environment, obtaining an aggregated f-measure of 79.2% and
a Mean Average Precision at 3 (MAP@3) of 85.6%. Keywords:Knowledge-based systems | Recommender systems | Natural language processing | Advertising | Social network services |
مقاله انگلیسی |
7 |
Age, gender, personality, ideological attitudes and individual differences in a persons news spectrum: how many and who might be prone to “filter bubbles” and “echo chambers” online?
سن ، جنس ، شخصیت ، نگرش های عقیدتی و تفاوت های فردی در طیف اخبار افراد: چه تعداد و چه کسی مستعد ابتلا به "حباب های فیلتر" و "اتاق های پژواک" بصورت آنلاین است؟-2020 Potential effects of demographics, personality, and ideological attitudes on the number of news sources consumed
should be investigated. The number of news sources consumed, in turn, was seen as inverse proxy for the susceptibility
to be caught in “filter bubbles” and/or “echo chambers” (online), which are hotly discussed topics also
in politics. A sample of 1,681 (n ¼ 557 males) participants provided data on demographics, the Big Five as well as
Right-Wing Authoritarianism (RWA) alongside the number of different news sources consumed and current
voting preferences. Results showed that age (positively), gender (higher in males), Openness (positively), and
RWA (negatively) predicted the number of different news sources consumed. The group of participants consuming
news exclusively offline showed highest scores in Conscientiousness and lowest scores in Neuroticism compared
to the “news feeds only” and the “news feeds and online” groups. However, less than 5% of the participants
exclusively consumed news via news feeds of social networking sites. Participants who stated that they would not
vote reported the lowest number of different news sources consumed. These findings reveal first insights into
predisposing factors for the susceptibility to be caught in “filter bubbles” and/or “echo chamber” online and how
this might be associated with voting preferences. Keywords: Psychology | Media psychology | Individual differences | Digital media | Political science | Political behavior | News spectrum | Big Five | RWA | Filter bubble | Echo chamber |
مقاله انگلیسی |
8 |
Knowledge transfer and innovation performance of small and medium enterprises (SMEs): An informal economy analysis
انتقال دانش و عملکرد نوآوری بنگاههای کوچک و متوسط (SME): یک تحلیل اقتصادی غیررسمی-2020 SME operators in the informal sector of developing economies have a significant influence on their nations economies through their involvement in international business relationships. However, the existing deficiency in the literature to show empirical relationships between knowledge transfer, from these SMEs and their international business partners, and innovation performance is a significant gap in the strategic management and international business literature. Therefore, this paper explores the link between knowledge transfer and innovation performance of informal economy SMEs that are involved in international business relationships. The study included a survey of 370 owners-managers and managers of small and medium enterprises in Nigerias informal electronic market. Using Structural Equation Model (AMOS 22) this study shows that knowledge transfer dimensions, such as R&D and social networking, have varying levels of impact on innovation performance of informal sector SMEs. Knowledge transfer from training showed an inverse and insignificant relationship with innovation performance. The study established implications and recommendations that will be useful for theory and practice. Keywords: Knowledge transfer | Innovation performance | SMEs | Informal economy | R&D | Social networking | Entrepreneurship | Business policy | Management | Business management | Strategic management |
مقاله انگلیسی |
9 |
Uncovering cyberincivility among nurses and nursing students on Twitter: A data mining study
کشف فضای مجازی در بین پرستاران و دانشجویان پرستاری در توییتر: مطالعه داده کاوی-2019 Background: Although misuse of social networking sites, particularly Twitter, has occurred, little is known about
the prevalence, content, and characteristics of uncivil tweets posted by nurses and nursing students.
Objective: The aim of this study was to describe the characteristics of tweets posted by nurses and nursing
students on Twitter with a focus on cyberincivility.
Method: A cross-sectional, data-mining study was held from February through April 2017. Using a data-mining
tool, we extracted quantitative and qualitative data from a sample of 163 self-identified nurses and nursing
students on Twitter. The analysis of 8934 tweets was performed by a combination of SAS 9.4 for descriptive and
inferential statistics including logistic regression and NVivo 11 to derive descriptive patterns of unstructured
textual data.
Findings: We categorized 413 tweets (4.62%, n=8934) as uncivil. Of these, 240 (58%) were related to nursing
and the other 173 (42%) to personal life. Of the 163 unique users, 60 (36.8%) generated those 413 uncivil posts,
tweeting inappropriately at least once over a period of six weeks. Most uncivil tweets contained profanity
(n=135, 32.7%), sexually explicit or suggestive material (n=37, 9.0%), name-calling (n=14, 3.4%), and
discriminatory remarks against minorities (n=9, 2.2%). Other uncivil content included product promotion,
demeaning comments toward patients, aggression toward health professionals, and HIPAA violations.
Conclusion: Nurses and nursing students share uncivil tweets that could tarnish the image of the profession and
violate codes of ethics. Individual, interpersonal, and institutional efforts should be made to foster a culture of
cybercivility. Keywords: Civility | Cyberincivility | Education | Incivility | Nurses | Nursing | Nursing students | Social media | Social networking sites | Twitter |
مقاله انگلیسی |
10 |
A social-semantic recommender system for advertisements
یک سیستم توصیه گر اجتماعی معنایی برای تبلیغات-2019 Social applications foster the involvement of end users in Web content creation, as a result of
which a new source of vast amounts of data about users and their likes and dislikes has become
available. Having access to users’ contributions to social sites and gaining insights into the
consumers’ needs is of the utmost importance for marketing decision making in general, and to
advertisement recommendation in particular. By analyzing this information, advertisement recommendation
systems can attain a better understanding of the users’ interests and preferences,
thus allowing these solutions to provide more precise ad suggestions. However, in addition to the
already complex challenges that hamper the performance of recommender systems (i.e., data
sparsity, cold-start, diversity, accuracy and scalability), new issues that should be considered
have also emerged from the need to deal with heterogeneous data gathered from disparate
sources. The technologies surrounding Linked Data and the Semantic Web have proved effective
for knowledge management and data integration. In this work, an ontology-based advertisement
recommendation system that leverages the data produced by users in social networking sites is
proposed, and this approach is substantiated by a shared ontology model with which to represent
both users’ profiles and the content of advertisements. Both users and advertisement are represented
by means of vectors generated using natural language processing techniques, which
collect ontological entities from textual content. The ad recommender framework has been extensively
validated in a simulated environment, obtaining an aggregated f-measure of 79.2% and
a Mean Average Precision at 3 (MAP@3) of 85.6%. Keywords: Knowledge-based systems | Recommender systems | Natural language processing | Advertising | Social network services |
مقاله انگلیسی |