Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study
رانندگان ، موانع و ملاحظات اجتماعی برای پذیرش هوش مصنوعی در مشاغل و مدیریت: یک مطالعه عالی-2020
The number of academic papers in the area of Artificial Intelligence (AI) and its applications across business and management domains has risen significantly in the last decade, and that rise has been followed by an increase in the number of systematic literature reviews. The aim of this study is to provide an overview of existing systematic reviews in this growing area of research and to synthesise their findings related to enablers, barriers and social implications of the AI adoption in business and management. The methodology used for this tertiary study is based on Kitchenham and Charter’s guidelines , resulting in a selection of 30 reviews published between 2005 and 2019 which are reporting results of 2,021 primary studies. These reviews cover the AI adoption across various business sectors (healthcare, information technology, energy, agriculture, apparel industry, engineering, smart cities, tourism and transport), management and business functions (HR, customer services, supply chain, health and safety, project management, decisionsupport, systems management and technology acceptance). While the drivers for the AI adoption in these areas are mainly economic, the barriers are related to the technical aspects (e.g. availability of data, reusability of models) as well as the social considerations such as, increased dependence on non-humans, job security, lack of knowledge, safety, trust and lack of multiple stakeholders’ perspectives. Very few reviews outside of the healthcare management domain consider human, organisational and wider societal factors and implications of the AI adoption. Most of the selected reviews are recommending an increased focus on social aspects of AI, in addition to more rigorous evaluation, use of hybrid approaches (AI and non-AI) and multidisciplinary approaches to AI design and evaluation. Furthermore, this study found that there is a lack of systematic reviews in some of the AI early adopter sectors such as financial industry and retail and that the existing systematic reviews are not focusing enough on human, organisational or societal implications of the AI adoption in their research objectives.
Keywords: artificial intelligence | business | machine learning | management | systematic literature review | tertiary study
City limits in the age of smartphones and urban scaling
محدودیت های شهر در عصر تلفن های هوشمند و مقیاس بندی شهری-2020
Urban planning still lacks appropriate standards to define city boundaries across urban systems. This issue has historically been left to administrative criteria, which can vary significantly across countries and political systems, hindering a comparative analysis across urban systems. However, the wide use of Information and Communication Technologies (ICT) has now allowed the development of new quantitative approaches to unveil how social dynamics relates to urban infrastructure. In fact, ICT provide the potential to portray more accurate descriptions of the urban systems based on the empirical analysis of millions of traces left by urbanites across the city. In this work, we apply computational techniques over a large volume of mobile phone records to define urban boundaries, through the analysis of travel patterns and the trajectory of urban dwellers in conurbations with more than 100,000 inhabitants in Chile. We created and analyzed the network of interconnected places inferred from individual travel trajectories. We then ranked each place using a spectral centrality method. This allowed to identify places of higher concurrency and functional importance for each urban environment. Urban scaling analysis is finally used as a diagnostic tool that allowed to distinguish urban from non-urban spaces. The geographic assessment of our method shows a high congruence with the current and administrative definitions of urban agglomerations in Chile. Our results can potentially be considered as a functional definition of the urban boundary. They also provide a practical implementation of urban scaling and data-driven approaches on cities as complex systems using increasingly larger non-conventional datasets.
Keywords: City boundaries definition | Spectral network analysis | Urban informatics | Social computing | Scaling laws | Complex systems | Big data
Special interest tourism is not so special after all: Big data evidence from the 2017 Great American Solar Eclipse
جهانگردی با علاقه ویژه از همه مهم تر نیست: شواهد داده های بزرگ از خورشید گرفتگی بزرگ آمریکایی 2017-2020
This study puts to empirical test a major typology in the tourism literature, mass versus special interest tourism (SIT), as the once-distinctive boundary between the two has become blurry in modern tourism scholarship. We utilize 41,747 geo-located Instagram photos pertaining to the 2017 Great American Solar Eclipse and Big Data analytics to distinguish tourists based on their choice of observational destinations and spatial movement patterns. Two types of tourists are identified: opportunists and hardcore. The motivational profile of those tourists is validated with the external data through hypothesis testing and compared with and contrasted against existing motivation-based tourist typologies. The main conclusion is that large share of tourists involved in what is traditionally understood as SIT activities exhibit behavior and profile characteristic of mass tourists seeking novelty but conscious about risks and comforts. Practical implications regarding the potential of rural and urban destinations for developing SIT tourism are also discussed.
Keywords: Big data | Instagram photos | Social media | Spatial analysis | Special interest tourism | Astro-tourism
شدت و پتانسیل انتقال کرونا در کره جنوبی
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 6 - تعداد صفحات فایل doc فارسی: 14
اهداف: ازآنجایی که اولین مورد کروناویروس جدید 2019(کوید-19) در 20 ژانویۀ 2020 در کرۀ شمالی شناسایی شد، تعداد موارد به سرعت افزایش یافت به طوری که تا 6 مارس 2020، منجربه ابتلای6284 مورد و فوت42 نفر شد. اولین تحقیق درمورد گزارش تعداد تکثیر کوید-19 در کرۀ جنوبی را برای بررسی سرعت شیوع بیماری، ارائه می دهیم.
روش کار: موارد روزانۀ تأیید شدۀ کوید-19 در کرۀ جنوبی از منابع عمومی موجود استخراج شد. با استفاده از توزیع تجربی گزارشات دارای تأخیر و شبیه سازی مدل رشد کلی، تعداد تکثیر مؤثر را برمبنای توزیع احتمال گسستۀ فاصلۀ زایشی ارزیابی کردیم.
نتایج: چهار گروه اصلی را شناسایی و تعداد تکثیر را 1.5(1.6-1.4 CI: 95%) برآورد کردیم. به علاوه، نرخ رشد طبیعی 0.6 (0.7، 0.6 CI: 95%) و مقیاس بندی پارامتر رشد 0.8 (0.8،0.7 CI: 95%) برآورد شدند، که نشان-دهندۀ پویایی رشد زیر نمایی کوید-19 می باشد. نرخ مرگ و میر موارد خام در بین مردان (1.1%) در مقایسه با زنان (0.4%) بیشتر است و با افزایش سن افزایش می یابد.
نتیجه گیری: نتایج ما انتقال پایدار اولیۀ کوید-19 در کرۀ جنوبی را نشان می دهد و از اجرای اقدامات فاصله گذاری اجتماعی برای کنترل سریع شیوع بیماری حمایت می کند.
کلمات کلیدی: کروناویروس | کوید-19 | کره | تعداد تکثیر
|مقاله ترجمه شده|
The impact of social power and influence on the implementation of innovation strategies: A case study of a UK mega infrastructure construction project
تأثیر قدرت و نفوذ اجتماعی در اجرای استراتژی های نوآوری: مطالعه موردی یک پروژه ساخت و ساز زیرساخت مگا انگلیس-2020
Influence plays a key role in reaching consensus among multiple actors involved in project-based decision- making processes. While prior literature devotes considerable attention to describing influence, little attention has been paid to influence at the individual level of the strategic project manager within the context of megaprojects. This research intended to fill this knowledge gap by identifying and describing the influence strategies that a strategic project manager applies when implementing innovation strategies on megaprojects. A qualitative case study was used to examine the complex social processes involved in a major UK capital investment programme. The findings underline a critical subset of influence strategies, notably higher-management support, inspirational appeal and bargaining. The study proposes a utilitarian structure of social power comprising selective, supportive and executory power bases
Keywords: Social power | Influence strategies | Megaprojects | Social processes | Project innovation strategy
Remote sensing and social sensing for socioeconomic systems: A comparison study between nighttime lights and location-based social media at the 500m spatial resolution
سنجش از دور و سنجش اجتماعی برای سیستمهای اقتصادی اقتصادی: مطالعه مقایسه ای بین چراغ های شب و رسانه های اجتماعی مبتنی بر مکان در وضوح مکانی 500 متر-2020
With the advent of “social sensing” in the Big Data era, location-based social media (LBSM) data are increasingly used to explore anthropogenic activities and their impacts on the environment. This study converts a typical kind of LBSM data, geo-tagged tweets, into raster images at the 500m spatial resolution and compares them with the new generation nighttime lights (NTL) image products, the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) monthly image composites. The results show that the monthly tweet images are significantly correlated with the VIIRS-DNB images at the pixel level. The tweet images have nearly the same ability on estimating electric power consumption and better performance on assessing personal incomes and population than the NTL images. Tweeted areas (i.e. the pixels with at least one posted tweet) are closer to satellite-derived built-up/urban areas than lit areas in NTL imagery, making tweet images an alternative to delimit extents of human activities. Moreover, the monthly tweet images do not show apparent seasonal changes, and the values of tweet images are more stable across different months than VIIRS-DNB monthly image composites. This study explores the potential of LBSM data at relatively fine spatiotemporal resolutions to estimate or map socioeconomic factors as an alternative to NTL images in the United States
Keywords: Nighttime lights imagery | Geo-tagged tweets | Socioeconomic factors | Social sensing
Neural Response Selectivity to Natural Sounds in the Bat Midbrain
انتخاب پاسخ عصبی به صداهای طبیعی در بطن میانی-2020
Little is known about the neural mechanisms that mediate differential action–selection responses to communication and echolocation calls in bats. For example, in the big brown bat, frequency modulated (FM) food-claiming communication calls closely resemble FM echolocation calls, which guide social and orienting behaviors, respectively. Using advanced signal processing methods, we identified fine differences in temporal structure of these natural sounds that appear key to auditory discrimination and behavioral decisions. We recorded extracellular potentials from single neurons in the midbrain inferior colliculus (IC) of passively listening animals, and compared responses to playbacks of acoustic signals used by bats for social communication and echolocation. We combined information obtained from spike number and spike triggered averages (STA) to reveal a robust classification of neuron selectivity for communication or echolocation calls. These data highlight the importance of temporal acoustic structure for differentiating echolocation and food-claiming social calls and point to general mechanisms of natural sound processing across species.
Key words: big brown bats | echolocation | social communication sounds | inferior colliculus
Using multi-features to partition users for friends recommendation in location based social network
استفاده از چند ویژگی برای توصیف دوستان برای توصیه دوستان در شبکه اجتماعی مبتنی بر مکان-2020
Friend recommendation is an important feature of social network applications to help people make new friends and expand their social circles. However, the user-location and user-user information in location based social network are both too sparse which contributes to a big challenge for recommendation. In this paper, a new multi-feature SVM based friend recommendation model (MF-SVM) is proposed which regarded as a binary classification problem to tackle this challenge. We extract three features of each user by new methods respectively. The kernel density estimation and information entropy are used to smooth the check-in data and highlight the activity level of users to extract spatial-temporal feature. Then the social feature is extracted by considering the diversity of common friends. After that a new topic model improved by LDA is proposed which both considers user reviews and corresponding service description to extract textual feature. Finally, these features are used to train the SVM and whether the users have a friend link can be predicted by our model. The experiments on real-world datasets demonstrate that the proposed method in this paper outperforms the state-of-art friend recommendation methods under different types of evaluation metrics.
Keywords: Friend recommendation | Binary classification | SVM | Multi-feature
Towards a real-time processing framework based on improved distributed recurrent neural network variants with fastText for social big data analytics
به سمت یک چارچوب پردازش در زمان واقعی بر اساس بهبود انواع شبکه عصبی مکرر توزیع شده با fastText برای تجزیه و تحلیل داده های بزرگ اجتماعی-2020
Big data generated by social media stands for a valuable source of information, which offers an excellent opportunity to mine valuable insights. Particularly, User-generated contents such as reviews, recommendations, and users’ behavior data are useful for supporting several marketing activities of many companies. Knowing what users are saying about the products they bought or the services they used through reviews in social media represents a key factor for making decisions. Sentiment analysis is one of the fundamental tasks in Natural Language Processing. Although deep learning for sentiment analysis has achieved great success and allowed several firms to analyze and extract relevant information from their textual data, but as the volume of data grows, a model that runs in a traditional environment cannot be effective, which implies the importance of efficient distributed deep learning models for social Big Data analytics. Besides, it is known that social media analysis is a complex process, which involves a set of complex tasks. Therefore, it is important to address the challenges and issues of social big data analytics and enhance the performance of deep learning techniques in terms of classification accuracy to obtain better decisions. In this paper, we propose an approach for sentiment analysis, which is devoted to adopting fastText with Recurrent neural network variants to represent textual data efficiently. Then, it employs the new representations to perform the classification task. Its main objective is to enhance the performance of well-known Recurrent Neural Network (RNN) variants in terms of classification accuracy and handle large scale data. In addition, we propose a distributed intelligent system for real-time social big data analytics. It is designed to ingest, store, process, index, and visualize the huge amount of information in real-time. The proposed system adopts distributed machine learning with our proposed method for enhancing decision-making processes. Extensive experiments conducted on two benchmark data sets demonstrate that our proposal for sentiment analysis outperforms well-known distributed recurrent neural network variants (i.e., Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), and Gated Recurrent Unit (GRU)). Specifically, we tested the efficiency of our approach using the three different deep learning models. The results show that our proposed approach is able to enhance the performance of the three models. The current work can provide several benefits for researchers and practitioners who want to collect, handle, analyze and visualize several sources of information in real-time. Also, it can contribute to a better understanding of public opinion and user behaviors using our proposed system with the improved variants of the most powerful distributed deep learning and machine learning algorithms. Furthermore, it is able to increase the classification accuracy of several existing works based on RNN models for sentiment analysis.
Keywords: Big data | FastText | Recurrent neural networks | LSTM | BiLSTM | GRU | Natural language processing | Sentiment analysis | Social big data analytics
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