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
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
Business value of big data analytics: A systems-theoretic approach and empirical test
ارزش تجاری تجزیه و تحلیل داده های بزرگ: یک رویکرد سیستم-تئوری و آزمون تجربی-2020
Although big data analytics have been widely considered a key driver of marketing and innovation processes, whether and how big data analytics create business value has not been fully understood and empirically validated at a large scale. Taking social media analytics as an example, this paper is among the first attempts to theoretically explain and empirically test the market performance impact of big data analytics. Drawing on the systems theory, we explain how and why social media analytics create super-additive value through the synergies in functional complementarity between social media diversity for gathering big data from diverse social media channels and big data analytics for analyzing the gathered big data. Furthermore, we deepen our theorizing by considering the difference between small and medium enterprises (SMEs) and large firms in the required integration effort that enables the synergies of social media diversity and big data analytics. In line with this theorizing, we empirically test the synergistic effect of social media diversity and big data analytics by using a recent large-scale survey data set from 18,816 firms in Italy. We find that social media diversity and big data analytics have a positive interaction effect on market performance, which is more salient for SMEs than for large firms.
Keywords: Big data analytics | Social media analytics | Synergies | Business value of information technology | Market performance | Digital innovation
Can twitter analytics predict election outcome? An insight from 2017 Punjab assembly elections
آیا تحلیل های توییتر می توانند نتیجه انتخابات را پیش بینی کنند؟ بینشی از انتخابات مجلس پنجم 2017-2020
Since the beginning of this decade, there has seen an exponential growth in number of internet users using social media, especially Twitter for sharing their views on various topics of common interest like sports, products, politics etc. Due to the active participation of large number of people on Twitter, huge amount of data (i.e. big data) is being generated, which can be put to use (after refining) to analyze real world problems. This paper takes into consideration the Twitter data related to the 2017 Punjab (a state of India) assembly elections and applies different social media analytic techniques on collected tweets to extract and unearth hidden but useful information. In addition to this, we have employed machine learning algorithm to perform polarity analysis and have proposed a new seat forecasting method to accurately predict the number of seats that a political party is likely to win in the elections. Our results confirmed that Indian National Congress was likely to emerge winner and that in fact was the outcome, when results got declared.
Keywords: Analytics | Election prediction | Social media | Natural language processing | Machine learning | Sentiment analysis | Twitter
Role of big data and social media analytics for business to business sustainability: A participatory web context
نقش تجزیه و تحلیل داده های بزرگ و رسانه های اجتماعی برای پایداری تجارت از مشاغل: زمینه وب مشارکتی-2020
The digital transformation is an accumulation of various digital advancements, such as the transformation of the web phenomenon. The participatory web that allows for active user engagement and gather intelligence has been widely recognised as a value add tool by organisations of all shapes and sizes to improve business productivity and efficiency. However, its ability to facilitate sustainable business-to-business (B2B) activities has lacked focus in the business and management literature to date. This qualitative research is exploratory in nature and fills this gap through findings arising from interviews of managers and by developing taxonomies that highlight the capability of participatory web over passive web to enable different firms to engage in business operations. For this purpose, two important interrelated functions of business i.e. operations and marketing have been mapped against three dimensions of sustainability. Consequently, this research demonstrates the ability of big data and social media analytics within a participatory web environment to enable B2B organisations to become profitable and remain sustainable through strategic operations and marketing related business activities. The research findings will be useful for both academics and managers who are interested in understanding and further developing the business use of participatory web tools to achieve business sustainability. Hence, this may be considered as a distinct way of attaining sustainability.
Keywords: Participatory web | Marketing and operations | Big data | Social media analytics | Business sustainability | Business-to-business (B2B)
In the absence of a main attraction : Perspectives from polar bear watching tourism participants
در غیاب یک جذابیت اصلی: دیدگاه های شرکت کنندگان گردشگری خرس قطبی-2020
Wildlife watching tourism has recently received more attention in the tourism literature. However, research is still needed on participants’ perceptions on the unpredictable nature of wild animals as main attractions. Information on this topic may help providers keep participants satisfied in the absence of wildlife and move away from exploitative practices sometimes used to guarantee close encounters. Using polar bear tourism as a case study, content analysis of TripAdvisor reviews from Churchill (Canada) and Svalbard (Norway) was used to examine participants’ comments on unpredictable wildlife and reactions when polar bears were not found. Findings indicate that to keep participants satisfied, wildlife watching tourism providers should focus on more controllable parts of the experience, such as high-quality guiding, expectations management, and secondary, more guaranteed side activities. They should also make the most of the natural surroundings, other wildlife in the area and signs of the focal species when encountered.
Keywords: Social media content analysis | Wildlife tourism | Tour guides’ role | Wildlife unpredictability | Tourist perceptions | TripAdvisor | Tourism management
Is hybrid AI suited for hybrid threats? Insights from social media analysis
آیا هوش مصنوعی ترکیبی برای تهدیدهای ترکیبی مناسب است؟ بینش از تحلیل رسانه های اجتماعی-2020
Social media create the opportunity for a truly connected world and change the way people communicate, exchange ideas and organize themselves into virtual communities. Both understanding online behavior and processing online content are of strategic importance for security applications. However, high volumes, noisy data and rapid changes of topics impose challenges that hinder the efficacy of classification models and the relevance of semantic models. This paper performs a comparative analysis on supervised, unsupervised and semantic-driven approaches used to analyze social data streams. The goal of the paper is to determine whether empirical findings support the enhancement of decision support and pattern recognition applications. The paper reports on research that has used various approaches to identify hidden patterns in social data collections where text is highly unstructured, comes with a mix of modalities and has potentially incorrect spatial-temporal stamps. The conclusion reports that the disconnected use of machine learning models and semantic-driven approaches in mining social media data has several weaknesses.
Index Terms: social networks | hybrid AI | defense and security
Advancing social media derived information messaging and management: A multi-mode development perspective
پیشبرد پیام رسانی و مدیریت اطلاعات مشتق شده از رسانه های اجتماعی: چشم انداز توسعه چند حالته-2020
With global reach of over 2 billion active users, the evolution of Social Media (SM) systems has provided organizations with sophisticated tools and technologies for delivering business objectives. Importantly, while marketers and public relations experts have taken leading positions in promotion and advancement of SM, project managers are often tasked with delivering SM systems. In this study, a sample of 127 project managers were asked to evaluate and recommend modes of SM development for six diverse firms using a four-part taxonomy. The results show that firms of varying size can employ narrowly focused and low cost SM development modes to meet their business objectives, with well-resourced firms able to use experimental modes to deliver widespread and higher cost ‘listen and learn’ SM systems. Alternatively, in addition to achieving groundswell promotions and broader business marketing and sales influencing objectives, firms that engage in large scale SM developments can document and implement SM best practices and apply multi-organizational collaborations required for information exchange, customer feedback and experience sharing. These managerial perspectives expose the intrinsic connections between SM systems and information messaging and management within firms. The article builds further into cumulative studies directed at SM systems construction, deployment, and firm capability affordances.
Keywords: Social media | Development | Mode | Messaging | Systems
Examining the Relationship between Social Media Analytics Practices and Business Performance in the Indian Retail and IT Industries: The Mediation Role of Customer Engagement
بررسی رابطه بین شیوه های تجزیه و تحلیل رسانه های اجتماعی و عملکرد تجاری در خرده فروشی ها و صنایع IT: نقش میانجیگری درگیر شدن مشتری-2020
Social media analytics (SMA) is a dynamic field which has received considerable attention from both academics and management practitioners alike. A significant number of the scholarly research currently being conducted in SMA, however, is conceptual. Industry experts know that SMA creates new opportunities for organisations who want to more strongly engage with their customers and improve business performance. However, the relationship between social media analytic practices (SMAP), customer engagement (CE), and business performance (BP) has not yet been sufficiently investigated from an empirical perspective. In order to gain a better understanding of the relationship between SMAP and BP and the mediation role of CE in that process, a largescale survey was conducted among senior and mid-level managers as well as consultants in the Retail and information technology (IT) industries in India. Specifically, a structured closed-ended questionnaire was administered to managers and management consultants country-wide and gathered usable responses from 281 respondents holding positions such as: Digital Marketing Executive/Digital Marketing Specialist, Management Consultant, Analytics Manager, Customer Relationship Manager, Marketing Director, Engagement Manager, etc. who were in charge of digital marketing strategies in the respondent retail and IT organisations. The questionnaire addressed issues related to the way in which SMAP contribute to an enhanced business performance through the mediation role of customer engagement. Structural Equation Modelling was employed to analyse the received empirical data. On the basis of the findings our research concludes that there is a significant positive relationship between SMAP and BP mediated by CE in the Indian retail and IT industries.
Keywords: Social media analytics | Customer engagement | Business performance | Indian retail and IT Industries
The impact of social media on consumer acculturation: Current challenges, opportunities, and an agenda for research and practice
تأثیر رسانه های اجتماعی بر جمع آوری مصرف کننده: چالش های فعلی ، فرصت ها و دستور کار برای تحقیق و عمل-2020
The concept of acculturation has been based on the assumption of an adaptation process, whereby immigrants lose aspects of their heritage cultures in favour of aspects of a host culture (i.e. assimilation). Past research has shown that acculturation preferences result in various possibilities and influence consumption behaviour. However, the impact of social media on consumer acculturation is underexplored, although the social purpose and information sharing online is utilized for a variety of social purposes. Recent studies have shown the transformation from an offline to an online context, in which social networks play an integral part in immigrants’ communications, relationships and connections. This study merges the views from a number of leading contributors to highlight significant opportunities and challenges for future consumer acculturation research influenced by social media. The research provides insights into the impact of social media on consumer acculturation.
Keywords: Consumer acculturation | Global consumer culture | Information management | Information systems | Marketing | Social media