Tag Archives: Analytics

December 3, 2019 · 7:37 pm

Submit your paper to Sustainability’s special issue on smart cities and digital innovation

I am co-editing a Special issue for Sustainability (IF: 2.592). Your contributions should be related to “The Sustainable Development of Smart Cities through Digital Innovation”

Deadline for manuscript submissions: 31 October 2020.

Special Issue Information

The ‘smart city’ concept has been wrought from distinctive theoretical underpinnings. Initially, this term was used to describe those cities that utilized advanced computerized systems to provide a safe, secure, green, and efficient transportation services and utilities to meet the demands of their citizens (Caragliu, Del Bo & Nijkamp, 2011; Hall, Bowerman and Braverman, Taylor, Todosow and Von Wimmersperg, 2000). A thorough literature review suggests that several cities are already using disruptive technologies, including advanced, integrated materials, sensors, electronics, and networks, among others, which are interfaced with computerized systems to improve their economic, social and environmental sustainability (Camilleri, 2015, 2017; Deakin and Al Waer, 2011; Hall et al., 2000). These cities are increasingly relying on data-driven technologies, as they gather and analyze data from urban services including transportation and utilities (Ramaswami, Russell, Culligan, Sharma and Kumar, 2016; Gretzel, Sigala, Xiang and Koo, 2015). Their underlying objective is to improve the quality of life of their citizens (Ratten, 2017; Buhalis and Amaranggana, 2015). Hence, ‘smart cities’ have introduced technological innovations to address contingent issues like traffic congestion; air pollution; waste management; loss of biodiversity and natural habitat; energy generation, conservation and consumption; water leakages and security, among other matters (Camilleri, 2019; 2014; Ahvenniemi, Huovila, Pinto-Seppä and Airaksinen, 2017; Ratten and Dana, 2017; Ratten, 2017).

Ecologically-advanced local governments and municipalities are formulating long-term sustainable policies and strategies. Some of them are already capturing data through multisensor technologies via wireless communication networks in real time (Bibri, 2018; Bibri and Krogstie, 2017). Very often, they use the Internet’s infrastructure and a wide range of smart data-sensing devices, including radio frquency identification (RFID), near-field communication (NFC), global positioning systems (GPS), infrared sensors, accelerometers, and laser scanners (Bibri, 2018). A few cities have already started to benefit from the Internet of Things (IoT) technology and its sophisticated network that consists of sensor devices and physical objects including infrastructure and natural resources (Zanella, Bui, Castellani, Vangelista and Zorzi, 2014).

Several cities are crunching big data to better understand how to make their cities smarter, more efficient, and responsive to today’s realities (Mohanty, Choppali and Kougianos, 2016; Ramaswami et al., 2016). They gather and analyze a vast amount of data and intelligence on urban aspects, including transportation issues, citizen mobility, traffic management, accessibility and protection of cultural heritage and/or environmental domains, among other areas (Angelidou, Psaltoglou, Komninos, Kakderi, Tsarchopoulos and Panori, 2018; Ahvenniemi et al., 2017). The latest advances in technologies like big data analytics and decision-making algorithms can support local governments and muncipalities to implement the circular economy in smart cities (Camilleri, 2019). The data-driven technologies enable them them to reduce their externalities. They can monitor and control the negative emissions, waste, habitat destruction, extinction of wildlife, etc. Therefore, the digital innovations ought to be used to inform the relevant stakeholders in their strategic planning and development of urban environments (Camilleri, 2019; Allam & Newman, 2018; Yigitcanlar and Kamruzzaman, 2018; Angelidou et al. ,2018; Caragliu et al., 2011).

In this light, we are calling for theoretical and empirical contributions that are focused on the creation, diffusion, as well as on the utilization of technological innovations and information within the context of smart, sustainable cities. This Special Issue will include but is not limited to the following topics:

  • Advancing the circular economy agenda in smart cities;
  • Artificial intelligence and machine learning in smart cities;
  • Blockchain technologies in smart cities;
  • Green economy of smart cities;
  • Green infrastructure in smart cities;
  • Green living environments in smart cities;
  • Smart cities and the sustainable environment;
  • Smart cities and the use of data-driven technologies;
  • Smart cities and the use of the Internet of Things (IoT);
  • Sustainable energy of smart cities;
  • Sustainable financing for infrastructural development in smart cities;
  • Sustainable housing in smart cities;
  • Sustainable transportation in smart cities;
  • Sustainable tourism in smart cities;
  • Technological innovation and climate change for smart cities;
  • Technological innovation and the green economy of smart cities;
  • Technological innovation and the renewable energy in smart cities;
  • Technological innovation and urban resilience of smart cities;
  • Technological innovation for the infrastructural development of smart cities;
  • The accessibility and protection of the cultural heritage in smart cities;
  • The planning and design of smart cities;
  • The quality of life of the citizens and communities living in smart cities;
  • Urban innovation in smart cities;
  • Urban planning that integrates the smart city development with the greening of the environment;
  • Urban planning and data driven technologies of smart cities.

Special Issue Editors

Prof. Dr. Mark Anthony Camilleri E-Mail Website
Department of Corporate Communication, University of Malta, Msida, MSD2080, Malta.
Interests: sustainability; digital media; stakeholder engagement; corporate social responsibility; sustainable tourism
Prof. Dr. Vanessa Ratten E-Mail Website
Department of Entrepreneurship, Innovation and Marketing, La Trobe University – Melbourne, Australia
Interests: innovation; technology; entrepreneurship

 

References:

  1. Ahvenniemi, H., Huovila, A., Pinto-Seppä, I., & Airaksinen, M. (2017). What are the differences between sustainable and smart cities?. Cities, 60, 234-245.
  2. Allam, Z., & Newman, P. (2018). Redefining the smart city: Culture, metabolism and governance. Smart Cities, 1(1), 4-25
  3. Angelidou, M., Psaltoglou, A., Komninos, N., Kakderi, C., Tsarchopoulos, P., & Panori, A. (2018). Enhancing sustainable urban development through smart city applications. Journal of Science and Technology Policy Management, 9(2), 146-169.
  4. Bibri, S. E., & Krogstie, J. (2017). Smart sustainable cities of the future: An extensive interdisciplinary literature review. Sustainable cities and society, 31, 183-212.
  5. Bibri, S. E. (2018). The IoT for smart sustainable cities of the future: An analytical framework for sensor-based big data applications for environmental sustainability. Sustainable Cities and Society, 38, 230-253.
  6. Buhalis, D., & Amaranggana, A. (2015). Smart tourism destinations enhancing tourism experience through personalisation of services. In Information and communication technologies in tourism 2015 (pp. 377-389). Springer, Cham.
  7. Camilleri, M. (2014). Advancing the sustainable tourism agenda through strategic CSR perspectives. Tourism Planning & Development, 11(1), 42-56.
  8. Camilleri, M. A. (2015). Environmental, social and governance disclosures in Europe. Sustainability Accounting, Management and Policy Journal, 6(2), 224-242.
  9. Camilleri, M. A. (2017). Corporate sustainability and responsibility: creating value for business, society and the environment. Asian Journal of Sustainability and Social Responsibility, 2(1), 59-74.
  10. Camilleri, M. A. (2019). The circular economy’s closed loop and product service systems for sustainable development: A review and appraisal. Sustainable Development, 27(3), 530-536.
  11. Caragliu, A., Del Bo, C., & Nijkamp, P. (2011). Smart cities in Europe. Journal of urban technology, 18(2), 65-82.
  12. Deakin, M., & Al Waer, H. (2011). From intelligent to smart cities. Intelligent Buildings International, 3(3), 140-152.
  13. Gretzel, U., Sigala, M., Xiang, Z., & Koo, C. (2015). Smart tourism: foundations and developments. Electronic Markets, 25(3), 179-188.
  14. Hall, R. E., Bowerman, B., Braverman, J., Taylor, J., Todosow, H., & Von Wimmersperg, U. (2000). The vision of a smart city (No. BNL-67902; 04042). Brookhaven National Lab., Upton, NY (US).
  15. Mohanty, S. P., Choppali, U., & Kougianos, E. (2016). Everything you wanted to know about smart cities: The internet of things is the backbone. IEEE Consumer Electronics Magazine, 5(3), 60-70.
  16. Ramaswami, A., Russell, A. G., Culligan, P. J., Sharma, K. R., & Kumar, E. (2016). Meta-principles for developing smart, sustainable, and healthy cities. Science, 352(6288), 940-943.
  17. Ratten, V., & Dana, L. P. (2017). Sustainable entrepreneurship, family farms and the dairy industry. International Journal of Social Ecology and Sustainable Development (IJSESD), 8(3), 114-129.
  18. Ratten, V. (2017). Entrepreneurship, innovation and smart cities. Routledge: Oxford, UK.
  19. Yigitcanlar, T., & Kamruzzaman, M. (2018). Does smart city policy lead to sustainability of cities? Land Use Policy, 73, 49-58.
  20. Zanella, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014). Internet of things for smart cities. IEEE Internet of Things journal, 1(1), 22-32.

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI’s English editing service prior to publication or during author revisions.

Keywords

  • Sustainability
  • Smart Cities
  • Digital innovation
  • Technological innovation
  • Sustainable innovation
  • Big Data
  • Internet of Things
  • Artificial Intelligence

Published Papers

This special issue is now open for submission.
Advertisement

Leave a comment

Filed under Analytics, Big Data, blockchain, Business, Circular Economy, Corporate Social Responsibility, Corporate Sustainability and Responsibility, CSR, destination marketing, digital media, ESG Reporting, Impact Investing, Integrated Reporting, responsible tourism, Shared Value, smart cities, Socially Responsible Investment, SRI, Stakeholder Engagement, Sustainability, sustainable development

Tagged as , , , , , , ,

December 8, 2018 · 1:24 pm

Data-Driven Marketing Technologies and Disruptive Innovations

The latest disruptive technologies are supporting  the  marketing mix elements as they can improve the businesses’ interactive engagement with prospective customers, and enhance their personalization of services. They  may also provide secure pricing options.

Many firms are evolving from their passive, rigid, and product-centric state to a more flexible, dynamic, and customer-centric environment. Technology is enabling data-driven companies to monitor and detect any changes in consumer sentiment. Savvy technology giants including Facebook, Amazon, Microsoft and Google are capturing (and analyzing) the online and mobile activity of prospective customers. Their analytics captures the consumers’ interactions with brands and companies through digital media. Big data is enabling them to target and re-target individu­als and online communities with instantaneous pricing and access options, across multiple channels (via web-site activity, mobile,video, social media, e-commerce, among others). 

Mobile tracking technologies are being utilized by big technology conglomerates as they gather information on the consumer behaviours, including their shopping habits, lifestyle preferences , et cetera. Businesses have learnt how to take advantage of on-demand, real-time information from sensors, radio frequency identification and other location tracking devices to better understand their marketing environments at a more granular level (Storey and Song, 2017). This way business could come up with personalised products and services, that are demanded by individual customers. From a business perspective, it is important to acquire this data, quickly, and in high velocities.

Many businesses are already benefiting of the programmatic advertising environment; where buyers and sellers of digital advertising connect online to exchange available inventory (Busch,2016; Stevens et al., 2016).  The challenge for tomorrow’s businesses is to recognize the value of smart technologies as effective tools that can help them analyse their marketing environment; that comprise their customers as well as their competitors.

The predictive-analytical tools can examine different scenarios as they can anticipate what will happen, when it will happen, and can explain why it happens. These technologies can monetise data by identifying revenue generating opportunities and cost savings.

Other innovations, including; blockchain’s distributed ledger technologies are improving data privacy. This technology involves the verification and the secure recording of transactions among an interconnected set of users. Blockchain tracks the ownership of assets before, during, and after any online transaction. Therefore, this technology could be used by different businesses to facilitate their transactions with marketplace stakeholders, including; suppliers, intermediaries, and consumers across borders. The block chain will probably be more convenient than other payment options, in terms of time and money. Therefore, blockchain’s ledger technology can possibly lead to better customer service levels and operational efficiencies for businesses.

The smart tourism technologies, including big data analytics are shifting how organisations collect, analyze and utilise and distribute data. A thorough literature review suggests that the crunching of big data analytics is generating meaningful insights and supporting tourism marketers in their decision making. Moreover,other technologies, including the programmatic advertising and block chain are helping them to improve their financial and strategic performance, whilst minimizing costs. Table 1 illustrates how smart tourism businesses are capturing, analysing and distributing data.

Table 1. Data-driven approaches for smart tourism

(Camilleri, 2018)

Emerging Trends and Future Research

Tomorrow’s tourism businesses will be serving customers from geographically-diverse regions. There will be more travellers from emerging markets and developing economies. The tourism service providers will have to cater to different demographics, including senior citizens and individuals with special needs; as the populations are getting older in many countries.

Therefore,  smart technologies can be used to anticipate the discerned consumers’ requirements. For instance, the use of programmatic advertising will probably increase the individuals’ intuitive shopping experiences and can tap into the individuals’ discretionary purchases.

It is very likely, that the third-party retailers will continue to form part of the distribution mix. However, many service providers will be using their direct channels to reach out to their targeted customers. 

The sales of products will continue to rely on mobile devices with increased consumer interactions through speech and voice recognition software. The service providers may possibly rely on artificial intelligence and other forms of cognitive learning capabilities, like machine learning and deep learning.

The businesses’ distributive systems could interface with virtual reality software to help online intermediaries to merchandise their products in captivating customer experiences. Many online prospects may use blockchain’s secure technology to purchase tourism products, in the foreseeable future.

This contribution calls for further empirical research that could explore smart tourism innovations for individuals and organisations, including; mobile social networking, mobile visualisation, personalization and behavioural modelling for mobile apps, programmatic advertising, blockchain, AI, and the internet of things, among other areas.

References

Busch, O. (2016), “The programmatic advertising principle”, In Programmatic Advertising (pp. 3-15). Springer, Cham, Switzerland.

Camilleri, M.A. (2018) Data-Driven Marketing and Disruptive Technologies. Working Paper 08/2018, Department of Corporate Communication, University of Malta. 

Stevens, A., Rau, A., and McIntyre, M. (2016), “Integrated campaign planning in a programmatic world”, In Programmatic Advertising (pp. 193-210), Springer, Cham, Switzerland. 

Storey, V. C., and Song, I. Y. (2017), “Big data technologies and Management: What conceptual modeling can do?”, Data and Knowledge Engineering, Vol. 108, pp. 50-67.

 

Leave a comment

Filed under Analytics, Big Data, blockchain, Business, digital media, Marketing

Tagged as , , , , , , , , , , ,

July 25, 2018 · 8:05 am

The Use of Smart Tourism Technologies

An excerpt from my latest Working Paper, entitled: The Use of Big Data, Programmatic Advertising and Blockchain Technologies in Tourism

The latest disruptive technologies are supporting the tourism businesses’ marketing mix elements as they improve the interactive engagement with individual prospects, enhance the personalisation of services, whilst providing secure pricing options. Many tourism firms are evolving from their passive, rigid, and product-centric state to a more flexible, dynamic, and customer-centric environment, as they monitor and detect any changes in consumer sentiment. Data-driven companies are increasingly capturing and analysing the online and mobile activity of prospective customers, as they delve into ecommerce and review sites, personal blogs and social media (Sigala, 2017; Kumar et al., 2017). Their analytics captures the consumers’ interactions with brands and companies through digital media. Therefore, big data is enabling them to target and re-target individuals and online communities with instantaneous pricing and access options, across multiple channels (via web-site activity, mobile, video, social media, ecommerce, among others). Large technology giants use mobile tracking technologies, to gather information on the consumer behaviours, including their shopping habits, lifestyle preferences , et cetera (Aksu et al., 2018).

Tech-savvy firms have learnt how to take advantage of on-demand, real-time information from sensors, radio frequency identification and other location tracking devices to better understand their marketing environments at a more granular level (Storey & Song, 2017). This way business could come up with personalised products and services, that are demanded by individual customers (Li et al., 2017). From a business perspective, it is important to acquire this data, quickly, and in high velocities. This paper reported that many businesses are already benefiting of the programmatic advertising environment; where buyers and sellers of digital advertising connect online to exchange available inventory (Busch, 2016; Stevens et al., 2016).

The challenge for tourism businesses is to recognise the value of smart technologies as effective tools that can analyse their marketing environment, including the customers as well as their competitors. The predictive-analytical tools can examine different scenarios; and the prescriptive analytics anticipate what will happen, when it will happen, and explains why it happens. These technologies can monetise data by identifying revenue generating opportunities and cost savings.

Other innovations, including blockchain’s distributed ledger technologies are improving data privacy, as it involves the verification and the secure recording of transactions among an interconnected set of users. Blockchain tracks the ownership of assets before, during, and after any online transaction. Therefore, this technology could be used by tourism businesses to facilitate their transactions with marketplace stakeholders, including suppliers, intermediaries, and consumers across borders. The block chain will probably be more convenient than other payment options, in terms of time and money. Therefore, blockchain’s ledger technology can possibly lead to better customer service levels and operational efficiencies for the tourism businesses.

The smart tourism technologies, including big data analytics are shifting how organisations collect, analyse and utilise and distribute data. A thorough literature review suggests that the crunching of big data analytics is generating meaningful insights and supporting tourism marketers in their decision making. Moreover, other technologies, including the programmatic advertising and blockchain’s distributed ledger system is helping them to improve their financial and strategic performance. In conclusion, this contribution calls for further research on data-driven tourism.

 

Leave a comment

Filed under Big Data, blockchain, Business, digital media, Marketing, tourism

Tagged as , , , , , , , , ,

July 23, 2017 · 2:45 pm

Targeted Segmentation Through Mobile Marketing

mobile

The mobile is an effective channel to reach out to many users. The mobile devices, including smart phones and tablets could increase the productivities and efficiencies of organisations. For the time being, the mobile applications (apps) are an “in demand” area for research and development. Gartner (2015) anticipated that mobile analytics was going to be one of the latest technologies that could disrupt business intelligence. In fact, the market for advertising on mobile is still escalating at a fast pace. Moreover, there are niche areas for professional growth, as more and more individuals are increasingly creating new applications for many purposes on mobile operating systems.

Recent advances in mobile communication and geo-positioning technologies have presented marketers with a new way how to target consumers based on their location (Camilleri, 2016). Location-targeted mobile advertising involves the provision of ad messages to mobile data subscribers. This digital technology allows marketers to deliver ads and coupons that are customised to individual consumers’ tastes, geographic location and the time of day. Given the ubiquity of mobile devices, location-targeted mobile advertising are increasingly offering tremendous marketing benefits.

In addition, many businesses are commonly utilising applications, including browser cookies that track consumers through their mobile devices as they move out and about. Very often, when users leave the sites they visited, the products or services they viewed will be shown to them again in advertisements, across different websites. Hence, many companies are using browsing session data combined with the consumers’ purchase history to deliver “suitable” items that consumers like. There are also tourism businesses who are personalising their offerings as they collect, classify and use large data volumes on the consumers’ behaviours. As more consumers carry smartphones with them, they may be easily targeted with compelling offers that instantaneously pop-up on their mobile devices.

For instance, consumers are continuously using social networks which are indicating their geo location, as they use mobile apps. This same data can be used to identify where people tend to gather — this information that could be very useful. This information is valuable to brands as they seek to improve their consumer engagement and marketing efforts. It may appear that businesses are using mobile devices and networks to capture important consumer data. For instance, smart phones and tablets that are wifi-enabled interact with networks and convey information to network providers and ISPs. This year, more businesses shall be using mobile devices and networks as a sort of sensor data – to acquire relevant information on their consumers’ digital behaviours and physical movements. These businesses have become increasingly interactive through the proliferation of near-field communication (NFC). Basically, embedded chips in the customers’ mobile phones are exchanging data with the retailers’ items possessing the NFC tags. The latest iPhone, Android and Microsoft smartphones have already included these NFC ca­pabilities. This development has recently led to the use of mobile wallets. The growth of such data-driven, digital technologies is surely adding value to the customer-centric marketing. Therefore, analytics can enable businesses to provide a deeper personalisation of content and offers to specific customers.

The geo-based marketing message or offer is delivered at the right time, and at the right place. The brands that hold customer data can gain a competitive edge over their rivals. Of course, firms will need more than transaction history and loyalty schemes to be effective at this. They may require both socio-demographic and geo-data that new mobile technologies are capable of gathering.

For instance, many mobile service companies are partnering with local cinemas, in response to the location-targeted mobile advertising; as cinema-goers often inquire about movie information, and they may book tickets and select their seats through their mobile app. The consumers who are physically situated within a given geographic proximity of the participating cinemas could be receiving location-targeted mobile ads. The cinemas’ ads will inform prospects what movies they are playing and could explain how to purchase tickets through their phone. The consumers may also call the cinemas’ hotlines to get more information from a customer service representative. Besides location-targeted advertising, the mobile companies can also promote movie ticket sales via mobile ads that arte targeted to individuals, according to their behaviour (not by location). Therefore, the companies may direct mobile-ad messages to those consumers who had previously responded to previous mobile ads (and to others who had already purchased movie tickets, in the past months). Moreover, the cinema companies could also promote movies via Facebook Messenger Ads if they logged in the companies’ website, via their Facebook account. The mobile users might receive instant message ads via pop-up windows whenever they log into the corporate site of their service provider.

It is envisaged that such data points will only increase as the multi-billion dollar advertising monopolies are built on big data and analytics that can help businesses personalise immersive ads to target individual customers. The use of credit card transactions is also complementing geo-targeting and Google Maps, with ads; as the physical purchases are increasingly demanding personalisation, fulfillment and convenience. Consumers and employees alike are willing to give up their data for value. Therefore, the businesses need to reassure their customers through concise disclosures on how they will use personal data. They should clarify the purpose of maintaining consumer data, as they should provide simple user controls to opt in and out of different levels of data sharing. This way, they could establish a trust-worthy relationship with customers and prospects.

Companies are already personalising their mobile shopping experience based on the user situation and history. Tomorrow’s tourism businesses are expected to customise their user experiences of applications and web interfaces, according to the specific needs of each segment. Big data and analytics capabilities are increasingly allowing businesses to fully leverage their rich data from a range of new digital touchpoints and to turn this into high impact interactions. Those businesses that are able to reorient their marketing and product-development efforts around digital customer segments and behaviours will be in a position to tap into the hyper-growth that mobile, social media and the wearables market are currently experiencing.

References:

Camilleri, M. A. (2016). Using big data for customer centric marketing. Using Big Data for Customer-Centric Marketing. In Evans, C. (Ed.) Handbook of Research on Open Data Innovations in Business and Government, IGI Global, Hershey, PA, USA. https://www.um.edu.mt/library/oar/bitstream/handle/123456789/10682/Using%20Big%20Data%20for%20Customer-centric%20Marketing.pdf?sequence=3&isAllowed=y

Gartner (2015) Gartner Says Power Shift in Business Intelligence and Analytics Will Fuel Disruption. http://www.gartner.com/newsroom/id/2970917

Leave a comment

Filed under Analytics, Big Data, digital media, Marketing

Tagged as , , , , , ,

December 12, 2016 · 7:27 pm

Responsible Investing: Making a Positive Impact

flower

Impact investing is one of the fastest growing and promising areas of innovative development finance (Thornley, Wood, Grace & Sullivant, 2011; Freireich & Fulton, 2009). This form of socially-responsible investment (SRI) also has its roots in the venture capital community where investors unlock a substantial volume of private and public capital into companies, organisations and funds – with the intention to generate social and environmental impact alongside a financial return.

The stakeholders or actors in the impact investing industry can be divided into four broad categories: asset owners who actually own capital; asset managers who deploy capital; demand-side actors who receive and utilise the capital; and service providers who help make this market work.

Impact investments can be made in both emerging and developed markets, and target a range of returns from below market to market rate; depending on the investors’ strategic goals. Bugg-Levine and Emerson (2011) argued that impact investing aligns the businesses’ investments and purchase decisions with their values. Defining exactly what is (and what is not) an impact investment has become increasingly important as it appears that the term has taken off among academia and practitioners.

The impact investments are usually characterised by market organisations that are driven by a core group of proponents including foundations, high-net worth individuals, family offices, investment banks and development finance institutions. Responsible entities are mobilising capital for ‘investments that are intended to create social impact beyond financial returns’ (Jackson, 2013; Freireich & Fulton 2009). Specific examples of impact investments may include; micro-finance, community development finance, sustainable agriculture, renewable energy, conservation, micro-finance and affordable and accessible basic services, including; housing, healthcare, education and clean technology among others.

Micro-finance institutions in developing countries and affordable housing schemes in developed countries have been the favorite vehicles for these responsible investments, though impact investors are also beginning to diversify across a wider range of sectors (see Saltuk, Bouri, & Leung 2011; Harji & Jackson 2012). Nevertheless, micro-finance has represented an estimated 50% of European impact investing assets (EUROSIF, 2014). This form of investing has grown to an estimated €20 billion market in Europe alone (EUROSIF, 2014). The Netherlands and Switzerland were key markets for this investment strategy, as they represented an estimated two thirds of these assets. These markets were followed by Italy, the United Kingdom and Germany.

Generally, the investors’ intent is to ensure that they achieve positive impacts in society. Therefore, they would in turn expect tangible evidence of positive outcomes (and impacts) of their capital. Arguably, the evaluation capacity of impact investing could increase opportunities for dialogue and exchange. Therefore, practitioners are encouraged to collaborate, exchange perspectives and tools to strengthen their practices in ways that could advance impact investing. The process behind on-going encounters and growing partnerships could surely be facilitated through conferences, workshops, online communities and pilot projects. Moreover, audit and assurance ought to be continuously improved as institutions and investors need to be equipped with the best knowledge about evaluation methods. Hence, it is imperative that University and college courses are designed, tested and refined to improve the quality of education as well as  professional training and development in evaluating responsible investments.

For evaluation to be conducted with ever more precision and utility, it must be informed by mobilising research and analytics. Some impact investing funds and intermediaries are already using detailed research and analysis on investment portfolios and target sectors. At the industry-wide level, the work of the Global Impact Investing Network (GIIN) and IRIS (a catalogue of generally accepted Environmental, Social and Governance – ESG performance metrics) is generating large datasets as well as a series of case studies on collaborative impact investments. Similarly, the Global Impact Investing Rating System (GIIRS) also issues quarterly analytics reports on companies and their respective funds in industry metrics (Camilleri, 2015).

For the most part, those responsible businesses often convert positive impact-investment outcomes into tangible benefits for the poor and the marginalised people (Garriga & Melé, 2004). Such outcomes may include increased greater food security, improved housing, higher incomes, better access to affordable services (e.g. water, energy, health, education, finance), environmental protection, and the like (Jackson, 2013).

Interestingly, high sustainability companies significantly outperform their counterparts over the long-term, both in terms of stock market and accounting performance (Eccles, Ioannou & Serafeim, 2012). This out-performance is stronger in sectors where the customers are individual consumers, rather than companies (Eccles et al., 2012).

It may be complicated and time-consuming to quantify how enterprises create various forms of humanitarian and environmental value, yet some approaches and analytical tools can help to address today’s societal challenges, including the return on impact investments in social and sustainability projects.

References

Bugg-Levine, A., & Emerson, J. (2011). Impact investing: Transforming how we make money while making a difference. innovations, 6(3), 9-18.
Camilleri, M. A. (2015). Environmental, social and governance disclosures in Europe. Sustainability Accounting, Management and Policy Journal, 6(2), 224-242.

Eccles, R. G., Ioannou, I., & Serafeim, G. (2012). The impact of a corporate culture of sustainability on corporate behavior and performance (No. W17950). National Bureau of Economic Research.

EUROSIF (2014). Press Release: 6th Sustainable and Responsible Investment Study 2014. Europe-based national Sustainable Investment Forums. http://www.eurosif.org/wp-content/uploads/2014/09/Press-Release-European-SRI-Study-2014-English-version.pdf (Accessed 14 May 2016).

Freireich, J., & Fulton, K. (2009). Investing for social and environmental impact: A design for catalyzing an emerging industry. Monitor Institute, January.

Garriga, E., & Melé, D. (2004). Corporate social responsibility theories: Mapping the territory. Journal of business ethics, 53(1-2), 51-71.

Harji, K., & Jackson, E. T. (2012). Accelerating impact: Achievements, challenges and what’s next in building the impact investing industry. New York, NY: The Rockefeller Foundation.

Jackson, E. T. (2013). Interrogating the theory of change: evaluating impact investing where it matters most. Journal of Sustainable Finance & Investment, 3(2), 95-110.

Saltuk, Y., Bouri, A., & Leung, G. (2011). Insight into the impact investment market: An in-depth analysis of investor perspectives and over 2,200 transactions. New York, NY: J.P. Morgan.

Thornley, B., Wood, D., Grace, K., & Sullivant, S. (2011). Impact Investing a Framework for Policy Design and Analysis. InSight at Pacific Community Ventures & The Initiative for Responsible Investment at Harvard University.

Leave a comment

Filed under Analytics, Big Data, Corporate Governance, Corporate Social Responsibility, Corporate Sustainability and Responsibility, Impact Investing, Marketing, Socially Responsible Investment

Tagged as , , , , , , , , , ,

July 17, 2015 · 7:32 pm

Crunching Big Data and Analytics from Web2.0

social media

The use of data and its analyses are becoming ubiquitous practices. As a result, there has been a dramatic surge in the use of business intelligence and analytics. These developments have inevitably led to endless opportunities for marketers to leverage themselves and gain a competitive advantage by untangling big data. Relevant data could help businesses to better serve customers as they would better know what they need, want and desire. This knowledge will lead to customer satisfaction and long lasting relationships.

Businesses are increasingly collecting and analysing data from many sources for many purposes. Much of the value of data is derived from secondary uses that were not intended in the first place. Very often datasets can possess intrinsic, hidden, not-yet-unearthed value. According to a research from IBM and the Saïd Business School at the University of Oxford; nearly nine in 10 companies were using transactional data, and three-quarters were collecting log data in 2012. This study suggested that business practitioners also gathered data from events, emails and social data (eMarketer, 2012).

This data is being collected and stored in massive amounts by search engines including Google, Bing and Yahoo as well as by e-commerce conglomerates such as eBay and Amazon. For instance, Security First boosted its productivity and customer satisfaction by using content analytics to bridge social media and the claims process. Similarly, Banco Bilbao Vizcaya Argentaria has improved its online reputation with analytics that quickly responded to online feedback (IBM, 2015).

In addition, users can easily access multiple sources of digital data that is readily available through websites, social networks, blogs, as well as from mobile devices, including smart phones and tablets. Big data is being gathered from social media content and video data from Facebook, Twitter, LinkedIn and Google Plus among others. These modern digital marketing tools are helping business to engage in social conversations with consumers. Social networks have surely amplified the marketers’ messages as they support promotional efforts. Here are some of the unique pieces of data each social network is collecting:

  • “Facebook’s interest/social graph: The world’s largest online community collects more data via its API than any other social network. Facebook’s “like” button is pressed 2.7 billion times every day across the web, revealing what people care about.
  • Google+’s relevance graph: The number of “+1s” and other Google+ data are now a top factor in determining how a Web page ranks in Google search results.
  • LinkedIn’s talent graph: At least 22% of LinkedIn users have between 500-999 first-degree connections on the social network, and 19% have between 301-499.The rich professional data is helping LinkedIn build a “talent graph.”
  • Twitter’s news graph: At its peak late last year the social network was processing 143,199 tweets per second globally. This firehose of tweets provide a real-time window into the news and information that people care about. Fifty-two percent of Twitter users in the U.S. consume news on the site (more than the percent who do so on Facebook), according to Pew.
  • Pinterest’s commerce graph: More than 17% of all pinboards are categorized under “Home,” while roughly 12% fall under style or fashion, these are windows into people’s tastes and fashion trends.
  • YouTube’s entertainment graph: What music, shows, and celebrities do we like? YouTube reaches more U.S. adults aged 18 to 34 than any single cable network, according to Nielsen. YouTube knows what they like to watch.
  • Yelp’s and Foursquare’s location graphs: These apps know where we’ve been and where we’ll go. Foursquare has over 45 million users and 5 billion location check-ins” (Business Insider, 2014).

Big data is fundamentally shifting how marketers collect, analyse and utilise data to reach out to customers. Business intelligence and analytics are helping companies to get new insights into how consumers behave. It is envisaged that the IT architecture will shortly develop into an information eco-system: a network of internal and external services where information is shared among users. Big data can support business in their decision making. It could be used to communicate meaningful results and to generate insights for an effective organisational performance. New marketing decision-making ought to harness big data for increased targeting and re-targeting of individuals and online communities. On-demand, direct marketing through digital platforms has already become more personalised than ever. The challenge for marketers is to recognise the value of big data as a tool that drives consumer in-sights.

Every customer contact with a brand is a moment of truth, in real-time. Businesses who are not responding with seamless externally-facing solutions will inevitably lose their customers to rivals. This contribution posits that a strategic approach to data management could drive consumer preferences. An evolving analytics ecosystem that is also integrated with web2.0 instruments could lead to better customer service and consumer engagement.

Leave a comment

Filed under Analytics, Big Data, Marketing

Tagged as , , , , , , , ,

July 16, 2015 · 10:08 am

Evaluating big data and predictive analytics

bigdata

The use of business intelligence and marketing information systems has expanded in recent years. Through advancements in technologies, marketers can extract value from very large data sets. Very often, companies can benefit if they use and reuse the same data to extract added value from it. Sometimes, it would also make sense for these companies to acquire data that they do not own (or data that was not collected).

All individuals leave a “digital trail” of data as they move about in the virtual and physical worlds. This phenomenon is called, “data exhaust”. Initially, this term was used to describe how Amazon.com used predictive analytics in order to suggest items for its customers. Predictive analytics could quantify the likelihood that a particular person will do something — whether it is defaulting on a loan, upgrading to a higher level of cable service or seeking another job. Such data anticipates human behaviours that have not happened as yet. For instance, Fedex has predicted which customers were most likely to defect to competitors. Even, Hewlett-Packard made a good use of suitable data to identify employees that were on the brink to leave the company. The latter corporation took remedial decisions in anticipation of staff turnover.

Predictive data is usually based on large amounts of cur¬rent and past indicative information that may have been collected from multiple sources. Such data could also provide additional details of customer personas, segments and prospects. Quantitative techniques can be deployed to find valuable patterns in data, enabling companies to predict the likely behaviour of customers, employees and others. First Tennessee Bank had used predictive analytics to increase its marketing response rate by better targeting its offers to high-value customers (IBM, 2015). Through predictive analytics businesses’ could quantify how many consumers will buy their products after receiving electronic mail. They may also measure how effective their personal mailing was.

Nowadays there are fewer inaccuracies in the measurement of big data. In addition, many applications of data can arise far from the purposes for which the data was originally intended. However, big data and predictive analytics could raise a number of concerns. Minor increases in the data accuracy of predictions can often lead substantial savings in the long term. There many companies that have saved significant financial resources by using predictive analytics. For instance, “Chickasaw Nation has used predictive and patron analytics to reduce its month-end close processes by 50%. This way it has also improved customer experience. In a similar vein, predictive tools and smart cards enabled Singapore Land Transit Authority to provide a more convenient transportation system.

Although, individuals tend to regularly repeat their habitual behaviours, pre¬dictive analytics cannot determine when and why they may decide to change their future preferences. The possibility of “one off” events must never be discounted. Many customers may be wary of giving their data due to privacy issues. The underlying question is; when does personalisation become an issue of consumer protection? In 2012, consumers learned that Target was using quantitative methods to predict which customers were pregnant. Very often, advances in technology are faster than legislation and its deployment. These issues could advance economic and privacy concerns that regulators will find themselves hard-pressed to ignore. It may appear that digital market manipulation is pushing the limits of consumer protection law.

Evidently, society has built up a body of rules that are aimed to protect personal information. Another contentious issue is figuring out the value of data and its worth in monetary terms. In the past, companies could have struggled to determine the value of their business; including patents, trade secrets and other intellectual property.

Despite its numerous pitfalls, the market is responding to the emerging demands for corporate IT solutions. Extant relational databases are capable of handling a wide variety of big data sources. Statistical analytical packages are similarly evolving and are working in conjunction with these new data platforms, data types and algorithms. Furthermore, big data is also being modified for those clients that may require cloud-based services. Cloud-based service providers offer on-demand pricing with a fast reconfiguration facility.

This short contribution suggests that in the foreseeable future many corporations would require bespoken software that is relevant for their particular line of business. Customised business intelligence software and big data systems allow organisations to load, store and query massive data sets in short time periods. Business could make good use of structured data (such as demographics) and unstructured information (including text and images) to improve their operational performance and customer service levels.

Leave a comment

Filed under Analytics, Big Data, Marketing

Tagged as , , , , , , , ,

July 6, 2015 · 8:53 am

Untangling Big Data for Digital Marketing

customers
The web and its online communities are expanding the use of big data. Ecommerce conglomerates including Amazon and eBay have already transformed the market through their innovative, highly scalable digital platforms and product recommender systems. Moreover, internet giants like Google and Facebook are leading the development of web analytics, cloud computing and social media networks. The emergence of user-generated content in fora, newsgroups, social media and crowd-sourcing platforms are offering endless opportunities for researchers and practitioners to “listen” to marketplace stakeholders; including customers, employees, suppliers, investors and the media.

Unlike the traditional transactional records that were conspicuous in past legacy systems, e-commerce systems continuously gather insightful data from the web. Much of the value of data is derived from secondary uses that were not intended in the first place. Every dataset can possess some intrinsic, hidden, not-yet-unearthed value. Having said that, many potential applications could skim along the edges of what might be ethical, moral or even legal.

In addition, online review sites and personal blogs often contain opinion-rich information that may be explored through textual and sentiment analysis. Arguably, consumer sentiment analysis may not be designed for automation but could be better adapted for the real-time monitoring of the marketing environment. Successful businesses strive to understand their customers’ personas so that they target them the right content with the relevant tone, imagery and value propositions.

Therefore, advertisers continuously gather consumer data and use it well to personalise every aspect of their users’ experience. They strive to take advantage of their consumers’ cognitive behaviour as they try to uncover and trigger consumer frailty at their individual level. It may appear that companies gather data on their customers in order to manipulate the market. They need to establish processes which determine when specific decisions are required. Firms use big data to delve into enormous volumes of information that they collect, generate or buy. Marketers need to realise that it’s important to analyse, decide and act expeditiously on data and analytics. It’s simply not enough to be able to monitor a continuing stream of information. Businesses should be quick in their decision making and take action.

Companies may use what they know about human psychology and consumer behaviour to set prices. Behavioural targeting is nothing new in digital marketing. When firms hold detailed information about their consumers, they may customise every aspect of their interaction with them. On the other hand, there could be instances when certain marketing practices could lead to unnecessary nuisances. Nowadays, customers are frequently bombarded with marketing endeavours including email promotions that are often picked up as spam. Therefore, one-size-fits-all messages could also have negative implications on prospective customers.

Eventually, firms could use this database to deliver promotional content to remind customers on their offerings. Consumer lists whether they are automated or in the cloud should always be used to deliver enhanced customer experiences. Customer-centric marketing is all about satisfying buyers. Customers may in turn become advocates for the business. Hence, technology has become instrumental for marketers in their ongoing interactions with people.

Evidently, without data, businesses could not keep a track record of their marketing effectiveness and performance stats. Engagement metrics; including, email-open rates, click through rates, pay per click and the like enable marketers to continually fine tune their individual customer targeting. Today, many individuals are becoming quite active on review sites, such as Yelp.com or Tripadvisor; and on social media channels; including Facebook, Twitter, Linkedin or Google Plus.These modern digital marketing tools are helping business to engage in social conversations with consumers. Social media networks are often rich in customer opinion and contain relevant behavioural information. Moreover, the social media analytics could capture fast-breaking trends on customer sentiments toward products, brands and companies.

Businesses may be interested in knowing whether there are changes in online sentiment and how these correlate with sales changes over time. Digital media is supporting many businesses to map out how customers receive promotions, messages, newsletters and even advertisements. Relevant data is also helping these businesses to keep a focus on their customer needs and wants.

This contribution suggests that there is scope for businesses to consider realigning (and personalising) their incentives toward individual consumers by using data-driven marketing. Many businesses have become proficient on the use of maintaining databases of prospects and customer lists. They gather this valuable information to communicate and build relationships. This data collection may possibly drive new revenue streams and build long-term loyalty.

Leave a comment

Filed under Analytics, Big Data, Marketing

Tagged as , , , , , , ,

June 19, 2015 · 9:46 am

Crunching Big Data for Operations Management

Big data

For decades businesses have been using data in some way or another to improve their operations. For instance, an IT software could support small enterprises in their customer-facing processes. Alternatively, large corporations may possess complex systems that monitor and detect any changes in consumer sentiment towards brands.

Recently, many industry leaders, including McKinsey, IBM and SAS among others have released relevant studies on big data. It transpires that they are using similar terminology to describe big data as a “situation where the volume, velocity and variety of data exceed an organisation’s ability to use that data for accurate and timely decision-making” (SAS). These providers of business intelligence solutions have developed technical approaches to storing and managing enormous volumes of new data.

The handling and untangling of such data requires advanced and unique storage, management, analysis and visualisation technologies. The terms of “big data” and “analytics” are increasingly being used to describe data sets and analytical techniques in applications ranging from sensor to social media. Usually, big data analytics are dependent on extensive storage capacity and quick processing power requiring a flexible grid that can be reconfigured for different needs. For instance, streaming analytics process big data in real time during events to improve their outcome.

Insightful data could easily be retrieved from the Web, social media content and video data among other content. Notwithstanding, such data could be presented in different forms; ranging from recorded vocal content (e.g. call centre voice data) or it can even be genomic and proteomic data that is derived from biological research and medicine.
Big data is often used to describe the latest advances in technologies and architectures. Nowadays, big data and marketing information systems predict customer purchase decisions. This data could indicate which products or services customers buy, where and what they eat, where and when they go on vacation, how much they buy, and the like.

Giant retailers such as Tesco or Sainsbury every single day receive long-range weather forecasts to work 8-10 days ahead. Evidently, the weather affects the shopping behaviour of customers. For example, hot and cold weather can lead to the sales of certain products. It may appear that weather forecasting dictates store placement, ordering and supply (and demand) logistics for supermarket chains. Other retailers like Walmart and Kohl’s also use big data to tailor product selections and determine the timing of price markdowns.

Shipping companies, like U.P.S. are mining data on truck delivery times and traffic patterns in order to fine-tune their routing. This way the business will become more efficient and incur less operational costs. Therefore, big data extracts value by capturing, discovering and analysing very large volumes of data in an economic and expeditious way. This has inevitably led to a significant reduction in the cost of keeping data.

Big data can also be linked with production applications and timely operational processes that enable continuous improvements. Credit card companies are a good illustration of this dynamic as direct marketing groups at credit card companies create models to select the most likely customer prospects from a large data warehouse. Previously, the process of data extraction, preparation and analysis took weeks to prepare and organise. Eventually, these companies realised that there was a quicker way to carry out the same task. In fact, they created a “ready-to-market” database and system that allowed their marketers to analyse, select and issue offers in a single day. Therefore, this case indicates that businesses became much more effective (and efficient) in their processes through iterations and monitoring of websites and call-centre activities. They could also make personalised offers to customers in milliseconds as they kept tracking responses over time.

Organisations are increasingly realising the utility of data that could bring value through continuous improvements in their operations. This contribution indicated that relevant data needs to be captured, filtered and analysed. Big data is already swamping traditional networks, storage arrays and relational database platforms. The increased pervasiveness of digital and mobile activity, particularly from e-commerce and social media is leading to the dissemination of meaningful data – that is being created each and every second. Successful, online businesses can gain a competitive advantage if they are capable of gathering and crunching data.

Leave a comment

Filed under Analytics, Big Data, ICT

Tagged as , , , , , , , ,

May 30, 2015 · 4:11 pm

The future of marketing is mobile…

mobile

An IBM (2012) technology trends survey indicated that mobile devices could increase the productivities and efficiencies of organisations. This study showed that mobile software was the second most “in demand” area for research and development. In addition, Gartner BI Hype Cycle (2012) also anticipated that mobile analytics was one of the latest technologies that may potentially disrupt the business intelligence market. At the same time, the market for mobile advertising is escalating at a very fast pace. Interestingly, eMarketer (2012) had predicted that mobile advertising shall experience a surge from an estimated $2.6 billion in 2012 to more than $10.8 billion in 2016. Evidently, there are niche areas for professional growth, particularly in this specialised field; as more and more individuals are increasingly creating new applications for mobile operating systems.

Recent advances in mobile communication and geo-positioning technologies have presented marketers with a new way how to target consumers based on their location. Location-targeted mobile advertising involves the provision of ad messages to cellular subscribers based on their geographic locations. This digital technology allows marketers to deliver ads and coupons that are customised to individual consumers’ tastes, geographic location and time of day. Given the ubiquity of mobile devices, location-targeted mobile advertising seems to offer tremendous marketing benefits.

In addition, many businesses are commonly utilising applications, including browser cookies that track consumers through their mobile devices as they move out and about. Once these users leave these sites, the products or services that they had viewed online will be shown to them again in advertisements, across different websites. Hence, businesses are using browsing session data combined with the consumers’ purchase history to deliver “suitable” items that consumers like. Therefore, savvy brands are becoming increasingly proficient in personalising their offerings as they collect, classify and use large data volumes on their consumers’ behaviours. As more consumers carry smartphones with them, they are (or may be) receiving compelling offers that instantaneously pop up on their mobile devices.

For instance, consumers are continuously using social networks and indicating their geo location as they use mobile apps. This same data can be used to identify where people tend to gather — information that could be useful in predicting real estate prices et cetera. This information is valuable to brands as they seek to improve their consumer engagement and marketing efforts. Businesses are using mobile devices and networks to capture important consumer data. Smart phones and tablets that are wifi-enabled interact with networks and convey information to network providers and ISPs. This year, more brands shall be using mobile devices and networks as a sort of sensor data – to acquire relevant information on their consumers’ digital behaviours and physical movements. These businesses have become increasingly interactive through the proliferation of near-field communication (NFC). Basically, embedded chips in the customers’ mobile phones are exchanging data with retailers’ items possessing the NFC tags. It is envisaged that mobile wallet transactions using NFC technologies are expected to reach $110 billion, by the year 2017. The latest Android and Microsoft smartphones have already include these NFC capabilities. Moreover, a recent patent application by Apple has revealed its plans to include NFC capabilities in their next products. This will inevitably lead to an increase in the use of mobile wallets (GSMA, 2015). Undoubtedly, the growth of such data-driven, digital technologies is adding value to customer-centric marketing. Therefore, analytics can enable businesses to provide a deeper personalisation of content and offers to specific customers.

Apparently, there are promising revenue streams in the mobile app market. Both Apple and Android are offering paid or free ad-supported apps in many categories. There are also companies that have developed apps for business intelligence. For example, enterprise / industry-specific apps, e-commerce apps and social apps. Evidently, the lightweight programming models of the current web services (e.g., HTML, XML, CSS, Ajax, Flash, J2E) as well as the maturing mobile development platforms such as Android and iOS have also contributed to the rapid proliferation of mobile applications (Chen et al., 2012). Moreover, researchers are increasingly exploring mobile sensing apps that are location-aware and activity-sensitive.

Possible future research avenues include mobile social innovation for m-learning; (Sharples, Taylor and Vavoula, 2010; Motiwalla, 2007), mobile social networking and crowd-sourcing (Lane et al., 2010), mobile visualisation (Corchado and Herrero, 2011), personalisation and behavioural modelling for mobile apps in gamification (Ha et al., 2007), mobile advertising and social media marketing (Bart et al., 2014; Yang et al., 2013). Google’s (2015) current projects include gesture and touch interaction; activity-based and context-aware computing; recommendation of social and activity streams; analytics of social media engagements, and end-user programming (Dai, Rzeszotarski, Paritosh and Chi, 2015;  Fowler, Partridge, Chelba, Bi, Ouyang and Zhai, 2015; Zhong, Weber, Burkhardt, Weaver and Bigham, 2015; Brzozowski, Adams and Chi, 2015).

 

References:

Bart, Y., Stephen, A. T., & Sarvary, M. (2014). Which products are best suited to mobile advertising? A field study of mobile display advertising effects on consumer attitudes and intentions. Journal of Marketing Research, 51(3), 270-285.

Brzozowski, M. J., Adams, P., & Chi, E. H. (2015, April). Google+ Communities as Plazas and Topic Boards. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 3779-3788). ACM. Retrieved May 22nd, 2015, from http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43453.pdf

Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS quarterly, 36(4), 1165-1188.

Corchado, E., & Herrero, Á. (2011). Neural visualization of network traffic data for intrusion detection. Applied Soft Computing, 11(2), 2042-2056.

Dai, P., Rzeszotarski, J. M., Paritosh, P., & Chi, E. H. (2015). And Now for Something Completely Different: Improving Crowdsourcing Workflows with Micro-Diversions. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (pp. 628-638). ACM. Retrieved May 17th, 2015, from http://dl.acm.org/citation.cfm?id=2675260

eMarketer (2012). eMarketer in the News: June 1, 2012 Retrieved January 28th, 2015, from http://www.emarketer.com/newsroom/index.php/emarketer-news-june-1-2012/

Fowler, A., Partridge, K., Chelba, C., Bi, X., Ouyang, T., & Zhai, S. (2015, April). Effects of Language Modeling and its Personalization on Touchscreen Typing Performance. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 649-658). ACM. Retrieved May15th, 2015, from http://cslu.ohsu.edu/~fowlera/Fowler_CHI2015.pdf

Gartner (2012). Big Data Drives Rapid Changes in Infrastructure and $232 Billion in IT Spending Through 2016. Retrieved January 20th, 2015, from https://www.gartner.com/doc/2195915/big-data-drives-rapid-changes

Google (2015). Human-Computer Interaction and Visualization Research at Google. Retrieved May 20th, 2015, from http://research.google.com/pubs/Human-ComputerInteractionandVisualization.html

Ha, I., Yoon, Y., & Choi, M. (2007). Determinants of adoption of mobile games under mobile broadband wireless access environment. Information & Management, 44(3), 276-286.

IBM (2012) Tech Trends Report. Fast track to the future. Retrieved May15th, 2015, from http://www-01.ibm.com/common/ssi/cgi-bin/ssialias?subtype=XB&infotype=PM&appname=CHQE_XI_XI_USEN&htmlfid=XIE12346USEN&attachment=XIE12346USEN.PDF#loaded

Lane, N. D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A. T. (2010). A survey of mobile phone sensing. Communications Magazine, IEEE, 48(9), 140-150.

Motiwalla, L. F. (2007). Mobile learning: A framework and evaluation. Computers & Education, 49(3), 581-596.

Sharples, M., Taylor, J., & Vavoula, G. (2010). A theory of learning for the mobile age. In Medienbildung in neuen Kulturräumen (pp. 87-99). VS Verlag für Sozialwissenschaften.

Yang, B., Kim, Y., & Yoo, C. (2013). The integrated mobile advertising model: The effects of technology-and emotion-based evaluations. Journal of Business Research, 66(9), 1345-1352.

Zhong, Y., Weber, A., Burkhardt, C., Weaver, P., & Bigham, J. P. (2015). Enhancing Android accessibility for users with hand tremor by reducing fine pointing and steady tapping. In Proceedings of the 12th Web for All Conference (p. 29). ACM. Retrieved Ma7 20th, 2015, from http://dl.acm.org/citation.cfm?id=2747277

Leave a comment

Filed under ICT, Marketing

Tagged as , , , , , , , , , , , , , ,