Building the cross-sectional partnerships in subsistence markets: The role of hyper CSR, its impact to subsistence markets, and the
decision-making framework
Master Thesis
Business Administration – Track Marketing
Name: Qi Liu
Student number: 11086904
Date: 19-08-2016
Statement of originality
This document is written by Qi Liu who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in created in. The Faculty of Economics and Business is responsible solely for the supervision completion of the work, not for the contents.
Contents:
Introduction: 4
Research Questions: 7
Literature Review: 8
Research Method: 18
Analyse of the Case: 20
What is hyper CSR: 31
The Decision Making Framework: 34
Conclusion and Discussion: 45
Introduction
The development of subsistence markets is gradually becoming a significant concern for both governments and international institutes. Building cross-sectional partnerships has been seen as an approach to help the development of the subsistence markets. Many scholars find that the cross sector partnerships, for example, the partnership between firms, NGOs and/or public sectors, have a positive impact in emerging markets (e.g., Crawford-Mathis, Darr, & Farmer, 2010, etc.). Amongst various approaches of cross-sectional partnership, the usage, exchange and donation of big data between governments and institutions accounts for a predominant role (Taylor and Broeders, 2015). Companies and institutions collect, analyse and mine data for their business purposes, yet they receive critics for the potential danger that they put their own corporate interests in front of the rights of people in the subsistence markets. For example, the French telecom company Orange has been holding the Data for Development program in Senegal, and some scholars are questioning the methodology correctness of collecting mobile data (Taylor, 2015). However, in addition to help companies to improve their marketing and management performance, data can be seen as a precious asset to the whole nation’s welfare if being used properly. The McKinsey Global
Institute has recognized the big data technology as “the next frontier for innovation, creation and productivity” (MGI, 2011). United Nation is also holding the Global Pulse Project that aims to find new approaches to the development and humanitarian challenges via the doing the collection and analysis of data cases (Luengo-Oroz, 2015). For example, Wesolowski et al. (2015) use the mobile data of 40 million mobile phone subscribers in Pakistan to analyze the spread pattern of the dengue virus in Pakistan, and they proposed a concept that can be useful in the future prevention of the dengue plague.
The data collection and donation of companies seem socially responsible as companies are devoted into social responsibility and every stakeholder is satisfied. Therefore, in 1999, a new corporate donation concept was proposed: data philanthropy (Stempeck, 2014). The concept provokes that organizations (e.g., Orange) should donate their private-collected data to NGOs and governments for the benefit of the whole society. However, this is not exact the case. Concerns on the changing role of companies’ donating and analysing data behaviour occur as well. Companies donate data not only for the benefit of the people in need, but also for their own business benefits. The data collection process often violates the privacy of the others (Taylor, 2015). Selinger and Hartzog (2013) also believe that many people are not able to protect their
personal data in a technology mediated environment. In addition, Zuboff (2015) also expresses her concern on ‘surveillance capitalism’, which means the “unexpected and often illegible mechanisms of extraction, commodification, and control that effectively exile persons from their own behaviour while producing new markets of behavioural prediction and modification”. There are also other concerns on big data technology. Taylor and Schroeder (2014) point out that epistemological challenge and pragmatic issues of applying big data technology are to be resolved. Taylor (2015) also points out that there are ethical and methodological problems with tracking human mobility using data from mobile phones. In addition, the data philanthropy behaviour also suggests a close relationship between private companies and policy makers, which makes companies acquire more power. As this political power, which goes beyond merely CSR, comes along with the relationship, companies are playing a role that is more than simply doing business. As Taylor and Broeders (2015) put it, the empowerment of public–private partnerships around datafication in LMICs and the way commercially generated big data grant companies the ability to see like a state.
The data collection and donation behaviour of companies and the power shift from the public sectors to the private sectors in LMICs suggests that a new type of so called corporate social responsibility is appearing, which I
refer ‘hyper CSR’ here. The influence of this hyper CSR on both social and managerial perspectives are important and yet there is little research focusing on this gap. For example, the governments that have cross-sectional partnerships with hyper CSR corporations are likely to suffer from the partnerships, rather than benefiting from them. It should be noted that the harm to the governments are often the harm to the citizens as the activities and policies that governments propose are related to the citizens’ daily life. Therefore, citizens under such partnerships are facing risks, for example the privacy violence and behavior modification due to the power shift from governments to private institutions (e.g., Zuboff, 2015). Therefore, it is necessary for the decision makers of governments to have a decision making framework as a guidance to perform better on building the cross-sectional partnership. In the decision making part, governments must consider the interests of the stakeholders, refer to the available information, and focus on the evaluation stages if they want to benefit from the cross-sectional partnerships.
This thesis will firstly discuss the role of changing CSR in terms of data donation of companies, then reach the decision making framework on whether governments should enter into a cross-sectional partnership. The thesis aims to provide two contributions: first, to identify the role of hyper CSR and second, to provide a decision making framework to help
governments to reference on whether they should enter a cross-sectional partnership with private sectors that related to the welfare of development and people’s welfare.
Research Questions
1. What is the trait of the new type of CSR (referred as hyper CSR here) of companies that have cross-sectional partnerships with governments in terms of data collection, analysis and donation behaviors, and what impact do hyper CSR companies have to the emerging markets?
2. Governments may face risks when entering cross-sectional partnerships with private sectors. For example, the potential of power transfer and private actors taking advantage of the partnership for their own benefit. What should the government do when deciding whether or not to engage into a new cross-sectional partnership?
Literature Review
In order to identify the role of hyper CSR and provide the structures of decision making framework, several notions are to be discussed in the
literature review. They are: stakeholder theory, institutional theory, big data technology, the power shift from governments to private sectors and the concern that goes along with it.
Stakeholder Theory
The stakeholder theory is considered as a significant approach for describing and understanding the relationships between business and society (Carroll, 1993). Stakeholder theory is originally developed by Freeman (1984), it states that companies should think about the interests of their stakeholders as stakeholders are crucial for the success of the company. Clarkson (1988) believes that the survival and success are highly depend on company’s ability to provide wealth, value and satisfaction to its stakeholders. Jonker and Foster (2002) also point out that the major business goal for a company is to understand the fluctuating aspirations and objectives of its stakeholders. In terms of building a cross-sectional CSR relationship, Lozano (2013) believes that the successful achievement of corporate sustainability requires continuous adjustment to how companies involve and empower their stakeholders.
Institutional Theory
The institution theory suggests that organizational behaviours are shaped by macro-level factors such as policies, traditions and social norms
(Campbell 2007, Marquis et al. 2007). Scott (2007) points out that institutions can be represented by three systems: cognitive, normative and regulation systems. The cognitive system suggests that institutions are boned shared meaning. Normative system suggests that institutions are formed through social sanctions and obligations. Regulative system suggests that institutions are constrained and guided through formal authorities. The institutional theory serves as a good angel to analyse the decisions that firms make. For example, company’s choice can be affected by institutionalized norms concerning corporate behaviour, stakeholder dialogue principles and preferences of the institutional investors, etc. (Campbell 2007).
Big Data Technology
Big data is the data generated from plurality sources, for example internet clicks, page view numbers, mobile phone signals and economic and business transactions such as sales queries. In order to uncover the hidden patterns and trends under those data, strong computational capabilities are often required. New insights gained from big data can be meaningful complements of official statistics and surveys, and can narrow the gaps of both information and time (George, Haas and Pentland, 2014). Organizations are exploring how big data can benefit individual, business and governments (MGI, 2011). Generally, there are five sources of big
data, namely public data, data exhaust, community data, self-qualification data and private data (MGI, 2011). Public data are data collected by the governments, for example the energy use and the health care records. Data exhaust are the data that are passively collected and of no use to the original data collector, for example, the video data of people buying Starbucks from a transportation police video camera. Community data are the data that distillated from unstructured data in order to catch up with social trends, for example the poll and the number of the clicks of the like button on Facebook. Self-qualification data are data that reveal personal behaviours. For example, the data from Apple wristband can tell the exercise preference and behaviour of the individual. Finally, private data are data held by NGOs and private companies, such as transaction records and user profiles (George, Haas and Pentland, 2014). Private data also overlap with other kinds of data as well. The sufficient source of big data indicates that the amount of data is enormous. Manyika et al. (2011) argue that the size of a big data set is beyond the ability of normal software tools to handle, and the size increases with the advance of relevant technology. However, despite the large scale, big data can create value in multiple ways including transparency, customer segmentation, decision making and the innovation of new business models (Manyika et al. 2011). In terms of government issues, big data is also widely accepted as an anchor of policy making due to the fact that it can predict human
behaviour when properly used (George, Haas and Pentland, 2014). In addition to the benefits of big data discussed above, a further implication for the researchers is to use big data to improve the living quality of human beings (MGI, 2011).
However, the discussions of the use of big data technology have drawn enormous literature focus from outstanding researchers as well as policy makers (Taylor, 2015; Zuboff, 2015; Taylor and Broeders, 2015; Selinger and Hartzog, 2015).
The Cross-sectional Partnerships in Emerging Markets
Partnerships, or social alliances, between private and governmental sectors have received a growing amount of academic and practitioner interests as this innovative arrangement can tackle with complex social problems that a single actor cannot (Kolk and Lenfant, 2015). Miguel, Rufin and Kolk (2012) believe that the cross-sectional partnerships in subsistence markets can replace governance mechanisms such as formal contracts and equity, with substitutes that are better suited to subsistence market contexts, for example informal contracts, in-kind contribution, and gifts. By replacing the governance mechanisms with substitutes that are better suited to subsistence market situations. Kolk and Lenfant (2015) also point out that even in a fragile country like Eastern Congo, the cross-sector collaboration can improve the social welfare at organizational,
individual, and society level. For example, the cross-sector collaboration of coffee partnership in Eastern Congo has built up managerial capabilities for the companies, increased the income for farmers, reduced tensions and collaboration between previously hostile groups, and most importantly, created new governance modalities. In conclusion, cross-sectional can be beneficial to the development of subsistence markets.
Power shift from the public sectors to the private sectors
Although cross-sector collaboration can be beneficial, there are concerns on the demerits that such partnership may bring. In practice, they have been dogged by contract design problems, waste, and unrealistic expectations (Sharma and Bindal, 2014). A typical example of the potential risks is the power shift from public sector to the private during a cross-sectional partnership. This paper is going to use data collection and analysis cross-sectional partnerships as examples. Traditionally, this data collection and analysis job is conducted by the governments. However, in such a digitized world, the job, and the power along with doing it, have been transferred from the governments to the multinational companies and non-governmental organizations (Taylor and Broeders, 2015). For example, the “Data for Development” program held by Sonatel and the Orange Group, in corporation with the Senegal government, aims to provide the guidance on health, agriculture, transport, infrastructures and
energy construction of low and middle income countries in Africa by collecting and sharing big data (Challenge D4D, 2013). In this activity, the Orange collected the user data of mobile users in Senegal, and share them to researchers. The reasons of this transfer vary, partly because the lack of data analysis capabilities in governments. For instance, America government alone lacks 140,000 to 190,000 data experts to research on its huge database, let alone other small countries (Manyika et al. 2011). Another reason of this power transfer is the fact that companies have already collected data automatically from their daily business activities, and therefore governments do not need to collect again. Through data philanthropy, governments have access to companies’ data and can make informed policies as well as other activities that are pro-social. There are four advantages of companies’ data donation behaviour partnership with the governments. First, data philanthropy is helpful to policy makers. For example, mobile network operators can estimate the household income of their subscribers from the amount and frequency they purchase airtime, a result may require much field survey if conducted by the government (Kirkpatrick, 2013). Another example is the complaints about the food price on Tweets, which reflect inflation trends. U.S. and Australia governments are already implementing their own Twitter monitoring instruments (Kirkpatrick, 2013). With the help of data philanthropy, the information can be used more in-time and in a more effective way.
Second, data philanthropy can predict disasters, and therefore save lives. Apart from the dengue case mentioned above, satellite companies like DMC International Imaging have also provided valuable imagery to public actors. In February 2014, DMCii set its satellites on flooding in UK, fires in India, floods in Zimbabwe, and snow in South Korea. Related government departments can request on-demand access to those images captured by satellites in order to better assess damage and coordinate relief efforts (Stempeck, 2014). With such data donation, the disasters are controlled and lives are saved. The third advantage of data philanthropy between companies and governments and NGOs is the development of academia. Access to data can be a significant barrier to some scholars due to lack of funds and channels. The result is a waste of talent. Fortunately, companies like Twitter share their own data via the Data Grant program to support researchers. With access to sufficient data, more outstanding minds will be inspired. Finally, and most importantly, data philanthropy can be beneficial to the company per se as well. According to customer attribution theory (Ellen, Webb and Mohr, 2006), customers tend to attribute a company’s CSR behaviour to a particular reason. The effect of CSR is less effective when its reasons are perceived as self-oriented, for example for the benefit of stakeholders, than those are perceived as others-oriented, for example for the benefit of the whole society (Ellen, Webb and Mohr, 2006). Data philanthropy can be perceived as an
other-oriented behaviour and therefore companies donating data can receive positive evaluation.
Although medias are praising companies’ data philanthropy (e.g., Forbes, 2011), it should be noted that comes along with data donation is the power shift due to the cross-sectional partnership that governments and firms must establish in order to collect and donate data. Data is a valuable asset to governments, and companies that collect and share it are significant as well. Ericson and Haggerty (2006) use the term “data double” to describe the situation where important personal information of individuals are circulating in various computers and contexts of practical application. Companies’ collecting and analyzing data behaviors are exact example of data double, and the data double often lead to ethical crisis, for example, the violation of users’ privacy. Taylor and Broeders (2015) point out that in low and middle income countries, there is a shift of the power of collecting and analyzing data of the citizens from governments to organizations when the latter collect and share data as a valuable resource. Corporations are more cost-efficient on collecting consumer-generated data that can be a replacement of traditional state survey data for example households and other statistical products. Along with the power shift, the role of traditional CSR changes as well. Some scholars claim that under such power shift, the traditional governance mechanisms,
for example formal contract and equity, are changed into more informal rules and gifts (Santos, Rufin and Kolk, 2012). Kolk (2014) also believe that there are institutional gaps between citizen and the governments, for example the lack of efficient healthy and education departments in low and middle income countries, and companies can help to fill in that gap. The hyper CSR makes the position of companies significant as they become partners of governments, and such significant position means the power that former belongs to governments now transfer to the hands of those companies, which implies that companies can to some extent do the governments’ job. For example, data donors are now capable of defining, sorting and categorizing identities, individuals, groups and whole societies to a big-data model (Taylor and Broeders, 2015).
Concerns on such power shift
There are many concerns on the consequences such power shift may bring with. Namely, the fear of privacy violation, the problematic methodology, and behavior modification by companies due to increased surveillance. Zuboff (2015) particularly points out that the misuse of big data can result in surveillance on citizens and challenge democratic norms. Cohen (2013) believes that companies trading information are also able to modulate people’s behavior, for example their buying preference and lifestyle. Taylor (2015) also claims that there are ethical problems with
tracking mobility using data of users from mobile phones. Selinger and Hartzog (2015) believe that the emotional contagion study conducted by Facebook is ethically problematic because the users are unable to determine what and how information is presented, and thus their behavior can be modified. In addition, Taylor and Schroeder (2014) point out that as big data being accepted by more and more disciplines, it is emergent for relevant researchers and scholars to think about how to deal with both the epistemological challenge as well as pragmatic issues.
However, little research attention is being paid on how this changing role of organizations affects the institutional gaps of low and middle countries. Will the more-than-company role affect policies and even invade privacy? There is a research gap lying between the changing role of companies and its upcoming results. Therefore, this thesis will analyze the influence of the changing role of CSR to emerging markets. The theoretical contribution of this article is to fill the research gap of changing role of CSR, and the managerial contribution of this essay is to provide governments the insight of power shift in cross-sectional partnerships and the decision making framework on whether to enter into such a partnership or not.
1. Rationale of choosing the case study method
This thesis follows a qualitative case-study method. Amongst other research methods, the case study method particularly focuses on how and why the questions are raised and how to solve the problems (Yin, 2003). In this paper, the questions of the identification of the role beyond traditional CSR and its influence on emerging markets are posed. In addition, another attribute of the case study method is that the researchers have little control over the situation. This happens in this case because the author has no control to the decision making process of companies donating data. Yin (2003) also points out that a case study should be based on a real-world context, which in this case is the D4D program in Africa. Finally, this paper uses a deskwork method rather than a fieldwork research method. Although the deskwork method can produce both qualitative and quantitative data, in this case, it is qualitative-oriented. The data selected are generally qualitative rather than quantitative, and thus the mathematical analysis approach is not an option. Therefore, the case study research method is chosen. The framework bred from this case study is suitable in other similar cases if the research is conducted well. By constructing and analysing the case of D4D in Africa, the thesis aims to first produce a decision making process framework for companies that struggle in donating data by suggesting
under which criteria and fill the research gap in academic area.
2. Steps to establish the case
The case is formulated in three steps. First, I have collected qualitative data from a various dimensions of resources: articles discussing the D4D program and its relevant consequences from academic journals are first chosen (mainly from Google Scholar and the Social Science Research Network). In addition to the articles, the relevant company information and reports from the D4D website are also applied (See http://www.d4d.orange.com/). The second step is the establishment of the case introducing the current situation of D4D in Senegal and its problems. The case will first give a brief introduction of the D4D program and the company and the government of Senegal who conducted it. Then, the case will introduce the stakeholders in the program and the advantages and disadvantages for each actor. Next step is how the program worked, what consequences it has triggered and its future likelihood of development. Finally, the case will pose the changed role of CSR and the political and economic challenges that D4D faced. This part will involve the insights of the academic literatures, from both positive and negative perspectives.
D4D Program and second, to introduce the advantages and problems along with the stakeholders of the program.
Analysis of the case
Based on this case, the thesis will discuss the changing role of CSR in terms of the data donation and its relationship with emerging markets from both politic and economic perspectives. To answer the research questions raised above, the author will first identify the company’s role that goes beyond traditional CSR, and then use a qualitative approach to establish a conceptual decision making framework for company managers to decide whether companies should engage in a cross-sectional relationship and how to perform the project successfully. The decision making framework will be a cycle-style map, with each part standing for different stages. Then, considering different moderators and mediators, for example the interests of the stakeholders, managers will be able to decide whether or not to donate the data.
Introduction of Orange D4D and Senegal
In 2012, "Data for Development Challenge" (D4D) program was first launched by the global telecommunications company Orange to analyse how anonymized and aggregated mobile network data, acquired from
Orange’s local subsidiary in Côte D’Ivoire, could be applied to gain novel insights on socio-economic development issues in developing countries (UN Global Pulse, 2014). The challenge invites researchers to use data to submit potential ways of addressing development problems. The 2012 program was the first time that a large database was accessed by scientists in order to analyse the social impacts of mobile network. Following the success in 2012, Orange and Sonatel, in partnership with UN Global Pulse, the Gates Foundation, GSMA, Paris21, the World Economic Forum, MIT, Catholic University `of Louvain, UC Santa Barbara, and Université Cheik Anta Diop de Dakar, have launched a second edition of the D4D challenge, this time with a focus on Senegal (UN Global Pulse, 2014). In 2013, Sonatel and the Orange Group are making anonymous data, extracted from the mobile network in Senegal, available to international research laboratories, as well as data on hours of sunshine (D4D Orange, 2016). In 2014, ‘Data for Development Senegal’ was held as an innovation challenge open on ICT Big Data for the purposes of societal development. The first objective of the 'Data for Development Senegal' Challenge, under the patronage of the Ministry of Higher Education and Research, in relation to the Sonatel and Orange policy in favour of development, is to contribute to the development and welfare of the populations (D4D Orange, 2016). The program was a great success on the generation of overwhelming responses. For example, over 80 research
teams from the world’s leading academic institutions submitted high calibre projects that demonstrated how analysis of mobile phone data can be applied to address a variety of development challenges from poverty mapping and disease to spread modelling and transportation optimization (UN Global Pulse, 2014). The results and patterns the program achieved shows great potential on the further developments in low and middle income countries (LMICs).
Senegal, a country lies on the West-Africa coast, is considered one of the most undeveloped countries in the world. According to the 2013 report of United Nations Development Program, 50.8% of the population of Senegal lives under the poverty line. According to the Orange company, this program was designed for the welfare of citizens of Senegal. For this purpose, 5 major subjects were acquired from the society. Those are: One, health. The Ministry of Health department of research wishes to explore the analyses mainly resulting from cross-referencing of data on the use of the mobile networks with other data directly or indirectly related to health. Two, agriculture: analyses based on mobile network use statistics. Three: transport/Urban planning. Four: energy. Five: national statistics.
The challenge wishes to achieve a technical objective as well. It seeks the plans that best advances in the algorithms of anonymisation, data-mining,
display and cross-referencing of data (D4D Orange, 2016).
Finally, the intention of the organizers of the Challenge is to achieve maximum involvement from local and regional stakeholders from the world of academic and applied research, in order to guarantee benefits in education and economic development, in particular the ecosystem of local companies and start-ups (D4D Orange, 2016).
The project has run from April 2014 to April 2015, the date on which the ‘D4D Committee’, composed of members of the Universities and Institutions supporting the initiative will choose the best projects.
Stakeholders, advantages and disadvantages
I outline eight major stakeholders who are involved in Orange D4D program. Orange company, the citizens of Senegal, the Senegal government, the scientists and paper contributors of the D4D challenge, the critical researchers, the consumers in home market and Senegal market, the regulators and the aid communities for example the World Bank. Each actor is interconnected with the others, and the pros/cons of the D4D program vary from each actor. Therefore, it is crucial for researchers to figure the advantages and disadvantages of the program from the perspective of different actors so that the criteria of the
decision-making framework can be reached.
For the Orange company, there are 4 advantages of the successful implementation of the D4D program. First, the company earns a good reputation for helping the poor. Second, the company builds a close relationship with scientists, institutions and data researchers. Third, the Orange company gains more market share in both home area (for example the Europe market) and the subject area (for example the Senegal market). Finally, the company can acquire knowledge on relevant areas such as health and agriculture in the country, and the knowledge can be useful for the future development of the company.
For the citizens of Senegal, if the D4D challenge is successfully
implemented, their average living standard will increase as the program mainly focus on the people’s livelihood, for example education, health and energy. The program also potentially increases the earning power and access to employment opportunities for individuals (Hoffman and Ballivian, 2015). In terms of the Senegal government, the research results of the program can help policy makers to make more transparent and effective policies. The government will build a tight relationship with private sectors and NGOs, and the national data they provide have high accuracy and coverage rate (Hoffman and Ballivian, 2015).
For scientists and paper contributors, the experiment in Senegal provides them a research example of using big data to influence the welfare and livelihood of a certain amount of population. In addition, according to the report of the external review panel of the Orange D4D program (ibe, 2015), the program increases the anonymization techniques that allows the analyse of the society while protecting privacy. In addition, the analysis of big data enables scientists and institutions to monitor and model the large scale of human behavior in a granularity that has never been achieved before, and this experience will offer a good reference for the future implementation of the big data technology.
In terms of regulators, the implementation of D4D program will provide a suitable example of what should be noticed when companies and governments using big data technology to monitor and guide the population behaviour. However, it is worthwhile to note that there are no international regulators focusing on the misuse of data in Senegal. The self-regulation of Orange is therefore necessary. The external panel is established to serve this purpose.
Finally, for aid groups, the potential advantage is that the implementation of the program provides a chance for them to help the LMICs and gain
experience of dealing with institutions, corporations and governments. In addition, the experience the aid groups gain during the collaboration amongst NGOs and governments is also helpful for their future development.
However, despite the potential benefits to the citizens of Senegal the program is likely to offer, for example the development in health, transportation and education, there are also critics on D4D Orange program. For example, Taylor (2015) in her article specifically points out that there are ethical problems in analyzing the tracking of mobility data. Taylor (2015) believes that the ability to track and analyse mobility data has the possibility to response the conflicts of migration, but also the possibility to unintended surveillance. Scott (1998) used term ‘legibility’ to describe such situation where the population is shaped into a more governable form by spatial movement due to high technology. In this case, the close collaboration between science institutions and the company makes it possible for the private company to acquire the information of the population movement, and therefore makes the population legible. In addition, individual’s personal privacy is also being offended. For example, (Hoffman and Ballivian, 2015) point out that such monitoring can lead to unacceptable intrusion into individual’s private life, loss of liberty or freedom of movement and chilling effect on freedom of speech and
association.
For the research institutions, aid groups and the Senegal government, another problem raised is that the liability of population data does not equal to the real-world situation, and therefore maybe not suitable as the reference for policy making and organizations’ future development. This methodology problem, taking mobile calling data as an example, the travelers may have difficulties in charging their call credits or phone batteries on the road. Therefore, the signal disappears and yet the population is still moving. In addition to that, it is unwise to assume on calling number stands on one person as well because some people may be use several SIM cards in case of one signal provider is not available in the area. One SIM card can have several users as well (Bengtsson et al., 2011). Such inaccuracy may lead to wrong estimation of the situation. Researchers should also note that governments and corporates are likely to use the big data technology to acquire benefits unethically if not being regulated.
For the Senegal government, the implementation of D4D program does not solely bring benefits. It brings disadvantages as well. For example, the over collaboration with organizations can trigger threats that can harm the national security and sovereignty of the country (Hoffman and Ballivian,
2015). Political change or backlash may also appear.
Finally, there is a mismatch between data analysis skills and the actual understanding of the data. For example, the mathematical and computational capability to analyse numerous data belongs to big institutions rather than field work experts who can really tell the truth from the data. This leads to a gap between the data analysis capability and those who can truly understand the data (Taylor, 2015).
Therefore, the D4D program, although started with good faith, is facing obstacles and challenges. Not only does it have potential disadvantages, but also it bears the truth that the ideas D4D program generated have not truly been applied to Cote d’ivoire yet1 (Taylor, 2016). Here in this case,
the Senegal government requires a regulatory and legal framework to allow the use of big data for the purposes of development.
The impact of hyper CSR to emerging markets
In conclusion, stakeholders and emerging markets enjoy both the advantages and disadvantages of adopting private hyper CSR companies to help their development. On one hand, emerging markets bear the
benefit that large companies bring. For instance, internationalized and global communication, efficient welfare system and a deeper understanding of the information of the country’s population. The collaboration between local governments and private hyper CSR corporations have improved the living standard of the local people to some extent. In terms of the D4D Project, the Orange has generated innovative ideas on how to use mobile data to improve the development in Senegal. Although there are not actual actions being done, there exists a potential that the living standard of Senegal citizens can be improved. On the other hand, as has already been discussed, there are disadvantages as well. The threat to national security is a consistent concern to governments, and legible population makes corporations to see like a real state, which is criticized by many scholars.
Legal Framework
As mentioned above, there lies a mismatch between the urgent requirements of data process ability and lack of proper regulations in LMICs. Governments ought to be more careful on companies’ data collection and analysis behaviors that under the name of development. To establish the regulatory and legal framework to allow the use of big data for the purposes of the common good and development, regulators must distinguish crystal clear what behaviors companies do are for the sake of
development of the country and what are for their own interests. In addition, governments should be vigilant on the actual results that the projects lead to. The true purpose of a company’s hyper CSR activities may lie behind the veil of development, but the actual results can always reveal the truth. For example, in the Orange D4D Challenge case, none of the wined ideas was implemented. The Senegal government (and that of Cote d'ivoire) should be able to identify such behavior. Next, it is also crucial for LMICs governments to not to alienate too much power to private sectors. Hoffman and Ballivian (2015) believe that if not managed well, the cross-sectional data collaboration behavior would harm the country’s trade, sovereignty and national security, and even lead to political backlash. Therefore, a proper regulation should at least contain three perspectives: clear distinguishing and identification of the difference between corporate and development, requirements on the actual effect of development behaviors and limited power alienation.
What is hyper CSR?
The Orange case implies three traits of a hyper CSR company. First, there is a power shift between local government and the organization due to datafication and development requirements. Governments need the patterns derived from big data to make policies, however their ability are
restrained due to lack of computational and analytical capabilities. Therefore，large companies are chosen as data agencies as they are more cost-efficient in terms of data collection and analysis, and local government can only get access to data that is firstly processed by companies (Taylor and Broeders, 2015). It is clear that a power transfer has already happened in Senegal: the job of collecting and analyzing data are being conducted by the Orange company rather than the Senegal government. A typical example of the existence of power shift is India’s bio-metric identification project, the Unique Identification Authority of India (UIDAI). This scheme contains the world’s largest biometric database and aims to provide every Indian resident with a 12-digit Unique Identity so that residents can use it as welfare recipient, bank account, mobile contract and many other commercial products. Ramanathan (2013) points out that although UIDAI is officially a is a quasi-governmental organization, it effectively is an autonomous organization which operates without a legal framework or parliamentary oversight. The unique identification reaches into the realm of governance and can lead to privacy problems (Jayaram, 2014). In this case, although the company did not donate data to any other parties, it is still an example of hyper CSR as there is a power shift from government to the company, and the company is putting its own interest in front of the welfare of citizens in a cross-sectional partnership. This privacy concern leads us to the next trait of
hyper CSR companies.
Second, hyper CSR companies often put corporate interests in front of privacy or ethical concerns, for example they use patterns from the data to alter customers’ behavior. Although private sector always plays a role in helping the development through CSR, the new datafication environment gives it a new approach. The private organizations become data collection agencies of the country. However, as Greenleaf (2013) points out, an overwhelming numbers of low and middle income countries are lack of data protection laws. For example, in Sub-Saharan Africa, only 8 states have data protection laws. In addition, although some of the countries adopt EU data regulation standards, they do not prevent corporations sharing data in the name of development. Without proper regulations, the surveillance and behavior modification of individuals by corporations are of high possibility. Zuboff (2015) particularly points out the possibility that companies use data to change customers’ emotion and behavior. An example of this is the Facebook emotional contagion experiment. In this experiment, researchers manipulate the emotions of Facebook viewers by showing fewer positive news to see if the users will express greater sadness (Kramer et al., 2014). This research has been criticized by many researchers due to its unethical nature and violation of the rights of customers (Caplan and Seife, 2014; Rosenbush, 2014).
Facebook, under a socially laudable goal, actually co-opted its users into a human subject experience that could potentially harm them (Selinger and Hartzog, 2015). Clearly, the companies doing hyper CSR put the corporate interest in front of privacy and ethical concerns and harm the rights of citizens.
Third, the corporations doing hyper CSR are using the data to create projects that are labeled development but entirely for their own interests (Taylor and Broeders, 2015). As shown in the case, the role of Orange is even more than a data collector – it becomes a powerful minister of the country. With such power, the program goals that are beneficial to the company are achieved: the company received tons of research papers that suggests how to make use of the data that it has. The relationship between Orange and data research institutions are tightened, and this will calibrate Orange's own dataset so that the company can know their customers well and develop more applications. In addition, by holding the D4D program Orange build a good brand image. However, the goals that are beneficial to the Senegal citizens are not met: none of the ideas are truly implemented. The Orange merely offers their mobile phone data to researchers and collects ideas, but none of those ideas are used in policy making or welfare improving yet (Taylor, 2016). In the first challenge in 2012/13, the winner idea was proposed by IBM, which is a competitor of
Orange. The company did not want the competitor to gain access to its strategy, therefore the plan was not able to be implemented. In the second challenge the situation has not been improved yet, none of the winning ideas are implemented so far. The power transfer due to datafication makes the original pro-bono behaviour a hyper CSR now, containing self-serving proposes for example marketing performance and relationship building.
It should be point out that, although the definition of hyper CSR derives from the case of D4D Senegal, it should be more than the companies that simply do data collection and donation behaviors in collaboration with governments, otherwise it would be too narrow. The hyper CSR companies refers to those who take advantage of the cross-sectional partnerships, and put their own interest in front of the benefits of the people that truly need help.
Decision making framework
Based on the case information and the discussions above, I propose a decision making framework to help the governments to decide whether or not to engage in a cross-sectional partnership in terms of data collection, donation and analysis. The article aims to offer a general decision making
framework that suits for not only data collection and donation, but also other cross-sectional partnerships activities as well. Although different institutions follow different organizational structure and corporate culture, it is important for government respondents, for example government officials, to make proper decisions according to their overall strategy, mission, vision, capability, strengths and weaknesses, the information from different perspectives, be consistent while flexible, and carefully implement and evaluate the activities. Therefore, this section offers a general decision making framework on whether companies should step into a cross-sectional partnership, regarding to the interests of stakeholders, governmental and operational level situations, business/moral concerns and alternative evaluation. There are four parts in the decision making framework: the opportunity and measurable objective identification, the information collection and analysis, the decision making criteria, and the evaluation of alternatives. Each part leads to the next part.
Opportunity and objective identification
In the opportunity and objective identification part, decision makers should be able to first find opportunities, and then define their objectives clearly. For example, the decision makers of governments in Senegal shall see that the mobile communication is widely used in Senegal, and it is
possible and beneficial to collect those data to gain a deeper understanding of the society, and help to develop the society. In addition, they must take fully consideration of the stakeholders’ interests. They should not only use the pervious knowledge, but also be able to consider the influences of stakeholders that are involved in the activity, and then identify the opportunities that whether a cross-sectional partnership can be built. For instance, the Orange company has held the D4D Challenges in Cote D’ivoire and Senegal, but the company failed to fully consider the stakeholders’ interest. For example, the company did not implement the winning program of D4D Cote D’ivoire as it was proposed by IBM, which is the competitor of Orange. To better understand the stakeholders of the program, the stakeholder theory is used in this part. Stakeholder holder theory believes that the performance excellency of a company is closely related to its capability of satisfying stakeholders’ interests. Therefore, it is significant for decision makers to identify the opportunity that contains the benefit and influence of the stakeholders into account. In this case, the citizens of Senegal are the most important stakeholders for the government to consider. If the governments already have a reputation in the area and their knowledge to the customers of the market is sufficient, then the decision makers will find it much easier to implement the program. On the other hand, operating the cross-sectional partnership is difficult for both sectors if there are no connections to the area at all.
Orange, as discussed in the case, is the major mobile phone service provider in Senegal and Côte D’Ivoire and therefore it already has a good understanding of its customers and sufficient data to conduct the experiments. In terms of the influence of stakeholders, the decision makers must consider what influence the stakeholders will cast upon to the project, and what influence will the project cast upon to the stakeholders. For example, some shareholders of the private sectors are not satisfied towards corporate CSR behaviour as they believe that such behaviour will harm the interests of shareholders (Vogel, 2008). Therefore, when identifying the opportunity, decision makers should think from the stakeholders’ perspective.
After taken stakeholders’ interests into fully account, the decision makers should then be able to set up the clear and measurable objective goal that this partnership wants to achieve. For example, the governments such the government and NGOs of D4D Program in Senegal, wish the program can first increase the livelihood level of the citizens of Senegal and then increase the experience on the large-scale data use, enhance the relationship with research institutions, etc. In order to achieve this goal, the decision makers must put the welfare of the citizens in Senegal in the first place. If the program can be harmful to the citizens of Senegal, for example violate their privacy safety, it should not be started. In order to
make the best choice, decision makers also need to set measurable targets and performance measurements. Every activity requires a target to measure the performance and to inspire the team. The measurable targets can be either qualitative or quantitative. However, instead of setting an ultimate goal, it is wise for managers to set several intermediate goals that are easier to achieve and evaluate. Intermediate goals also grant opportunities for manager to reevaluate and adjust the plans. Hohnen and Potts (2007) propose a “SMART” guideline to establish targets, they are: Simple, Measurable, Achievable, Reliable, and Time-bound.
Information Collection and Process
When the opportunity is identified, the framework goes to the information collection and process part. In order to make a rational, scientific choice, it is necessary for the decision makers to collect and analyse information from all available sources. For instance, in the D4D Senegal Challange, the decision makers of the public sector of Senegal can collect the information of the members of the Board of Directors and major shareholders of Orange to see where the company’s true interests lie. In addition to this, the decision makers of the public sector can also analysis the information of their own country, for example what aspects can make the best use of the benefit that this program may bring. By critically
analyse the information, including the strength, weakness of both sectors, the decision makers are able to make the rational decisions.
Decision Making Criteria
External Criteria
In the decision making criteria part, decision makers need to take two types of criteria into account: external criteria and internal criteria. The external criteria consist outside macro environment issues, for example law and regulations, political environments and economic situation of the local country. The decisions must be based on the fully compliance of those macro environment criteria. For public sectors, the external criteria serve as filters to choose private sectors that are suitable to the country’s situation, and thresholds to prevent private sector from withdraw from or break with the relationship. From the perspective of private sectors, for example the companies, they must at first make sure that the local government is going to support the behaviour, that the local environment is stable, and that doing such behaviour does not violate laws or regulations. The laws are the major tool that governments use to regulate firms’ social, economic and environmental impacts. Generally, countries have laws regrading to many layers and aspects, for example state level laws and the local level laws, and laws relating to tax, environments,
human rights and bribery and corruption. If the company wishes to establish the relationship, it is crucial for it to fully obey the laws and regulations of the country. However, there are debates on whether companies should follow a “compliance mentality”, which believes that companies should only do things that are required by the governments, or a “value-driven mentality”, which believes that companies should be innovative and seek for new opportunities (Hohnen and Potts, 2007). If the company manager believes that the company cannot fully obey the laws and regulations, it should withdraw from the relationship.
Internal Criteria
Then, decision makers should consider the internal criteria. The internal criteria contain specific determinants on whether the partnership will go smoothly, and whether the program will comply with the objective – to help increase the development and citizens’ welfare. The first internal criterial is the project implementation issue. Decision makers must make sure that the project can run smoothly in the operational level. That is, the proper training of personnel, previous experience/knowledge of the partners, etc. This part focuses on the implementation of the program and is essential to success, and failing to meet the commitments will lead to problems including unsatisfied employees, shareholders and customers (Hohnen and Potts, 2007). Therefore, decision makers must be vigilant on
the implementation of the project, and that companies are qualified and can be trusted on the successful implementation of the program. There are 2 perspectives that decision makers can observe in order to decide whether the private companies are qualified to implement the program: 1. Whether the private sector is able to prepare the business plan and
design the training program for the relevant personnel. The business plan must be consistent with the firm’s strategy and commitments. A reliable business plan can help the ensure the commitments and ideas are smoothly transferred to real actions (Hohnen and Potts, 2007). Decision makers must also check the business plan. A proper business plan determines what resources and activities are required to carry out the partnership successfully and to make the communication between public sector and private sector smoothly. In addition to the business plan, the decision makers must also take the quality of employees of the private partner as consideration. To fulfill the duty, the sufficient training of personnel is required. Executives from the private sectors must determine who is responsible for the decisions and actions of the program, and that employees are all aware of the information on the firm’s strategy. In terms of a cross-sectional partnership that is conducted in a cross culture environment, the managers need to make sure that the trainings are offered in different language and consider the personnel’s cultural orientation. For example, the IKEA’s
Co-Worker Environment and Social Responsibility Training Program covers the information of IKEA’s policies and strategies on worldwide environmental and social issues. With 90000 employees in 44 countries, the IKEA’s training program has been welcomed by its employees and received high compliments (IKEA, 2005).
2. Whether the private sector is able to establish correcting mechanisms for the problematic behavior. The success and future of the program and the welfare of the citizen relay on the careful detection and preparation of problematic behaviors. If something goes wrong, mangers must be able to identify it immediately and bring it back to track with proper correcting mechanisms. A good correcting mechanism contains not only detection processes, but also resolution processes. For example, company managers can set up communication hotlines for employees to report any misbehaviors, and ethic boards to determine whether a particular behavior is unethical or not.
The second internal criteria are the belief of the private companies, which also determines whether the partnership can be beneficial or harmful to the welfare of the citizens. Here the institutional theory is used. The institutional theory believes that a company’s behaviour can be influenced by its corporate culture. Take corporate social responsibility behaviour of companies as example, Van de Ven and Graafland (2006) believes that
there are two motivations for companies doing corporate social responsibility behaviors: the moral motivation, which suggests that CSR is a moral duty of companies towards society, and the strategic motivation, which suggests that CSR can improve the company image and eventually contributes to the financial success of the company in the long run. Here, the focus on business motivation or moral motivation can be seen as a difference between institutional systems. The cross-sectional relationship can be based on either strategic motivation or moral motivation, but hyper CSR is based on strategic motivation. As Revathy (2012) points out, some CSR are coming out of the notion of ‘doing social good’ and are fast changing towards to a ‘business necessity’. In this decision making framework, different motivations will lead to different selections and decisions. If the cross-sectional relationship is designed for strategic purpose, for example to increase company’s financial performance and shareholders’ interest, then the manager should give the priority to the business concerns rather than moral concerns. For example, some type of CSR will increase the transparency and the production costs of the company (Yager, 2006), and managers who put business interest first should avoid performing such CSR. On the other hand, companies which focus on moral duty should make the decision upon the welfare that the cross-sectional partnership can bring to the subject area. If there is potential damage towards the rights or benefits to the others, then the
manager should reconsider the plan. For instance, the collection of mobile phone user data may harm the privacy of the users due to methodology problems (Bengtsson et al., 2011; Taylor, 2015). When implementing the decision making framework, decision makers should carefully examine the company’s internal belief, and make the decisions accordingly.
Evaluation of Alternatives
The fourth part is the evaluation of the alternatives of the different choices. The alternative evaluation is a critical stage as it ensures decision makers to eliminate bias, potential risks, and to reach out the most suitable choice. The evaluation stage contains both quantitative and qualitative approaches. The quantitative approach is about using mathematical formulas and numbers to assess the values and the potential outcomes, for example the financial profit or the population that involved, of each alternative. For example, decision makers of the public sector can use a decision tree to visually present the outcome of each decision and sun-decision, the and probabilities that each outcome will occur. Another tool that can be used for the quantitative evaluation is the influence diagram. An influence diagram is a graphical and mathematical representation that shows different variables, factors and their probabilities and relating outcomes. The quantitative evaluation approach can make each alternative more transparent. However, the numbers are
not only standard to depend on. Qualitative approach involves interviews and surveys; it enables the decision makers to know the overall thoughts of each alternative of the citizens. While conducting qualitative evaluation approach, decision makers must be able to investigate what parts that the interviewees believe will work well and what parts will not, and make sure there are improvements. By evaluating the alternatives, the decision makers know whether they are achieving their current goal and what should be done to achieve it.
Discussion and Conclusion
The welfare and development of the low and middle income countries is one of the most popular topics in the world. While some non-profit organizations trying to contribute to the development of LMICs, some firms also engaged into the new markets with the banner of “development”. However, the strategic oriented companies put the corporate interests in front of the welfare of developing countries. The close relationship between non-private (mainly public) sectors and private sectors has led to hyper CSR, a new type of corporate social responsibility. This thesis discussed the nature of hyper CSR, and its influence in emerging markets, and proposed a decision making framework for governments to better choose whether to engage into a partnership. The thesis has proposed and answered two research questions:
1. What is the “changing role beyond the traditional CSR” (referred as hyper CSR here) of companies that donate data and what impact do hyper CSR companies have to emerging markets?
2. Governments may face risks when entering cross-sectional partnerships with private sectors. For example, the potential of power transfer and private actors taking advantage of the partnership for
their own benefit. What should the government do when deciding whether or not to engage into a new cross-sectional partnership？
The hyper CSR has three traits. First, it always occurs during a collaboration between public sectors, for example governments, and private sectors. The power shift from the public sector to the private sector during the cross-sectional partnership grants private companies power and convenience to act on behalf on their own interests. Second, a hyper CSR company’s CSR behavior is always strategic oriented. It tends to put the corporate interest in front of the moral concerns. Finally, hyper CSR companies usually put their own interest in front of the whole benefit of the people in need. The impact of this hyper CSR behavior is also identified. On one hand, stakeholders and emerging markets can benefit from companies’ hyper CSR behaviors. On the other hand, however, the privacy violation and national security threats derived by such hyper CSR companies should be noted.
The thesis has answered the second research question by providing a decision making framework. Namely, when facing a decision on whether or not to engage in a cross-sectional partnership, decision makers should first set up a clear, measurable goal, and then consider the influences of the company’s stakeholders. The satisfaction of stakeholders is crucial to
the success of the business. Then，after fully analysis of various information, decision makers should choose the partner based on the qualification of the private company’s ability and the company’s institutional nature. For example, whether the company is moral oriented or business interest oriented. To make sure that the project is conducted successfully, the decision makers should also see to the alternative evaluation stage. On the alternative evaluation stage, decision makers should carefully investigate the alternatives from both quantitative and qualitative perspectives.
The article aimed to fill the research gap that derived from emerging cross-sectional partnerships by identifying the role of hyper CSR. While discussing the impact of this new type of CSR, the paper also provided a decision making framework for government decision makers to think deeper on the issue. Although not all the cross-sectional partnerships are in the form of data collection and donation, the data collecting and donating behaviors by private companies can be used as a case to discuss the role and the impact of hyper CSR. In addition to the theoretical contribution, this article has managerial contribution as well. First, by identifying the traits of hyper CSR, the public sectors can be more vigilant when choosing their private partners. Second, by offering the decision making framework, governments as well as private sectors
have more clear ideas on whether or not to engage into a cross-sectional partnership, and how to make the partnership perform successfully.
This study has analyzed the impact of hyper CSR mainly to stakeholders of the cross-sectional partnership. However, this thesis has defined the concept of hyper CSR only from data collection and donation behavior in cross-sectional partnerships. Further researchers should explore other kinds of hyper CSR and enrich this definition. In addition, companies’ hyper CSR behaviors harm not only the rights of citizens, but also the national security of the object country. However, there is little evidence on where hyper CSR can lead a country to, for example, the political and economic impact of such behavior. Further studies can focus on the political and economic impact of hyper CSR.
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