Why Data Matters for Development? Exploring Data Justice, Micro-Entrepreneurship, Mobile Money and Financial Inclusion

ABSTRACT

With the widespread extraction of very large datasets, artificial intelligence using machine learning hold the promise to address socio-economic problems such as poverty, environmental safety, food production, security and the spread of disease. These applications entail Big Data for Development in which social problems, poverty, food security and responses to climate disasters can be solved in the most efficient and effective manner. This brave new world of solving pressing problems through machine learning has several dark sides. A data divide is being created that leaves the most vulnerable populations out of the solutions being created while discriminating against those whose data is churned by obscure algorithms. Complex mathematical models together with computing algorithms produce scores that are used to evaluate the lives of the masses. These systems have scaled to enormous proportions, changing lives by affecting credit scores, job prospects and access to healthcare. The promise of fairness, transparency, cost-effectiveness and efficiency gives rise to powerful scoring algorithms that have the power to create mass devastation while discriminating against the most vulnerable. Questions arise as to: What injustices (types of injustice) are created by datafication of development? how can the injustices caused by the extraction, analysis and commoditization of data be alleviated? Who has access to and what is being done with private data? And for whose benefit or purpose is personal data being extracted? Such questions are explored through the contributions in on data justice, the use of ICTs by micro-Entrepreneurs, mobile money and financial inclusion offered through papers in this issue.

Introduction

Data may be the most valuable resource of our time. The well-worn claim that it is even more valuable than oil in powering the global economy, has meant that data is permeating almost all facets of our lives (Economist, 2017). Even if we limit our use of Information and Communications Technology (ICT), we live in an increasingly digitalized world. Businesses and governments that have access to our data are able to harness its power in ways that may control our lives. Our data is collected through our electronic devices to track our location, the social networks we connect on to track our relationships and our communication devices to track what we say to whom. The International Telecommunications Union estimates that 53.6 per cent of the global population, or 4.1 billion people, are using the Internet (ITU, 2020). This use of smart devices in our homes, offices and public places is producing large scale data from which very specific insights can be gleaned about each and every one of us. The Economist summarizes this new world of large datasets comprising of:

Smartphones and the internet have made data abundant, ubiquitous and far more valuable. Whether you are going for a run, watching TV or even just sitting in traffic, virtually every activity creates a digital trace—more raw material for the data distilleries. As devices from watches to cars connect to the internet, the volume is increasing: some estimate that a self-driving car will generate 100 gigabytes per second. Meanwhile, artificial-intelligence (AI) techniques such as machine learning extract more value from data. Algorithms can predict when a customer is ready to buy, a jet-engine needs servicing or a person is at risk of a disease. Industrial giants such as GE and Siemens now sell themselves as data firms. (Economist, 2017, p. 2)

The data generated by and of each of us is very large, personal and easily accessed by strangers. It is often referred to as ‘Big Data’ as it differs from traditional datasets in its volume, velocity and variety (Kshetri, 2014; Ali et al., 2016; Heeks & Shekhar, 2019). Heeks and Shekhar (2019) refer to the datafication of development in terms of a growing volume, velocity, variety and visibility of data, with greater use of new forms and streams of data in decision making. The widespread adoption and use of ICTs in the context of development is seen to bring opportunities for improving the lives of people in marginalized communities. This large volume of data is generated rapidly from multiple sources.

Thanks to the spatial big data that is predominantly generated through smart phones with embedded GPS receivers, our locations can be tracked with precision and our movements predicted with uncanny accuracy. The majority of this data is collected through mobile devices such as smartphones, sensors, wearable devices and cameras located in homes and public places. The variability of Big Data assumes that correlations can be considered to be pragmatic relations between variables that point to anomalies or patterns in the data (Kshetri, 2014). Complexity in this large amount of data entails multiple analysis methods that involve matching our data with other data sets that may relate to us. Such datasets may involve survey responses, tweets and hashtags on social media, camera footage and census or crime fighting data. These large varied and complex datasets power the algorithms that offer scores or solutions to the problems they are created to solve (Ali et al., 2016; Heeks & Shekhar, 2019).

Some would argue that the perverse effects of datafication for development are numerous (Masiero & Das, 2019; Heeks & Shekhar, 2019). Like the people who create them, the that power the artificial intelligence systems are fallible. Unlike the people who created them, how the scores or decisions are arrived at cannot be understood and nor can they be easily corrected. These algorithms are laden with mystery, they send some people to prison, reduce jail sentences for some felons, line some people up for cheap loans or good jobs while reducing credit scores for others forcing them out of jobs and spiraling them into poverty (O'Neil, 2016). Cathy O’Neil found that too many people suffer from models powered by algorithms. ‘They slam doors in the face of millions of people, often for the flimsiest reasons, and offer no appeal. They are unfair.’ (O'Neil, 2016, p. 40). As these models are used to address problems affecting the masses, these badly written algorithms disproportionately hurt the poor (Eubanks, 2018).

This sentiment is echoed by many authours (Dencik et al., 2016; Heeks & Renken, 2018; Masiero & Das, 2019; McCarthy, 2016; Taylor, 2017; Thatcher, 2014; Zuboff, 2015). Zuboff (2015) alerts us to the global architecture of computer mediation that produces a distributed and largely uncontested new expression of power that exile people from their own behavior while producing new markets of behavioral prediction and modification. Thatcher (2014) adds that the limits of both what can be known and done with big data lie in the hands of a relatively small group of individuals and companies that control the data value chain. This bias favoring the few is reflected in the algorithms and mathematical models. In the face of what is now known as data discrimination, many authors call for data justice and an increase protection of citizens rights (Dencik et al., 2016; Taylor, 2017; Taylor, 2019; Heeks & Renken, 2018; Masiero & Das, 2019).

New issues arise that we need to address: how is our data being extracted and analyzed? Do we know who has access to and what is being done with our data? for whose benefit is our data being extracted? and for what purpose is it being analyzed? When this data is used to address socio-economic problems such as poverty, environmental safety, food production and the spread of disease, how are the injustices caused by the extraction of our data or those that are left out, to be addressed? These questions are explored in the context of a growing area called Data for Development and the need for data justice. The contributions offered through papers in this issue are summarized in the light of these issues and avenues for future research offered in the light of what we now know.

Is data for development a Double Edged sword?

Through the recognition that Big Data may shift the way social change is pursued, Data for Development (D4D) emerged as an area of research and practice. The applications of ‘Big Data for Development’ (BD4D) span a wide variety of domains and leverage new sources of data and new analytical tools (Albanna & Heeks, 2019). Big Data is capable of providing snapshots of the wellbeing of populations at high frequency, high degree of granularity, and from a wide range of angles, narrowing both time and knowledge gaps. Subsequently a competition organized by a local telecommunications provider, Orange, in Cote D’Ivoire helped accelerate the D4D momentum. In recognizing the power of its data, Orange wanted to gain new insights into the socio-economic development issues facing that country. The challenge illustrative of D4D, was to contribute to the socio-economic development and well-being of the Ivory Coast population. Participants were given access to four mobile phone datasets: The datasets are: (a) antenna-to-antenna traffic on an hourly basis, (b) individual trajectories for 50,000 customers for two-week time windows with antenna location information, (3) individual trajectories for 500,000 customers over the entire observation period with sub-prefecture location information, and (4) a sample of communication graphs for 5,000 customers (Blondel et al., 2012). While the customer data appeared to be anonymous, vast amounts of their individual data including call detail records were made available to researchers around the world. Winning research from the Data 4 Development challenge included topics such as visual analysis of mobile phone data, urban mobility and optimizing public transport and analyzing social divisions using cellphone data (Clausen Nielsen, 2013).

While these are important advancements in improving the lives of people credited to data analytics, there is still the need for a deeper understanding of social, political, and economic contexts that facilitate and inhibit the effective utilization of data analytics in key development areas (Kshetri, 2014; Heeks & Shekhar, 2019; Masiero & Das, 2019). Ali et al. (2016) identify an important issue in gaining access to important people-related data, which is often in the exclusive access of the government in the form of paper documents. Usable data is often stored and owned by telecommunications operators who are often reluctant to share this data (Kshetri, 2014). A trend in D4D is known as ‘open data’, which promotes open public sharing of data from various public and private sector entities in searchable and machine-readable formats (Ali et al., 2016).

While D4D research is generating a buzz amongst development agencies, Thatcher (2014) argues that Big Data is both a sociotechnical and epistemic process that involves the rapid combination, aggregation, and analysis of data in which researchers are tacitly accepting a commodification and quantification of knowledge. The analysis of big datasets offers meaningful insights about our actions and preferences. Connected to these big datasets are additional data generated by the multiple devices we use in our daily lives. The term ‘living on fumes’, coined by Thatcher (2014), demonstrates that there are additional datasets with information being extracted that add to our digital footprints. He states:

Taken directly from current big data research, digital footprints refer to information that is given off by actions humans are already taking. The information contained in these footprints, which may be generated through purposeful action or passive recording, is believed to offer deep and novel insights into both individual experience and society as a whole. A subset of digital footprints called data fumes refers to information given off through the use of already existing applications. (Thatcher, 2014, p. 1769)

The large amount of geo-spatially tagged data gives governments and corporations the ability to understand complex social problems in a data driven manner. For example, governments in India and China are making plans to develop smart cities in which citizens can be tracked at all times. It is evident that through the examination of spatial big data sets, meaning beyond the end user’s intent may be found (Thatcher, 2014). In fact, combined with artificial intelligence techniques and facial recognition software, the rights of citizens to voice their opinions or dissent of their governments can be significantly restrained.

On the other side of the data for development sword is what Zuboff (2015) has coined as surveillance capitalism which aims to predict and modify human behavior as a means to produce revenue and market control. According to Zuboff (2015), big data is the foundational component or the raw material in the logic of accumulation of surveillance capitalism which depends upon the distributed global architecture of the internet. She adds that big data is founded on the formal indifference to the populations that comprise both its data sources and its ultimate targets. In her use of the term ‘Big Other’, which she identifies to be

constituted by unexpected and often illegible mechanisms of extraction, commodification, and control that effectively exile persons from their own behavior while producing new markets of behavioral prediction and modification. Surveillance capitalism challenges democratic norms and departs in key ways from the centuries long evolution of market capitalism (Zuboff, 2015, p. 1)

Google, according to Zuboff (2015) is the creator of surveillance capitalism. Through the extraction of raw material or big data, our voices, search queries, mails, home sensors and location, Google is able to extract behavioral data to fabricate predictive products. Through data extraction and analysis, Zuboff identifies a new logic of accumulation. This logic of accumulating data assets through ubiquitous automated operations takes place without the consent or knowledge of those whose data is being collected. Companies offer devices and access to software for free to the most vulnerable populations in return for their data. Refugee populations are tracked through their cellphones, victims of climate change given free equipment so their data may be collected and behaviors modified to the benefit of the corporations. Local micro-entrepreneurs cannot compete with products given to their customers for free by foreign corporations. Unless local micro-entrepreneurs are able to tap into their local markets, they fall quickly into poverty.

In addition to being valuable resources for businesses, these data assets are also being used by public institutions to monitor and control their constituents. The Chinese government uses facial recognition software that uses machine learning to recognize people. They use cameras to extract face prints from passers-by in the streets of large cities like Guangzhou and Shenzhen. Boxy vending machines at airports accept payments by scanning faces. At the heart of facial recognition data analytics is the contextual information connected to the images. Chinese companies like Megvii and SenseTime have a sprawling digital infrastructure through which data are collected, cleaned and labelled before being processed into the machine-learning software that makes face recognition tick. The Chinese government is successfully using this infrastructure to score its citizens, identify dissidents and break up mass protests. The result is discriminatory treatment of innocent citizens (Economist, 2020).

Big data discrimination

Data data-driven discrimination as Taylor (2017) refers to, is advancing at a similar pace to data processing technologies, awareness and mechanisms for combating it are not. She adds that the trends that ‘make developing a global perspective on the just use of digital data urgently necessary: one is the exponential rise in technology adoption worldwide, and the other the corresponding globalisation of data analytics.’ (p. 1). This changes the way we perceive development. Sustainable development is defined as development that meets the needs of the present without compromising the ability of future generations to meet their own needs (United Nations, 2020). The United Nations offers a set of sustainable development goals that appear well intentioned at best and like their predecessor the Millennium Development goals, problematic when it comes to implementing policies and procedures directed at achieving these goals. Data is important for the measurement and the delivery of the United Nations Sustainable Development Goals. Despite a few success stories, the growth of the world economy has offered great prosperity for those who have access to the internet, mobile phones and markets they offer while leaving out those who cannot afford to purchase or use ICTs. The data divide has meant that those whose data cannot be extracted nor analyzed are invisible to policymakers and agencies tasked with implementing development efforts.

When we add technological innovations to the mix, the challenges compound to new levels. Those who do not have access or use ICTs become invisible to the data analysis used to guide policy makers and businesses working to achieve development goals. On the other hand, refugees’ every move is tracked through their cellphones and their behaviours analysed with precision based on how people use the internet, social media or pay for goods and services. Taylor and Meissner (2020) discuss the marketization of migration statistics as follows:

A big data analysis of migration flows involves a computational formalisation of central notions. What characterises a migrant, what forms of mobility are of interest to the modelling process, and what constitutes risk must all be formalized through proxies using available variables in the datasets chosen. … Big data analytics, applied to migration, tends to produce modes of mapping that are colonial in the way they proxy, sort and categorise. They thus perpetuate unequal and exclusive power structures, and at the same time they become harder to criticise when data analytics promise the ability to predict and optimize migration and its implications. (p. 5-6).

The widespread extraction, analysis and use of Big Data sets has given rise to two new types of discrimination which disproportionately marginalize the poorest populations of the world. The first form of discrimination arises from the data divide which leaves those who have no access to or use of digital media invisible to businesses and policy makers. The data divide effects about 3 billion people worldwide who reside outside the scope of data extraction and analysis. As they have no digital footprint, they are not represented in the new growing datasets used to glean insights into the socio-economic challenges we face. Even though far more of the global population are within scope of the new datasets, those who are not become invisible (Taylor, 2017; Dencik et al., 2016). These digital inequalities often referred to as the ‘big data divide’ according to McCarthy (2016) posits a split between those who have access and ownership to large-scale distributed datasets and those that do not. The growing issue of the data divide relates to those who are not being recorded leaving out their income, gender, geographic locations, living conditions and health.

The second form of discrimination is in the rapid growth of algorithmic discrimination which hunts out patterns in big data. Cathy O'Neil (2016) points out that at the heart of the Big Data Divide are the injustices caused by the algorithms based on sloppy statistics and biased models that create their own feedback loops. For example, algorithms designed to calculate prison sentences grant disproportionately harsher higher sentences to the poor, making it difficult for them to get jobs once they are released causing them to fall into greater poverty. As income and employment are some of the factors algorithms use in their calculations, the scores reduce the likelihood of these same people from qualifying for credit or rental housing thereby creating further homelessness. These, she terms are Weapons of Math Destruction (WMD) exacerbating inequality and punishing the poor. Even when designed well, the logic upon which these algorithms are powered is opaque. With the power of computing they can be scaled to cover entire populations of prisoners, school children, micro-entrepreneurs/borrowers seeking credit, professionals such as teachers and nurses whose livelihoods can be created or destroyed through a single calculation (O'Neil, 2016; Tisne, 2018).

The analysis of Big Data is crucial to revealing actionable information about us. It involves machine learning techniques that can reveal meaningful information about us or at least insights that we ourselves were not aware of. Ali et al. (2016) see opportunities from the rapid advances in the use of intelligent data analytics techniques that are drawn from the emerging areas of artificial intelligence and machine learning, provide the ability to process massive amounts of diverse unstructured data that is now being generated daily to extract valuable actionable knowledge. Regardless, the data is not neutral and nor are the techniques used to analyze these rapidly growing Big Data sets. The analysis techniques can be used to paint different views of us depending upon how the data is being analyzed. The meaning attributed to the data analyzed about us offers insights into what we perceive to be important and to what extent our thoughts and actions can be influenced by the information offered to us through the devices we use.

Development needs data justice

Data for development needs data justice. The concept of Data justice according to Taylor (2017), relates to the fairness in the way people are made visible, represented and treated as a result of their production of digital data. In particular, the surveillance of innocent citizens by governments through big data-driven surveillance programmes puts the rights of innocent citizens at risk (Mann, 2018). Dencik et al. (2016) add that:

regimes of governance and control have increasingly been based on digital infrastructures that facilitate ‘dataveillance’ – a form of continuous surveillance through the use of (meta)data. These regimes are rooted in the economic logic of ‘surveillance capitalism’ in which accumulation is pursued through the ability to extract, monitor, personalize, and experiment based on the pervasive and continuous recording of digital transactions (p. 3).

When the digital transactions that are recorded are based on data inequalities, then the results of the data analysis may not offer desired outcomes. Heeks and Renken (2018) refer to the disbenefits or negative developmental impacts of datafication which include growing surveillance and loss of privacy, capture of development gains by private corporations, and growing inequalities: especially a relative loss of power for individual citizens and civil society.

The first paper in this issue by Jonathan Cinnamon is entitled Data inequalities and why they matter for development addresses this very important issue. The author contends that key message underlying this growing and diverse body of research is that the various practices involved in producing, accumulating, and analyzing data have significant implications for democratic societies, since they produce inequality of opportunity and harm. In short, data inequalities matter he states and the growing discourses in the emerging field of ‘data for development’ (D4D) – as part of the wider domain of information and communication technologies for development (ICT4D) – ascribe power and agency to data as a development actor. This involves investing data with the capability to enhance evidence-based decision making, strengthen transparency and accountability, measure development progress, and through this, improve living standards and equality both within and between nations.

The authour posits that the ‘data revolution’ marks a time of growing interest and investment in data – big, small, or otherwise. Critical attention to data is also proliferating, exposing the diverse ways that data produces inequality of opportunity and harm in society. This paper draws the nascent field of critical data studies into conversation with emerging narratives in data-for-development (D4D) to advance the conceptualization of data inequalities, explaining how they both align with and diverge from core tropes of digital inequalities research – and why this matters for development. The paper examines the causes, consequences, and potential solutions to three ‘data divides’ – access to data, representation of the world as data, and control over data flows – through examples of digital identity systems and national data infrastructures, user-generated data, and personal behavioral data produced through corporate platforms. This understanding provides a basis for future research, practice, and policymaking on data-related (in)equalities in development contexts and beyond.

Sylvain Cibangu authou’rs the second paper in this issue entitled ‘Marginalization of Indigenous Voices in the Information Age: A Case Study of Cell Phones in the Rural Congo’. The author states that while awareness about the study and empowerment of indigenous, marginalized groups has been brought to light since the 1960s with postcolonial movements, research into marginalization and individuals thereof has been left to arcane, outdated disciplines; and thus, has yet to take root in the wider scientific community and industry. As stated by the author, the world’s most marginalized individuals tend to be described in broad strokes as mere passive recipients and second-hand beneficiaries of (certainly well-intentioned) ICT projects and research designs. ICT – and indeed any – researchers, cannot make the world a better place by using or (dis)regarding the world’s poorest as passive agents of their own lives and of our own research. Certainly, existing ICT bodies of work are awash with a rhetoric of ICT – and most notably cell phone – potentials for development.

Cibangu adds that with upgraded wearables on the rise, most information and communication technologies [ICTs] research describes marginalization as a lack of access to technology, leaving aside the marginalized and their lives. For example, while cell phones are becoming the most ubiquitous devices of our times, they need masts and their guards in order to best function. Using the capability approach, this study conducted open-ended interviews with 16 mast guards in the rural Congo to inquire into their lived experiences about ways in which cell phones generated development. The paper proposes the working, living conditions of concerned individuals as a research lens. It fits well with Taylor’s call to address the way people are treated in the public and private sector (Taylor, 2017).

Micro-entrepreneurship

In their analysis of the economic lives of the very poor, Nobel Laureates Banerjee and Duflo (2007), state that extremely poor households in rural areas tend to own very few durable goods such as bicycles. Most extremely poor households have a bed or a cot, but only about 10 percent have a chair or a stool and 5 percent have a table. They found that about half have a clock or a watch, fewer than 1 percent have an electric fan, a sewing machine, a bullock cart, a motorized cycle of any kind, or a tractor. No one had a phone. Many extremely poor households operate their own businesses, but do so with almost no productive assets. Yet their lives are invisible from the aggregate data collected and analyzed through the use of mobile phones.

The aggregate data suggests that when micro-entrepreneurs incorporate and use ICTs, visible and measurable outcomes such as improved operational efficiencies as well as increased revenues result. Such outcomes then enable these businesses to have a better positioning in the markets that they serve. It has been seen that when businesses used e-mail to communicate with their customers, they experienced sales growth 3.4 per cent greater than those businesses which did not (Qiang et al., 2009). The same study also pointed out similar outcomes for productivity and reinvestment. In other words, both these components were found to be greater for more intensive users of ICT (Qiang et al., 2009). In a study by Raymond et al. (2005), it was observed that a 4% increase in sales as well as 5% increase in export performance was obtained when e-business techniques were adopted by micro-entrepreneurs in the manufacturing sector in Canada. Specifically, Raymond et al. (2005) mention that by using technologies such as websites, email and telephones to communicate with customers, micro-entrepreneurs can provide better customer service as well as expand their customer base to help reach out to both local as well as international consumers for their products. Increased utilization of ICTs is not always evident through increased revenue of businesses.

Although it has been established through prior research that ICTs can bring about substantial benefits to firms, uptake of ICT has been extremely slow or rather lacking within micro-enterprises. In a study by Qiang et al. (2009), among the micro firms, only 27 percent use e-mail and 22 percent use Web sites to interact with clients and suppliers. And so the findings beg the question: If computer use affects firm productivity and IT expands networking within sectors and industries, the micro firms may not be benefiting from these externalities. There have been a number of studies that have looked at the various challenges that small businesses face. One issue is that of affordability. Small businesses operate on very constrained and limited financial terms and do not have sufficient capital to invest towards ICT. Awareness about ICT is another core problem. Most often micro-entrepreneurs do not possess any technical skills and are unaware of the capabilities that ICT may bring to their business. Infrastructure is a basic need for any form of ICT implementation to work. Lack of such infrastructure will be a major barrier to the adoption and use of ICT within the business. Private/Government sectors in any community also play an important role in either facilitating or inhibiting the development of ICT infrastructures to promote increased ICT adoption and use. The capacity to incorporate ICT into small business environments are also a major component in successful ICT adoption and use. Most of these studies refer to challenges that are tangible. An important aspect to the successful adoption of IT within the micro-enterprise environment has to do with intangible issues such as perceptions and attitudes that the micro-enterprise owners have towards technology (Qureshi et al., 2009; Wolcott et al., 2008).

The third paper in this issue co-authored by Yee Kwan Tang and Victor Konde is entitled ‘Differences in ICT use by entrepreneurial micro-firms: Evidence from Zambia’. According to the authors this paper is a response to Harris’s (2016) call to take practice and policy influence more seriously, by engaging more closely with the users of their research, by encouraging more and better communications with the public, especially through the use of ICTs. This study follows a systematic review, to collate the findings of the ICTD research in the area of urban microenterprises in the developing world to aid policy-makers. The research question raised is as follows: Does access to business-relevant information through the networked devices enhance the internal efficiency and business growth of the urban micro, small, and medium enterprises (MSMEs) in low- and middle-income countries?

The authors state that micro-firms are important for creating jobs and income in developing economies, but these firms face significant constraints, some of which could be ameliorated through ICT. However, it remains unclear which specific ICT uses are intensively employed by different entrepreneurial micro-firms. Notwithstanding external constraints, we examined differences in ICT use by comparably sized micro-firms operating in the same environment that exhibit different entrepreneurial attributes (proactiveness, innovativeness, risk-taking, and growth orientation). Using data from Zambian micro-firms, their findings demonstrate that the four entrepreneurial attributes have a positive yet different influence on three individual categories of ICT use: information and network access; online transaction and interaction; and in-house operations. They pinpoint which ICT applications will likely benefit entrepreneurial firms. Their findings could help researchers and policy-makers to target specific categories of ICT use that drive firm growth and nurture the desirable business behavioral orientations for deploying technology in business.

The fourth paper in this issue is co-authoured by Alejandro Cataldo, Gabriel Pino and Robert McQueen and is entitled ‘Size matters: the impact of combinations of ICT assets on the performance of Chilean micro, small and medium enterprises.’ Their paper studies the effects that different combinations of ICT have on the performance indicators of microenterprises and SMEs (MSMEs) of a developing economy. The authors focus on two questions: First, whether the impact of ICT differs according to different ICT assets used in companies? Second, whether the effects of these combined ICT change according to the company size, especially for MSME in emerging countries? Based on RBV and TOE, they posit that different combinations of ICT have different effects on performance indicators of MSMEs. To test this hypothesis, they used a survey of 5519 Chilean companies. Three major conclusions can be drawn from their results. Firstly, ICT combination in the MSMEs follows a four-stage maturity model. Secondly, each stage has positive effects on MSMEs’ revenues and profits. Thirdly, the organization’s size moderates the impact of ICT on productivity: the smaller the company, the more significant the benefits of ICT assets. The authors contend that the relationship between ICT and productivity for companies in developing countries has not been well established, especially for micro, small, and medium enterprises.

Samwel Macharia Chege,Daoping Wang and Shaldon Leparan Suntu co-authour the fifth paper in this issue entitled ‘Impact of information technology innovation on firm performance in Kenya.’ The authours contend that Information and Communication Technology (ICT) is driving modern employment creation with networking sites enabling people to interact through innovation. However, ICT uptake and implementation differ due to moderating factors such as entrepreneur innovativeness, which enhances how technology innovation impacts organizational performance. This study seeks to close the research gap by assessing how information technology innovation influences firm performance in Kenya given the low level of technology innovation and the rate of business closure. This study investigates the following questions: Does information technology innovation always lead to improved firm performance? What role do entrepreneurs play in information technology innovation to improve firm performance?

In this study, the authors examine the association between technology innovation and firm performance in Kenya by considering the impact of entrepreneur innovativeness on this association. A sample of 240 enterprises and structural equation modeling were used in the analysis. The findings indicate that technology innovation influences firm performance positively. The study recommends that entrepreneurs should develop innovative strategies to actualize firm performance. Government policy should aim at improving ICT infrastructure; promoting small and medium-sized enterprises’ (SMEs’) technological externalities within the industry, and establishing ICT resource centers to support SME performance. The study’s findings enrich existing theories and contribute to business management practices in both developed and developing countries.

Mobile money and financial inclusion

As the global economy expands, wealth is not distributed equally. It is argued that the divide between rich and poor continues to widen with only 10% of the world’s population holding 80% percent of its wealth. For example, in the United States the GDP is at an all-time high as is its debt, but house hold formation is declining as is life expectancy the ability of people to lead better lives. China on the other hand is the most populous country in the world and according to the World Bank is also the largest economy in the world based on Purchasing Power Parity (World Bank, 2020). This means people in China can buy more goods and services with their income than can people in the US. Even though China is growing almost 3 times the rate of the US, 30.46 million Chinese live in what their government classifies as poverty (Jennings, 2018). The US census bureau estimates that 38.1 million people in the US live in poverty (2018). Both countries are achieving a declining rates of poverty, one is considered developed (US) and the other, China is considered developing.

In order to be able to participate in the markets, micro-entrepreneurs need access to credit. Banerjee and Duflo (2010) found that with the exception of Indonesia (where there has been a large expansion of government-sponsored microcredit), no more than 6 percent of the funds borrowed by the poor came from a formal source. The vast majority of credit comes from moneylenders, friends, or merchants who charge high rates. Banerjee and Duflo (2010) argue that the field of development economics has gained unprecedented centrality because it offers the opportunity to integrate theoretical thinking and empirical testing, and the rich dialogue that can potentially take place between the two. The culture of development economics, they add is particularly well-suited to this tight integration because of the emphasis on collecting one's own primary data based on the theories that are to be tested. Development economics as an area of research and practice has been influential in the reinterpretation of the original development agenda set by Brundtland report on sustainable development. The conceptualizations of sustainable development have historically revolved around the needs of industrialized rather than developing countries (Barkemeyer et al., 2011).

The concept of development is evolving from measurement of growth and productivity at an aggregate level where the GDP of countries is high but the people remain poor or unable to lead the lives they choose to live. When the concept of development is measured in social and economic terms, a prominent development economist Amartya Sen, has been very influential in helping conceptualize this complex phenomenon. If people have the freedom to lead the lives they choose to live, then development can be seen to take place (Sen, 1999). However, the role ICTs, especially machine intelligence have had the effect of dampening the effects of productivity on the lives of people and their ability to partake in economic opportunities to lead the lives they choose to live. Whadcock (2014) argues that the dramatic dip in productivity growth after 2000 seems to have coincided with an apparent acceleration in technological advances as the web and smartphones spread everywhere and machine intelligence and robotics made rapid progress. Given the rapid technological growth, the poor become increasingly invisible and thus unable to participate in economic life.

Xiaoqing Li, Xiaogang He and Yifeng Zhang co-authour the sixth paper in this issue entitled ‘The Impact of Social Media on Business Performance of Small Firms in China.’ The authors contend that small firms have become an important driving force of China’s economy. Small and medium-sized firms contributed over 65% of the gross domestic product (GDP), over 50% of tax, over 68% of export, and 75% of employment in China. However, small firms are in a disadvantageous position on the market. In China, small firms lack information resources about markets, competitors, customers, and business partners. These entrepreneurs need to access information from different sources when they make business decisions. The possible information sources include different levels of government, business partners, and customers. In order to support the development of small business, one important task is to create a fair competition environment in order for small firms to get timely information and benefit from government policies. Therefore, it is critical to effectively deliver the required information to people running small businesses.

In this paper the researchers conducted an empirical study on the impact of information from social media on the business performance of small enterprises in China. They illustrate an empirical study conducted on the impact of major types of information gained from social media on the business performance of small enterprises in China. The paper presents a review of the related literature about the development of social media in China. In addition, it presents the research model the authors utilized, related hypotheses, the research methodology, and data analysis. Findings indicate that the information regarding government and industry policies has a significant impact on business performance. In addition, gender and education of entrepreneurs have moderating effects on the impact of information to business performance.

Devendra Dilip Potnis, Aakanksha Gaur and Jang Bahadur Singh co-authour the seventh paper in this issue entitled ‘Analysing Slow Growth of Mobile Money Market in India Using A Market Separation Perspective.’ The authours suggest that a majority of studies on the factors influencing the use of mobile technology for finance focus on mobile banking. In contrast, this study focuses on mobile money services and their use by customers. Since the application of mobile technology for financial services can contribute to the economic development of developing countries, it is critical to examine the inhibitors to using mobile money service in countries like India, which have an exceptionally low uptake of this service. Mobile money service enables the customer to carry out financial transactions over a mobile phone without requiring them to own a bank account. By adopting a market separation perspective, this theory-driven, exploratory study proposes and tests a rare event logistic regression model for using mobile money services in India. The analysis of 45,036 responses shows that the ownership of a SIM card (temporal separation), income and ownership of a bank account (financial separations), awareness of mobile money services (information separation), age and gender (social separations), and location of residence (spatial separation) significantly inhibit the use of mobile money services. Implications are discussed at the end.

Abhipsa Pal, Tejaswini Herath, Rahul De’ and Raghav Rao co-authour the eighth paper in this issue entitled: ‘Contextual facilitators and barriers influencing the continued use of mobile payment services in a developing country: Insights from adopters in India.’ The authors contend that in spite of the successful cases of mobile payment and financial services like M-Pesa in Kenya and GCash in the Philippines, worldwide adoption of mobile payment has been considerably low, especially in the developing regions, including India. In November 2016, the launch of ‘demonetization’ by the government of India, invalided 86% banknotes in circulation and created a temporary cash crisis. Mobile payment usage surged in the immediate aftermath, and again subsequently dropped within 6 months when banknotes were back in circulation. The authours identify evidence that the overall mobile payment usage continues to be on the increasing trend in India.

The authours argue that mobile payment services hold the potential for financial inclusion in developing economies. Low-income countries are characterized by distinctive conditions like price sensitivity, low digital penetration, high risk of failure, and competitive emerging markets, which further influence mobile payment usage. They develop a research model to identify the contextual facilitators (like price benefit, network externalities, trust, and habit) and barriers (like risk, lack of facilitating conditions, and operational constraints) driving mobile payment usage intention. They test the model using data from 298 survey respondents from India who had adopted and were currently using mobile payment services. The factors that facilitate or constrain users’ intention to continue using mobile payments are essential in understanding the technology’s sustenance and its future in enabling financial inclusion.

In the ninth paper in this issue entitled Do Mobile Financial Services Ensure the Subjective Well-being of Micro-Entrepreneurs? An investigation applying UTAUT2 model, Syed Abidur Rahman, Mirza Mohammad Didarul Alam and Seyedeh Khadijeh Taghizadeh suggest that information technology has been sensed as a tool for development which is influencing human well-being. They ask the question: can people from marginalized group of the society (who are having much lower income than the societal average, deprived access to basic utilities, and commonly excluded from participation in the social activities) accept and use financial services by means of mobile technology? Another unanswered question they investigate is how accepting and using mobile financial services can nurture the well-being of the poor people? Mobile financial service is a tool that allows individuals to make financial transactions by using mobile phone technology. Their research examines the influence of the UTAUT2 model on the subjective well-being of the bKash agents (micro-entrepreneurs) who belong to an underdeveloped societal group. Data were gathered from the bKash agents in Bangladesh with a response rate of 37.5% and was analyzed by SEM-PLS3.0 statistical software. The results reveal that price value strongly predicts behavioral intention for accepting and using mobile financial services along with other factors. Most importantly, the result suggests that the usage behavior of mobile financial services influences the subjective well-being of the respondents. Adjoining the concept of subjective well-being with a unified theory of acceptance and usage of technology is the paper’s uniqueness to the ‘development’ knowledge domain.

Conclusion

This issue highlights the need for research into the ways in which data are extracted, analyzed and commoditized as new markets are being created for the products of data analytics. It highlights the need for data justice, especially in the context of development. It highlights the emerging area of data for development in which big data is being used to solve socio-economic problems. Data discrimination is identified as a product of this and the need for data justice highlighted. The questions relating to the injustices caused by the extraction, analysis and commoditization of data be alleviated are explored in the context of data for development. The papers in this issue throw light on data justice, the use of ICTs by micro-Entrepreneurs, mobile money and financial inclusion.

Acknowledgements

We would like to thank Associate Editors Richard Heeks and Peter Wolcott for their insightful and valuable feedback on earlier versions of this editorial. We are also very grateful to Communications Editor Silva Masiero for her thoughtful comments and valuable additions to the references. It is through the reviews of editors like them, that the quality of papers published in this Journal continue to be strong.