Tag: Digital Transformation

Data and development, Digital agriculture, Digital economy, Digital geopolitics, Digital society, Digital transformation: New Research Outputs from CDD, Manchester

~ Richard Heeks ~ Leave a comment

Recent outputs on Data and development, Digital agriculture, Digital economy, Digital geopolitics, Digital society, Digital transformation – from Centre for Digital Development researchers, University of Manchester

Data and Development

  • Data Justice for International Development (open access)
    by Richard Heeks
    Develops a new and comprehensive model of structural data justice that is shown to be of particular value to critical data studies in understanding how both “power over” and “power to” are exercised in data-intensive development.

Digital Agriculture

  • ICT and Rural Development in the Global South
    by Katarzyna Cieslik
    This review highlights the strengths of the recently published ICT and Rural Development in the Global South, praising its grounded evaluation of digital development initiatives while questioning its limited engagement with rapidly advancing AI and machine learning technologies. As ICT4Agr evolves, the piece asks whether user intent and capacity still determine the outcomes of digital tools, or whether emerging technologies are reshaping that equation in uncertain ways.

  • Platformization of Rural Africa (open access)
    by Katarzyna Cieslik and colleagues
    Marks the launch of the UMRI project ‘Platformisation of Rural Africa: Dependency, Dispossession and Data in the Platform Economy’.  The project explores how digital agritech platforms are transforming farming and data governance across Sub-Saharan Africa, asking a vital question: what happens to farmer autonomy in a datafied agricultural landscape?

  • Uberisation of Mechanisation: Exploring Digital Tractor Hire Platforms in Ghana with Actor-Network Theory (open access)
    by Ebenezer Ngisah, Cees Leeuwis, Katarzyna Cieslik & Comfort Freeman
    Looks at the dynamics of a donor-supported digital matching platform in north-western Ghana that sought to connect smallholder farmers with tractor services. Using narrative interviews and an actor-network theory lens, it shows how existing brokerage systems and local practices shaped farmers’ engagement, emphasizing the importance of designing digital agricultural tools that work with established networks.

Digital Economy

Digital Geopolitics

  • Analysing the US-China “AI Cold War” Narrative (open access)
    by Yujia He & Richard Heeks
    Examines how the rhetoric of “AI Cold War” emerged and how – driven by securitisation, militarisation, and big tech interests – it can distort global understandings of AI development and international relations.

Digital Society

Digital Transformation

Digital Transformation in Chinese State-Owned Enterprise: The Tension Between Political Compliance and Business Needs

~ Yingqian Wang ~ Leave a comment

Digital Transformation has become essential for organisations worldwide. State-Owned Enterprises (SOEs) in China are large-scale companies where the government maintains majority ownership and significant control. Unlike private corporations or public sectors, Chinese SOEs occupy a unique hybrid position: they must generate profits and compete in markets like private companies whilst simultaneously fulfilling political directives and social objectives like government departments. This “dual identity” creates distinctive pressures—balancing commercial performance with policy compliance. This unique dynamic creates special challenges during digital transformation initiatives, as illustrated in the case study of “H Company“:

Stage 1: Political Triggers and Top-Down Instructions

The story begins with political signals—government policies like the “Digital China Strategy – a national initiative to promote digitalisation across all sectors of the Chinese economy and society“, and competitive pressure from other SOEs. Senior managers were the first to notice external policies and some unpublished draft regimes, aware of the changing regulatory trends, and therefore understood the digital transformation of SOEs as part of the implementation of the national strategy.

Leadership took a narrow, top-down approach: Regulatory directives were mechanically relayed downward without contextual adaptation.

As the top manager remarked, “When the Central Committee issues instructions, our only response is immediate downstream transmission.” At this stage, the key focus is to communicate and implement policy requirements, showing the dominance of “political coherence”, and that following instructions is more important than practice.

Stage 2: Fragmented Understanding Among Business Units

When middle managers received the directive from senior leadership, they naturally looked for practical applications—ways this initiative could solve their actual operational problems. However, as a senior leader stated:

“We (the managerial team) must ensure that all business units designate their accountable person as the information interface to collaborate with the project team—just one person, avoiding group discussions…they’re too troublesome.”

Without cross-departmental communication, business units each made sense of the digital transformation initiative in their own way, and their interpretations remained isolated within separate units.

This resulted in:

  • Fragmented understanding, with departments working separately and prioritising local efficiency over holistic efficiency

This reflects a corporate culture that “sees co-construction as inefficient,” where accountability flows strictly upward rather than across departments.

Stage 3: Managing Differences Through the Transformation Team

The newly formed project team held the organisation’s most strategic yet challenging position, responsible for gathering all information and evaluating divergent opinions and conflicting expectations. Through various meetings, they gathered opinions from both senior management and business units, becoming the convergence point of information between all stakeholders.

This team faced conflicting demands that couldn’t be reconciled with a limited budget:

  • Leadership wanted impressive results “high visibility-such as digital dashboards that looked impressive in presentations but provided little operational value or even added more workload to business units” that they could report to higher authorities
  • Business units needed practical, functional solutions to improve daily operations

Figure 1: Comparison of Differences Between Senior Managers and Business Units

In H company, the compromises favoured ‘high-visibility’ projects over practical value, as the project team believed that success depended on leadership’s satisfaction rather than actual user adoption.

This situation revealed how Company H maintained traditional hierarchical power structures while appearing modern. The project team’s technical expertise mainly served to legitimise leadership decisions through “digital” branding.

Stage 4: Meaning Verification and Executive Renegotiation

Senior managers “verified” the project team’s designs by asking: “Will this look good in our State Assets Commission (their supervisory institute) report?” They made minimal adjustments that preserved leadership’s original narrative.

During the design review and confirmation stage, communication was confined to the senior managers and the project team, as both understood that the ‘Digital Transformation’ was a flagship project that would never fail. This tacit understanding effectively ignored the concerns of operational staff.

Stage 5: Cognitive Dissonance and Parallel Systems

When the final transformation designs were officially published, business units discovered their actual needs had been largely ignored.

Their responses varied:

  • Some resisted: “This design is completely unusable“
  • Most developed workarounds—completing tasks twice: once on the official system for compliance, and again using their original tools for actual work

This created two parallel meaning systems:

  • The public narrative presented transformation as progressive and necessary
  • The private narrative dismissed it as “leadership’s vanity project“

This dysfunction continued without correction—problems were routinely dismissed as “growing pains” even after six years of implementation.

Further Reflection: The Logic Behind Narratives and Results

The unidirectional closed flow mechanism and ceremonial implementation dual-track system in H company digital transformation sensemaking are formed and sustained by a series of deep-rooted organisational and institutional factors. The primary factor lies in SOE’s unique leadership authority culture and political responsibility system, tightly linking project success with leadership performance, leading to digital transformation project evaluation appearing as a separation between political standards (obtaining government‘s commendation, conforming to policy orientation) and business standards (improving efficiency, solving practical problems). This separation provides soil for the dual-track meaning system, allowing projects to operate simultaneously in two evaluation systems.

Secondly, the specialisation of technical knowledge and knowledge barriers make it difficult for decision-makers to fully understand the essence of digital transformation, exacerbating the structural contradiction of “authority without information” and “information without authority”. This power-information asymmetry causes different levels to form partial understandings based on their limited information, difficult to integrate into unified cognition. Simultaneously, organisational legitimacy pressure makes superficial ceremony a necessary means to obtain political resources and legitimacy, even if disconnected from actual business needs.

These factors create a self-reinforcing pipeline: Different positions within the company lead to different understandings; lack of information sharing deepens these divisions; and the rigid power structure makes it difficult to resolve these differences. The result is two parallel systems operating simultaneously – one for political reporting and another for actual business operations. As a result, two separate systems of understanding emerge. Although this mechanism appears inefficient and troublesome (from business and management perspective), it can be seen as a rational adaptation for SOEs operating under specific institutional contexts, allowing them to respond to multiple pressures simultaneously, maintaining a certain degree of flexibility within strict hierarchical and bureaucratic context-which they are unable to change.

Why Should We Care About These?

For practice: Just a reminder: when digital transformation sets aside actual business and operational requirements, it creates an illusion of progress whilst concealing fundamental organisational dysfunction. This misalignment wastes valuable resources and perpetuates systemic inefficiencies. The emergence of parallel systems—official compliance alongside hidden workarounds—demonstrates how employees adapt to poorly designed initiatives, albeit at significant cost: duplicated effort, growing cynicism, and diminished trust in leadership.

For theory development: This case presents valuable opportunities to extend Western organisational studies theory into developing contexts. It reveals that sensemaking is not simply a linear cognitive process but deeply political; not merely collaborative but often divisive; not universally consistent but culturally and institutionally contingent. Future theoretical frameworks must account for these power dimensions, institutional embeddedness.

For this project, the researcher’s primary focus explores how the process of Digital Transformation—not merely its outcomes—shapes employee wellbeing, with this blog serving as a reflective interlude within the broader research project. Connecting to my research objective, digital transformation’s constructive process influences individual sensemaking of workplace change. Employees participate in, observe, and interpret the digital transformation process as cues, categorising digital transformation-related changes as either job demands (e.g., redundant systems, performative compliance) or job resources (e.g., tools that actually enhance productivity). These interpretations trigger psychological responses—stress, disengagement, or resilience—depending on how individuals navigate this dual reality based on contextual scanning.

Note: This article adapts field observations from a state-owned enterprise case study, anonymised as “H Company.” All names, quotes, and references to internal documents are sourced from focus group discussions, participant interviews and observational field notes.

A New Tool to Strengthen Digital Transformation Strategies in Development: The DX4D Scorecard

~ Richard Heeks ~ Leave a comment

Digital transformation is reshaping development practice, but how can organisations ensure that their strategies are truly transformative, contextually grounded, and development-focused?

My new working paper, co-authored with Jaco Renken, presents a practical answer: the DX4D Organisational Strategy Scorecard.

We introduce a co-produced tool designed to help development organisations evaluate and improve their digital-transformation-for-development (DX4D) strategies. Drawing on both academic insight and practitioner experience, the DX4D Scorecard addresses a growing need: while many development actors have created digital transformation strategies in recent years, most lack a structured framework tailored to the specific needs and challenges of development contexts.

Why a Scorecard for DX4D?

Until now, guidance on digital transformation has been dominated by literature focused on private-sector, Global North settings. This leaves a gap for development practitioners in the Global South and international development organisations, who need tools rooted in the realities of development work.

The DX4D Scorecard (shown below) responds to this gap. It was co-developed through an iterative process involving literature review, strategy analysis, and engagement with international development NGOs at the 2024 ICT4D Conference in Accra. The result is a concise but comprehensive framework based on nine key principles: from having a clear rationale for digital transformation, to addressing potential harms, ensuring sustainability, and involving stakeholders in strategy design and implementation.

What Does the Scorecard Do?

The scorecard serves as both an evaluative and generative tool. It can be used in three ways:

  • Before strategy creation: to guide development of new digital transformation strategies
  • During drafting: to reflect and revise as strategies take shape
  • After publication: to assess existing strategies and inform future revisions

The framework encourages collaborative reflection rather than rigid scoring. Colour-coded or numerical evaluations for each principle can be discussed among teams to generate shared understanding and prioritise improvements.

For example, one principle assesses whether an organisation explicitly identifies transformation-specific barriers (not just digitalisation barriers). Another asks whether the potential negative consequences of digital transformation are recognised and addressed. These kinds of reflective prompts help organisations go beyond surface-level digital initiatives and consider deeper, structural change.

Real-World Relevance

To demonstrate the scorecard’s application, we use two UN organisations’ strategies as case studies. These examples show how the scorecard highlights strengths and reveals gaps such as lack of measurable objectives, limited detail on implementation, or absence of stakeholder engagement.

But perhaps most importantly, the paper frames the process of scoring and discussion itself as a benefit. It fosters dialogue, builds shared understanding, and supports strategic alignment within teams and across organisations.

Who Should Read This?

The DX4D Scorecard will be useful to a range of readers:

  • Development practitioners involved in strategy design
  • ICT4D professionals seeking tools for reflection and improvement
  • Researchers interested in bridging theory and practice
  • Donors and policy-makers reviewing digital transformation efforts

Read the full paper and access the scorecard here:
👉 A Scorecard to Evaluate and Improve Organisational Strategies for Digital Transformation for Development (Working Paper No.115)

A first draft of some sections of this blogpost was developed using ChatGPT

AI4D, Digital Economy, Inclusion, Transformation, Twin Transition: New Research Outputs from CDD, Manchester

~ Richard Heeks ~ 1 Comment

Recent outputs on AI4D; Digital economy; Digital inclusion; Digital transformation; Twin transition – from Centre for Digital Development researchers, University of Manchester

AI-for-Development

This short paper constructs a new model of the AI value chain and its underpinning ecosystem that can be used by researchers and practitioners with interests in the AI economy and in maximising value-building including AI value chain upgrading.

This paper outlines views that are both affirmatory and critical of the growing narrative about existence of an “AI cold war” between the US and China, highlighting particularly the securitisation of discourse.

Digital Economy

This paper is a bibliometric analysis of the digital economy in developing countries, examining publication trends, influential authors, and research gaps from 2003 to 2023, providing insights for future studies and policy development.

This argues that Africa’s youthful population presents both opportunities and challenges. Business process outsourcing could reduce unemployment, but requires supportive legislation, infrastructure, and skills development to harness this potential and drive economic growth.

Digital Inclusion

This report investigates the extent to which digital pathways support equitable growth for micro, small and medium enterprises (MSMEs) in the Indian agriculture sector. The analysis focuses on issues of autonomy, market access, and dependency on large digital platforms, also highlighting the gendered experiences in digital transformation.

This paper investigates cross-sector employment effects of the gendered digital capabilities divide in Indonesia, finding differential gender gaps in agriculture, commerce and services.

Digital Transformation

  • Empowering Digital Transformation: Synergies to Capacity Strengthening

Jaco Renken from CDD partnered with Jeffery Lundberg from Catholic Relief Services to facilitate a workshop at the Nethope Global Summit 2024. Participants from around the world engaged on four topics at the intersection of digital transformation and organisational capacity: 1) Digital skills development – a CRS case; 2) Digital transformation leadership – challenges in the development sector; 3) Digital maturity assessment – a CRS case; 4) Digital transformation policy and strategy – the CDD digital transformation policy scorecard.  For further details you are welcome to contact: jaco.renken@manchester.ac.uk

Twin Transition

This reviews what the twin transition is, describes four specific aspects of its application in upland regions, and outlines a future research agenda.

This paper uses China Family Panel Studies data to demonstrate the inverted U-shaped effect of digital financial inclusion on household food carbon footprint in China, which is moderated by environmental concerns and future expectations of households.

The Role of Artificial Intelligence in Digital Transformation

~ Taiyuan Zeng ~ Leave a comment

In the context of the digital revolution, increasing numbers of enterprises are realising that the digital transformation (DT) is the key to maintain the competitiveness in global business environment, and are considering the DT as a crucial strategic plan for the sustainable growth of their businesses in the future [1]. In 2022, the global spending on DT has been reached to US$1.85 trillion. By 2027, this spending is expected to reach $3.9 trillion (see Fig. 1) [2].

Fig. 1. Digital transformation spending worldwide 2017-2027 [2]

The essence of the DT is to deeply integrate digital technologies into production process, operation management and business models that enable the enterprise to gain competitive advantage by enhancing their operational efficiency, customer experience and performance [3]. Therefore, the emergence, diffusion and application of the digital technologies are important antecedents that drive the DT [4]. Artificial intelligence (AI) is a digital technology that has received a lot of attention in recent years, and demonstrates cognitive abilities like humans, e.g., knowing, learning, sensing, perceiving, acting, communicating and reasoning [5]. Its deployment is crucial for the DT of the enterprise. This blog analyses the role of AI in three key dimensions of the DT in the enterprise: smart decision-making, intelligent manufacturing and smart CRM (customer relationship management).

  • Smart decision-making

The first action to the DT of the enterprise is the change in the way of organisational decision-making – gradually changing from the human decision-making based on managers’ cognition and experience to the smart decision-making driven by the AI. The AI utilises statistical machine learning algorithms to mine and analyse large-scale data to identify valuable information, reason and interpret its meaning, and then create new insights and predictions from the data [6]. In AI-based smart decision-making, algorithms and data resources are two important elements. The AI algorithms are a set of instructions, procedures and rules to be followed in computing or problem solving. It is equivalent to the ‘brain’ or ‘mindset’ of the AI, which constructs the AI neural network and training model [7] [8]. The data resources are the ‘raw materials’ that support the AI decision-making. For example, the generative AI that is rapidly evolving and having a significant impact on various fields around the world. It is analysing big data by applying deep learning models built by algorithms that simulate the learning and decision-making processes of the human brain, and then understanding and responding to the user’s natural-language requests and questions, and providing solutions [9]. Shrestha et al. (2019) compared AI-based smart decision-making with human decision-making (see Table 1).

Table 1：Comparison of AI and human in decision-making [10].

  • Intelligent manufacturing

The second action to the DT of the enterprise is developing intelligent manufacturing. Although the development of computers and mechanical manufacturing technologies has enabled the automation of production in many firms over the past half-century, intelligent manufacturing places higher expectations and requirements on organizational production management. That is, using a wide range of digital technologies to build integrated and collaborative intelligent manufacturing systems across the various modules and processes of production management beyond the automation, thereby being able to respond and meet the changing demands of factories, supply networks and customers in real time, reducing production costs, achieving more precise quality control and higher efficiency [11] [12]. The influence of the AI covers the operation and the entire lifecycle of the intelligent manufacturing system, which is reflected in the autonomous intelligent sensing, interconnection, collaboration, learning, analysis, cognition, decision-making, control, and execution towards people, machines, materials, environment, and information. It facilitates the establishment and integration of new elements (modes, means, forms) in the intelligent manufacturing system and ultimately constructs the intelligent manufacturing ecosystem (see Fig. 2). These elements are featured as service-orientation, collaboration, customizability, flexibility, socialization, digitalization, autonomous intelligence, data-driven, support for co-innovation, and interconnectedness [13]. In general, AI tools (e.g., computer vision, machine learning, natural language processing, robotic process automation, etc.) are applied in industrial design, product testing, productivity and quality assessment, human-robot collaboration, fault detection in products or machines, robot operation, monitoring and control of manufacturing processes, job dispatching and scheduling, resource allocation, repair and predictive maintenance of industrial assets, etc. [14] [15] [16].

Fig. 2. AI-driven intelligent manufacturing [13]

  • Smart CRM

The third action to the DT of the enterprise is to facilitate the digitalisation and intelligence of the CRM to build smart CRM systems. That is, integrating digital technologies into traditional CRM systems of the enterprise to manage current and potential customers and then improve the quality of customer service, which aims to attract, acquire, and retain customers and to establish and maintain sustainable business relationships with them to ultimately achieve a high level of economic benefits [17] [18] [19]. AI presents the greatest potential within many digital technologies that support smart CRM in satisfying customer experiences and demands to enhance their satisfaction and loyalty. The role of the AI in smart CRM is reflected in three areas: analytics predictions, automated digital marketing, and chatbots for automated customer interactions. Specifically, AI algorithms mine and analyse large amounts of data generated from current and past interactions and transactions between enterprise and their customers to identify customer preferences and purchase intentions, assess customer sentiment, predict their future demands and behaviours, and create precise customer profiles. Thereby providing key insights for the enterprise to improve customer relationships, and automatically delivering personalised and interesting content such as product/service discounts and promotional notifications, advertisements and recommendations to potential customers in digital platforms [20] [21] [22]. Furthermore, AI-driven chatbots are increasingly playing the role of virtual assistants or digital agents to help customers address requests and complaints [23]. For example, dialoguing and interacting with customers in real time during their consumption. Understanding customers’ language and emotions and responding to their requests. Providing advice and feedback to customers for purchase inquiries. Guiding customers throughout the transaction process, etc. [24] [25].

Overall, this article embeds the AI into three DT actions (i.e., smart decision-making, intelligent manufacturing, and smart CRM) of the enterprise to construct the analytical perspective, and then discusses the role of the AI in the DT. As the hot topics and research trends in the field of digital economy, AI in DT is worthy of gaining more attention and conducting deeper exploration in the future.

Reference

[1] Leão, P., & da Silva, M. M. (2021). Impacts of digital transformation on firms’ competitive advantages: A systematic literature review. Strategic Change, 30(5), 421-441.

[2] Digital transformation spending worldwide 2017-2027. Available at: https://www.statista.com/statistics/870924/worldwide-digital-transformation-market-size/

[3] Gong, C., & Ribiere, V. (2021). Developing a unified definition of digital transformation. Technovation, 102, 102217.

[4] Westerman, G., Bonnet, D., & McAfee, A. (2014). Leading digital: Turning technology into business transformation. Harvard Business Press.

[5] Holmström, J. (2022). From AI to digital transformation: The AI readiness framework. Business Horizons, 65(3), 329-339.

[6] Calp, M. H. (2020). The role of artificial intelligence within the scope of digital transformation in enterprises. In Advanced MIS and digital transformation for increased creativity and innovation in business (pp. 122-146). IGI Global.

[7] Artificial intelligence (AI) algorithms: a complete overview. Available at: https://www.tableau.com/data-insights/ai/algorithms

[8] Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International journal of information management, 48, 63-71.

[9] IBM. What is generative AI? Available at: https://www.ibm.com/topics/generative-ai

[10] Shrestha, Y. R., Ben-Menahem, S. M., & Von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California management review, 61(4), 66-83.

[11] Kusiak, A. (2018). Smart manufacturing. International journal of production Research, 56(1-2), 508-517.

[12] Barari, A., de Sales Guerra Tsuzuki, M., Cohen, Y., & Macchi, M. (2021). Intelligent manufacturing systems towards industry 4.0 era. Journal of Intelligent Manufacturing, 32, 1793-1796.

[13] Li, B. H., Hou, B. C., Yu, W. T., Lu, X. B., & Yang, C. W. (2017). Applications of artificial intelligence in intelligent manufacturing: a review. Frontiers of Information Technology & Electronic Engineering, 18(1), 86-96.

[14] Arinez, J. F., Chang, Q., Gao, R. X., Xu, C., & Zhang, J. (2020). Artificial intelligence in advanced manufacturing: Current status and future outlook. Journal of Manufacturing Science and Engineering, 142(11), 110804.

[15] Shojaeinasab, A., Charter, T., Jalayer, M., Khadivi, M., Ogunfowora, O., Raiyani, N., … & Najjaran, H. (2022). Intelligent manufacturing execution systems: A systematic review. Journal of Manufacturing Systems, 62, 503-522.

[16] Nti, I. K., Adekoya, A. F., Weyori, B. A., & Nyarko-Boateng, O. (2022). Applications of artificial intelligence in engineering and manufacturing: a systematic review. Journal of Intelligent Manufacturing, 33(6), 1581-1601.

[17] Quinton, S. (2013). The digital era requires new knowledge to develop relevant CRM strategy: A cry for adopting social media research methods to elicit this new knowledge. Journal of Strategic Marketing, 21(5), 402-412.

[18] Lipiäinen, H. S. M. (2015). CRM in the digital age: implementation of CRM in three contemporary B2B firms. Journal of Systems and Information Technology, 17(1), 2-19.

[19] Krizanic, S., Sestanj-Peric, T., & Tomicic-Pupek, K. (2019). The changing role of ERP and CRM in digital transformation. Economic and Social Development: Book of Proceedings, 248-256.

[20] Chatterjee, S., & Chaudhuri, R. (2023). Customer relationship management in the digital era of artificial intelligence. In Digital Transformation and Industry 4.0 for Sustainable Supply Chain Performance (pp. 175-190). Cham: Springer International Publishing.

[21] Roba, G. B., & Maric, P. (2023). AI in customer relationship management. In Developments in Information and Knowledge Management Systems for Business Applications: Volume 7 (pp. 469-487). Cham: Springer Nature Switzerland.

[22] Chatterjee, S., Chaudhuri, R., Vrontis, D., & Jabeen, F. (2022). Digital transformation of organization using AI-CRM: From microfoundational perspective with leadership support. Journal of Business Research, 153, 46-58.

[23] Ledro, C., Nosella, A., & Vinelli, A. (2022). Artificial intelligence in customer relationship management: literature review and future research directions. Journal of Business & Industrial Marketing, 37(13), 48-63.

[24] Youn, S., & Jin, S. V. (2021). In AI we trust?” The effects of parasocial interaction and technopian versus luddite ideological views on chatbot-based customer relationship management in the emerging “feeling economy. Computers in Human Behavior, 119, 106721.

[25] Galitsky, B. (2020). Artificial intelligence for customer relationship management. Springer International Publishing, Cham. https://doi. org/10.1007/978-3-030-52167-7.

Checklist for Digital-Transformation-for-Development (DX4D) Research

~ Richard Heeks ~ Leave a comment

The checklist below derives from the Principles for DX4D Research and Consulting, modified on the basis of presentation to an international development audience, and a revision workshop of digital development researchers.

It is intended for use by researchers undertaking DX4D research, to help improve the fit of that research with the particular nature of digital transformation for development:

1. Does your research incorporate a definition of digital transformation for development: both the digital transformation element and the developmental transformation that is the goal of DX4D?

2. Is digital transformation for development understood in your research to be different from incremental digitally-enabled change: creating significant systemic disruption that involves technological changes to digital data and systems but also involves and requires broader, parallel transformative changes in development processes, resource distributions, formal and informal institutions, and structural relations?

3. Does your research therefore recognise that the impact of digital transformation for development emerges not deterministically from technology alone but from a mix of social and technological factors?

4. Is there recognition in your research that there are both positive and negative impacts associated with DX4D?

5. Is the focus for your research the micro-level, proactive actions of individuals within organisations (digital transformation for development) and/or the macro-level societal changes (digital transformation of development) that both derive from and shape micro-level actions?

6. If your research discusses implementation drivers, barriers, processes, etc., does it move beyond traditional ICT4D research, to take into account the specific scope, duration, disruption and other features of digital transformation for development?

7. If your research provides practical DX4D recommendations, do these cover not just the content of organisational (private, public, NGO and international agency) strategy or government policy but also details of the processes and the structures through which that strategy or policy will be implemented?

To find out more, watch this video explanation of the checklist:

 

A PDF version of the checklist is available here

These revisions are based on workshop inputs from Adolph Sedem Yaw Adu, Katazo Amunkete, Judy van Biljon, Leonard P. Binamungu, Adheesh Budree, Ishmael Chikoo, Faheem Hussain, Epiphania Kimaro, Masoud Mahundi, Mandipezano Makawonesu, Silvia Masiero, Rangarirai Matavire, Rogerio Melo, Nita Mennega, Sarah Mulaji, Eric Munyambabazi, Sinte Mutelo, Gwamaka Mwalemba, Sajda Qureshi, Sundeep Sahay, Tim Savage, Lisa Seymour, Anand Sheombar, Cecilia Strand and P. J. Wall, plus colleagues from JICA and the JICA Ogata Research Institute.  They were edited by Richard Heeks.

Image adapted from: https://www.linkedin.com/posts/evevlemincx_what-digital-transformation-is-really-all-activity-7003396620490792961-B5L5/

Exploring Barcelona Smart Tourism through a Digital Transformation Lens

~ Julin Liu ~ Leave a comment

The evolution of urban tourism is increasingly shaped by digital technologies, with cities around the world embracing innovative models and digital transformation to enhance visitor experiences. Digital transformation refers to the integration of digital technologies into all aspects of an organization’s operations, fundamentally altering how it operates and delivers value to customers (Westerman et al., 2011). In the tourism context, digital transformation encompasses the adoption of innovative technologies to enhance the tourist experience, streamline operations, and drive sustainable growth. Building upon foundational studies by scholars such as Buhalis and Amaranggana (2014) and Gretzel et al. (2015) about smart tourism platforms and destinations, this blog explores a theoretical framework for understanding the key elements of smart tourism through a digital transformation lens. The elements of connectivity, data analytics, personalization, and sustainability are drawn from the Barcelona Smart Tourism Platform, reflecting a holistic approach to digital transformation in tourism.

Connectivity serves as the backbone of the digital transformation, enabling the integration of tourists, service providers, and city authorities, which is in line with the vision outlined by Law et al. (2016). Digital connectivity is a means of creating a cohesive ecosystem where information flows freely and efficiently. This connectivity manifests through various channels, including Wi-Fi hotspots, mobile apps, and digital signage, enabling tourists to access information, make bookings, etc. Moreover, by integrating disparate systems and stakeholders, connectivity enhances collaboration and coordination, fostering a more integrated approach to destination management.

Data analytics enables the city to gain actionable insights from the massive amount of data generated by tourist interactions. Drawing on methodologies outlined by Xiang et al. (2017) and Gretzel et al. (2015), Barcelona employs advanced analytics techniques to analyse visitor behaviour, preferences, and trends. This data-driven approach empowers the city to make informed decisions thereby optimizing the tourism experience, and enables stakeholders to tailor offerings, optimize resource allocation, and anticipate demand, thereby maximizing satisfaction and operational efficiency.

Personalization allows a fit to the individual needs and preferences of tourists (Wang et al., 2017). As Gretzel et al. (2021) noted the importance of techniques, Barcelona leverages AI-driven algorithms to customize offerings and recommendations for each visitor. From personalized itineraries to targeted promotions, this personalized approach enhances visitor satisfaction and fosters deeper engagement with the destination.

Sustainability of Barcelona is in line with principles outlined by Niñerola (2019), Barcelona integrates sustainability across all aspects of the visitor experience, from transportation to accommodation to attractions. This encompasses initiatives such as promoting eco-friendly modes of transportation, reducing waste through recycling programs, and supporting local communities through responsible tourism practices.

As echoed by scholars such as Buhalis (2020) and Gretzel (2021), Barcelona is creating smarter, more sustainable destinations through technology, exemplifying the potential of digital innovation in sustainable tourism by integrating connectivity, data analytics, personalization and sustainability. As cities around the world navigate the complexities of urban tourism in the digital age, the Barcelona Tourism Platform offers a good example, and a framework that others working in smart tourism can utilize.

An Adapted Digital-Transformation-for-Development Organisational Strategy Framework

~ Richard Heeks ~ Leave a comment

What issues should shape organisational digital-transformation-for-development (DX4D) strategy?

One way to answer this is through adaptation of one of the most widely-cited guides to digital transformation strategy, the Digital Transformation Framework[i].  The Framework provides a series of strategic questions that managers “have to address when embarking on digital transformation”, divided into four areas: use of technologies, change in value creation, structural changes, and finance.  They do not per se constitute a strategy, and nor should they be seen as exhaustive but they provide a frame for digital transformation strategy.

These questions derive from experiences of German private sector media companies, and so have been adapted below to make them more relevant to the context of development organisations.  We start with an adapted definition of strategy itself:

A digital transformation strategy signposts the way toward digital transformation and guides managers through the transformation process resulting from the integration and use of digital technologies. A digital transformation strategy impacts an organisation more comprehensively than an IT strategy and addresses potential effects on interactions across organisational borders with clients, collaborators and suppliers

DX4D Organisational Strategy Framework

1. Use of Technologies

Involves assessment of the role of ICTs and of the IT/ICT department in the organisation.

Question Strategic Options Description
1a. How significant is your organisation’s ICT to achieving strategic goals? Enabler ICT is an enabler of strategic goals
Supporter ICT is seen as a support function to reach strategic goals
1b. How ambitious is your organisation’s approach to new digital technologies? Innovator The organisation is at the forefront of innovating new technologies
Early adopter The organisation actively looks for opportunities to implement new technologies
Follower The organisation relies on well-established solutions

2. Changes in Value Creation

Involves assessment of the way in which digital technologies alter the organisation’s core business model[ii].

Question Strategic Options Description
2a. How is digital tech used in external client engagement and delivery? Enhanced External-facing systems are fully digitalised
Extended External-facing systems’ data structures and work processes are redesigned and optimised through use of digital
Redefined External-facing systems are creating new sources of value for clients through use of digital
2b. How is digital tech used in the organisation’s internal systems? Optimised Internal-facing systems are fully digitalised
Integrated Internal-facing systems are fully digitally-integrated
Leveraged New sources of value are leveraged from the data available within integrated organisational systems

3. Structural Changes

Involves assessment of the implications of transformation of organisational structures.

Question Strategic Options Description
3a. Who is in charge of digital transformation? Organisational CEO Overall Chief Executive Officer
Organisational CDO Overall Chief Digital Officer
Organisational CIO/CITO Overall Chief Information/IT Officer
Departmental head Head of individual department or function within the organisation
3b. Do you plan to integrate new operations/business models into existing structures or create a new entity? Integrated Digital operations for new business models will be fully-integrated into the organisation’s current structures
Separated Digital operations for new business models will be implemented separate from the existing core organisation
3c. What type of operational changes do you expect? Services and products New organisational services and (if relevant) products
Business processes New / improved business processes
Skills New skills because of digital and other changes
3d. How will any new competencies be acquired? Internally Relying on existing resources
Partnership Fostered via links with external partners
External sourcing Sourcing additional competencies from outside

4. Finance

Involves assessment of the pressures and financial resources that digital transformation will entail.

Question Strategic Options Description
4a. How strong are financial and other pressures for change? Low Core activities are subject to few external pressures for change
Medium Core activities are sustainable but subject to external pressures for change
High Current core activities are not sustainable due to external pressures for change
4b. How will your organisation finance digital transformation? Internal From existing internal funds
External Additional external funding will be required

As noted, this is not an exhaustive list and revision suggestions are welcome, but this can be at least a relevant starting point for organisational DX4D strategy.

Image source: Digital Transformation Vectors by Vecteezy

[i] Hess, T., Matt, C., Benlian, A., & Wiesböck, F. (2016). Options for formulating a digital transformation strategy. MIS Quarterly Executive, 15(2), republished as Hess, T., Matt, C., Benlian, A., & Wiesböck, F. (2020). Options for formulating a digital transformation strategy in: Strategic Information Management, R.D. Galliers, D.E. Leidner & B. Simeonova (eds), Routledge, New York, NY, 151-173.

[ii] Adapted from ideas in: Collins, K. (2018) Strategy, leadership and team building. In: Transformational Leadership and Not for Profits and Social Enterprises. Wiltshire, K., Malhotra, A., Axelsen, M. (eds.) Routledge, London, 239-263.

What are the moral consequences of digital platforms ?

~ nicholsonbrian ~ Leave a comment

Digital platforms, surveillance and processes of demoralization https://doi.org/10.1177/02683962231208215

In 2021 I curated a special section in Information Systems Journal on digital platforms and development.  A notable omission in that special issue was coverage of morality and digital platforms. It is this gap in the literature that I attempt to address with my colleagues Sung Hwan Chai, Robert Scapens and Chunlei Yang in a recently published paper in the Journal of Information Technology. The paper is titled Digital platforms, surveillance and processes of demoralization.   In this paper we draw on the eminent sociologist Zygmunt Bauman.  Bauman was until his death in 2017 Professor of Sociology at Leeds University, UK.  His work extends to 57 books and over 100 articles on the themes of globalisation, liquid modernity and morality.

Bauman’s work is considered seminal in sociology and has been discussed and applied in many other disciplines. Our interest in Bauman’s work on morality was stimulated by the findings of our research as they unfolded in our ethnographic fieldwork. We were also influenced by previous applications of Bauman’s thought in management particularly by Stuart Clegg and also in information systems scholarship by Suprateek Sarker  and colleagues.   

A paper by Tommy Jensen in Journal of Business Ethics gave us a usable framework to perform the work of applying Bauman’s concepts to our field data.

Our paper concerns the case of a resort hotel in Vietnam that implemented digital platforms. Once instance of implementation was intentional – a “front door” entry – following a senior management decision to introduce a performance management platform “Medallia”.  However, other platforms entered informally via the “back door” without any planning or management oversight. For instance, WhatsApp and Tripadvisor entered regular use by hotel staff as a result of informal “back door” means. The paper expands on this but the following gives a taster of what happened in one of the “back door” implementations.

The story of the informal “back door” implementation of WhatsApp started in the domain of golf cart transport for  the hotel guests. Golf carts transported guests around the large hotel site that stretches across several km of beachside to their holiday beach huts etc.  These journeys were booked and coordinated by wireless walkie talkies which over time ran out of capacity as the restricted available channels became overcrowded.  Guests became frustrated by waiting, so out of necessity, drivers started to use their smartphones and WhatsApp to coordinate trips.  Before this incident, smartphone use at work by hotel staff was strictly prohibited.  

Hotel management allowed this rule bending out of necessity to satisfy guests and over time smartphone and WhatsApp use quickly spread organically to other staff and management in all areas of the hotel.  Chat forums were set up that were sectioned into several different areas of interest.  At the same time, Tripadvisor was gaining prominence for performance evaluation in the hotel and staff and management monitored customer reviews.  We were fortunate to be present in the hotel at the time of this critical incident and already engaged in a period of ethnographic study.  This turn of events allowed us to observe how the smartphones and various platforms were spreading and being used by staff and management. 

What we found was surprising. Although we did not have Bauman’s morality concepts in mind at the time when we were in the hotel, the morality of platforms turned out to be a very significant theme. Here is a taster of what is contained in the paper where we recount several episodes that show the different facets of platforms and morality using Bauman’s concepts as a lens. 

One of the episodes tells a story that started with an interview about the use of platforms in service quality.  The interview took place with a manager in the hotel restaurant.  During the interview, to demonstrate her point about how she was using WhatsApp for service quality purposes, the manager looked around the restaurant and noticed that an “afternoon tea” (an elaborate tray of drinks, cakes and pastries) had been left uneaten by guests who had already departed.  The manager proceeded to take a photo of the uneaten food and immediately uploaded the image to the main WhatsApp group for all hotel staff. The manager accompanied the image with a request for comments on why the food had been left uneaten by the guests.  While we were sat at the table, hotel staff on WhatsApp quickly responded to the image  and request for explanation.  Firstly, we were surprised at the speed of the replies that started almost immediately after the manager had posted the image.  Then the comments rolled in which were visible to all. We were surprised at the increasingly vitriolic tone of the comments that rallied around accusations of incompetence and lack of care of the kitchen / waiting staff. It seemed that individuals were trying to outdo each other in their comments and accusations towards the catering staff.  We were struck by how humiliated the catering staff must have felt being publicly shamed without any opportunity to put their side of the story.

To make sense of what we had observed, in the paper we theorise the episode with Bauman’s concepts of morality in mind.  For instance, the WhatsApp platform provided conditions of effacement of face, the individuals posting on WhatApp were physically distanced and unable to meet and see the kitchen staff.  Therefore, what Bauman refers to as moral impulse was suspended.  The catering staff were dehumanised and reduced to sets of traits by the WhatsApp community posting the increasingly critical comments.  Furthermore, the comments appeared to show a focus on the technical (ie. service to customers) over moral (ie. caring for the humiliation of the catering staff).  Milgram’s electroshock experiment helps explain the behaviour.  In this experiment, students were asked to deliver a punishment (i.e., electroshocks) to another person (a hired actor) in order to study how it affected learning. Milgram found that individuals who gradually become absorbed by technological aspects of the task at hand, how this task could best be technically solved and carried out, pay lesser and lesser attention to consequences other than those belonging to the technological realm of action.   

Returning to our interview in the hotel restaurant, the manager told us she felt proud of the critical comments and how individuals were posting on WhatApp. This, she said, represented the service culture in the hotel.  However, we theorise in the paper how WhatsApp created synoptic surveillance (workers watching each other) who were responding rapidly based on their  fear of not being noticed and desire for others to see them. The individuals on WhatsApp could not easily observe how their actions affected the catering staff, a feature when viewed through the lens of Bauman’s morality concepts, dehumanizes them. These individuals posting on WhatsApp had come to equate the manner in which they perceive responsible actions with acting in accordance with organizational rules and demands of the service culture. Consequently, again drawing on Bauman, these individuals appeared to feel excluded from the authorship of their acts and no longer bear full, undivided responsibility for the consequences on the catering staff.

Much has been written about digital platforms from a management perspective but our understanding of the potential negative moral consequences is limited.  In the paper, we explain a number of episodes of this type also theorised using Bauman’s lens on morality.  This builds an argument derived from Bauman that suggests digital platforms have the potential to foster a state of moral ambivalence. 

I encourage you to read the full paper to get a sense of the completeness of Bauman’s treatise, it makes for compelling reading. Overall, we hope subsequent researchers will see value in applying Bauman’s concepts as a theoretical frame to make sense of the implications of digital platforms in various domains of application.     

X-Washing: When “Digital Transformation” Isn’t Digital Transformation

~ Richard Heeks ~ Leave a comment

When is “digital transformation” not digital transformation?

Answer: when it’s just the same as mainstream digitalisation.

We’ve recently produced a set of 13 principles for digital-transformation-for-development (DX4D) research and consulting, but a key essence is that transformation is special and different.  Digital transformation means doing something different from the kind of digitalisation that has been undertaken for decades:

Digitalisation:

Digital Transformation:

Source

Yet the term “digital transformation” is now being applied to all sorts of initiatives, some of which are not digital transformation.  As greenwashing is to sustainability, some of this looks like “X-washing”: labelling a project as transformation even when it patently is not.

To identify if something is digital transformation, a simple substitution test can help.  Replace the term “digital transformation” with “digitalisation”[i] (defined here as “adaptation of a system, process, etc. to be operated with the use of computers and the internet”[ii]).  If it still makes sense, it’s not digital transformation.

I give some examples below, though kept simple by just dealing with definitions:

EXAMPLE 1

Here’s a fairly-obvious example:

Original:

“digital transformation … goes beyond the digitalization of process, and it is a deep transformation of the organization activities, processes, competences, and patterns to face challenges and take advantage of the emerging technology opportunities and its accelerated impact on society”[iii]

Substitution:

“digitalisation … goes beyond the digitalization of process, and it is a deep transformation of the organization activities, processes, competences, and patterns to face challenges and take advantage of the emerging technology opportunities and its accelerated impact on society”

This does not make sense.  Quite apart from the obvious problem of contrasting digitalisation with itself, digitalisation (“adaptation”) is not the same as “deep transformation”.

EXAMPLE 2

From the same source, here’s a reverse example:

Original:

“digital transformation (DT) is the organizational alignment between processes, people, and technology with the aim of complying efficiently with all the relevant activities of the company”

Substitution:

“digitalisation is the organizational alignment between processes, people, and technology with the aim of complying efficiently with all the relevant activities of the company”

The substitution text could pass as a definition of digitalisation, and there is no sense of transformation e.g. disruption or radical change.  So the original does not appear to be referring to actual digital transformation.

EXAMPLE 3

Lastly, here’s a more shades-of-grey example:

Original:

“Digital transformation can be defined as the migration of companies and societies to a stage in which digital technologies become the backbone of their products and services, giving rise to the development of new forms of operation and new business models”[iv]

Substitution:

“Digitalisation can be defined as the migration of companies and societies to a stage in which digital technologies become the backbone of their products and services, giving rise to the development of new forms of operation and new business models”

The substitution text could work but it is quite a bold definition of digitalisation.  You could argue that it fits with those approaches that see digitalisation encompassing all digital change from the incremental to the transformative.  However, it seems to be ignoring the incremental improvement and redesign elements of the digitalisation spectrum.  Since it also takes things beyond the individual process / system focus of digitalisation, I would lean towards saying the original definition passes the test and does reflect actual digital transformation.  But it’s debatable.

CONCLUSION

The substitution test only focuses on one aspect of what digital transformation truly means: you can find the other DX4D principles here.

However, it will in some cases help to identify definitions and other usages which can appear to be X-washing: the re-badging as “digital transformation” of something that is not.

The test can also be used for more than just definitions; for example, in the assessment of policies or strategies or projects – are they transformative or are they actually just standard digitalisation, re-badged to make them look more modern and innovative.

[i] Terms other than “digitalisation” can also be used for substitution e.g. “digitisation” or “automation”.

[ii] Google/Oxford Languages definition

[iii] Serna Gómez, J.H., Díaz-Piraquive, F.N., Muriel-Perea, Y.D.J. and Díaz Peláez, A. (2021). Advances, opportunities, and challenges in the digital transformation of HEIs in Latin America. In: D. Burgos & J.W. Branch (eds), Radical Solutions for Digital Transformation in Latin American Universities, Springer, Singapore, 55-75.

[iv] CEPAL (2020). Food Systems and COVID-19 in Latin America and the Caribbean N° 8: The Opportunity for Digital Transformation. CEPAL, Santiago, Chile.