SuDiSEEP

Sustainable and digital supply chain transformations in emerging economies: The case of Pakistan (SuDiSEEP)

The simultaneous transformation of supply chain (SC) driven by sustainability demands and enabled by technology advancements poses multiple challenges for firms. The rise of different digital technologies is changing markets and SCs allowing new actors to emerge and challenging conventional processes. This intersection is increasingly explored in recent years, while affecting all sizes of companies as much as single entrepreneurs. It drives a multitude of research needs, which are only rarely explored in emerging economies so far. Pakistan offers an interesting, almost ideal empirical context for conducting related research. It has a comparatively young population which has a high degree of affinity to technological change, while not yet being saturated.

There are different aspects of particular relevance, such as how technology is adopted, a topic where individual and corporate actions largely overlap. Building on the universal technology acceptance and use of technology model, which is frequently applied on an individual level, the research question arises, how technology adoption in manufacturing and/or service industry in Pakistan would take place and can be studied. This is linked to the social networks people and companies operate in, so this approach provides a second theoretical stream, as technological and sustainable transformation requires that several actors in a SC would interact and exchange information, money and materials or goods. A third related theoretical input will be taken from adaptive structuration theory.

These theoretical foundations will be combined with a range of different methods of empirical data collection and analysis. The range of data collection methods will comprise interviews with managers, in-depth case studies and survey data, which will then be analysed employing respective research instruments, such as transcription, content analysis, but also statistical techniques as appropriate for the set of data.