ShopWithMe!: Collaborative Information Searching and Shopping for Online Retail

We present research on the development and evaluation of a collaborative search and shopping system for online retail tasks based on domain specific product requirements. We describe the design rationale for the system development and inclusion of collaborative features, including search, chat, clip-board, product suggestions, shared views, and shopping cart with a focus on how these features are used for collaborative online retail shopping and information searching and sharing. Our research goal is to understand whether collaborative search tools are useful in supporting actual collaborative online retail shopping tasks for experience goods. We describe system development and report findings from preliminary user studies of the system, using mixed methods analysis, with an emphasis on the qualitative findings. The findings highlight that systems for the online shopping domain can support searching, shared views, and group communication to aid in collaborative shopping for experience goods by improving information sharing among group members. Implications are that ecommerce systems, websites, and web apps should support collaboration based on product types

Re-examining dynamic capabilities in the context of digital transformation.

Maklan, Stan - Associate SupervisorWhile digital transformation is often a necessity to allow incumbent firms to remain competitive in a fast-changing world, it suffers from high failure rates in practice. The dynamic capability perspective was developed to address rapidly changing environments, so it can be utilised as a theoretical foundation to improve our understanding of digital transformation. With dynamic capabilities often disaggregated into three capability clusters: sensing, seizing, and transforming, these clusters are mostly presented in a static sequence and evolve independently, which is a practice challenged by this thesis. To explore the possible reasons hindering digital transformation, a longitudinal case study is conducted, exploring the evolution of dynamic capability clusters over time. It is observed that sensing, seizing, and transforming, rather than being sequential, coexist and coevolve during digital transformation. When they evolve at different speeds, mismatches can occur, which can act as bottlenecks slowing down the transformation but at the same time can act as catalysts improving underdeveloped capabilities. This finding contributes to the theory by demonstrating how mismatches arise during the coevolution of dynamic capability clusters and discussing their consequences for digital transformation. This finding also contributes to practice by arguing that the way in which firms orchestrate the coevolution of these dynamic capabilities over time holds a key to successful digital transformation, providing a more dynamic approach for emergent strategy development. It is therefore suggested that managers embrace the tensions caused by these mismatches and adopt a mindset that allows them to concurrently improve different dynamic capability clusters supporting digital transformation. While dynamic capabilities were introduced to address the static nature of the resource-based view (RBV), as previously described, the sensing, seizing, and transforming clusters are often applied in a sequential fashion, ignoring their possible interdependencies and evolutionary paths, and thus failing to capture the essential dynamism of the underlying phenomenon, which is particularly important in a high-velocity digital context. Therefore, this study further developed the conceptualisation of dynamic capability from an evolutionary perspective, better serving the current digital environment, which is changing faster than ever. As regards future research, firstly, since this thesis advances the conceptualisation of sensing, seizing, and transforming capabilities from an evolutionary perspective, it needs to be validated by more empirical studies. Secondly, the context is a limitation of this thesis. While this thesis provides deep insights through a single longitudinal case study in the retail sector, more studies are called for in diverse industries and national contexts to examine the coevolution of dynamic capabilities over time. Thirdly, while this thesis observes the mismatches during the coevolution of dynamic capabilities, further research is needed to explore the fundamental reasons behind this observation. The potential reasoning assumptions proposed by this thesis in attempting to explain the fundamental mechanism of dynamic capability mismatches require further examination via empirical research. Fourthly, an evolutionary underpinning indicates the methodological implications, calling for a longitudinal research design that moves away from a serial view in order to further advance and validate the framework of sensing, seizing, and transforming.PhD in Leadership and Managemen

Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers

Although various techniques have been proposed to generate adversarial samples for white-box attacks on text, little attention has been paid to black-box attacks, which are more realistic scenarios. In this paper, we present a novel algorithm, DeepWordBug, to effectively generate small text perturbations in a black-box setting that forces a deep-learning classifier to misclassify a text input. We employ novel scoring strategies to identify the critical tokens that, if modified, cause the classifier to make an incorrect prediction. Simple character-level transformations are applied to the highest-ranked tokens in order to minimize the edit distance of the perturbation, yet change the original classification. We evaluated DeepWordBug on eight real-world text datasets, including text classification, sentiment analysis, and spam detection. We compare the result of DeepWordBug with two baselines: Random (Black-box) and Gradient (White-box). Our experimental results indicate that DeepWordBug reduces the prediction accuracy of current state-of-the-art deep-learning models, including a decrease of 68\% on average for a Word-LSTM model and 48\% on average for a Char-CNN model.Comment: This is an extended version of the 6page Workshop version appearing in 1st Deep Learning and Security Workshop colocated with IEEE S&