•Digital strategy making is different from upfront strategic planning.•A digital transformation strategy is continuously in the making.•The crafting of a digital transformation strategy is a highly ...dynamic process.•We propose a process model for pre-digital organizations in traditional industries.
The formulation and implementation of a digital transformation strategy (DTS) has become a key concern for many pre-digital organizations across traditional industries, but how such a strategy can be developed remains an open question. We used interpretive in-depth case study research to study how a European financial services provider has formulated and implemented a DTS. By focusing on the underlying processes and strategizing activities, we show that digital strategy making not only represents a break with the conventions of upfront strategic information systems (IS) planning, but reveals a new extreme of emergent strategy making. Specifically, we conclude that a DTS is continuously in the making, with no foreseeable end. By building on theory from IS strategizing and strategy-as-practice literature, we theorize an integrated process/activity model that characterizes DTS formulation and implementation in pre-digital organizations. Our model shows that the crafting of a DTS is a highly dynamic process involving iterating between learning and doing.
Real world data mining applications must address the issue of learning from imbalanced data sets. The problem occurs when the number of instances in one class greatly outnumbers the number of ...instances in the other class. Such data sets often cause a default classifier to be built due to skewed vector spaces or lack of information. Common approaches for dealing with the class imbalance problem involve modifying the data distribution or modifying the classifier. In this work, we choose to use a combination of both approaches. We use support vector machines with soft margins as the base classifier to solve the skewed vector spaces problem. We then counter the excessive bias introduced by this approach with a boosting algorithm. We found that this ensemble of SVMs makes an impressive improvement in prediction performance, not only for the majority class, but also for the minority class.
Hazards and disasters have always negative impacts on the way of life. Landslide is an overwhelming natural as well as man-made disaster that causes loss of natural resources and human properties ...throughout the world. The present study aimed to assess and compare the prediction efficiency of different models in landslide susceptibility in the Kysuca river basin, Slovakia. In this regard, the fuzzy decision-making trial and evaluation laboratory combining with the analytic network process (FDEMATEL-ANP), Naïve Bayes (NB) classifier, and random forest (RF) classifier were considered. Initially, a landslide inventory map was produced with 2000 landslide and non-landslide points by randomly divided with a ratio of 70%:30% for training and testing, respectively. The geospatial database for assessing the landslide susceptibility was generated with the help of 16 landslide conditioning factors by allowing for topographical, hydrological, lithological, and land cover factors. The ReliefF method was considered for determining the significance of selected conditioning factors and inclusion in the model building. Consequently, the landslide susceptibility maps (LSMs) were generated using the FDEMATEL-ANP, Naïve Bayes (NB) classifier, and random forest (RF) classifier models. Finally, the area under curve (AUC) and different arithmetic evaluation were used for validating and comparing the results and models. The results revealed that random forest (RF) classifier is a promising and optimum model for landslide susceptibility in the study area with a very high value of area under curve (AUC = 0.954), lower value of mean absolute error (MAE = 0.1238) and root mean square error (RMSE = 0.2555), and higher value of Kappa index (K = 0.8435) and overall accuracy (OAC = 92.2%).
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•Landslide susceptibility modeling plays a significant role in disaster prevention.•Three models (FDEMATEL-ANP, NBC, and RFC) were applied and compared.•The ROC curve was used for evaluating and comparing the performance of the results.•Random forest classifier (RFC) produced the best result for susceptibility assessment.•An accurate model of susceptibility is helpful for landslide risk mitigation and planning.
Cloud computing is a powerful technology to perform massive-scale and complex computing. It eliminates the need to maintain expensive computing hardware, dedicated space, and software. Massive growth ...in the scale of data or big data generated through cloud computing has been observed. Addressing big data is a challenging and time-demanding task that requires a large computational infrastructure to ensure successful data processing and analysis. The rise of big data in cloud computing is reviewed in this study. The definition, characteristics, and classification of big data along with some discussions on cloud computing are introduced. The relationship between big data and cloud computing, big data storage systems, and Hadoop technology are also discussed. Furthermore, research challenges are investigated, with focus on scalability, availability, data integrity, data transformation, data quality, data heterogeneity, privacy, legal and regulatory issues, and governance. Lastly, open research issues that require substantial research efforts are summarized.
•The amount of data continues to increase at an exponential rate.•Cloud computing and big data are conjoined.•Only a few tools are available to address the issues of big data processing in cloud.•Open research issues that require substantial research efforts are summarized.
With the proliferation of the use of chatbots across industries, business-to-business (B2B) businesses have started using cognitive chatbots for improved customer service which signifies our ...research. By extending the Technology Acceptance Model and Information Systems Success Model, this study examines personalised contextual customer service using cognitive chatbot. A quantitative research method is applied to the primary data collected from 300 respondents of B2B businesses. The study contributes to the limited research on chatbots and suggests improvement in customer service. The findings provide evidence of high value by customers, particularly while checking for real-time information on reliability and accessibility of products/services. The automated answers to repetitive questions on the recurrent issues create a seamless experience for the customers. This research makes significant theoretical contributions by integrating two models into a simplified model in chatbot literature and manifest that trust affects the willingness to use the cognitive chatbot which drives automation.
Social media plays an important part in the digital transformation of businesses. This research provides a comprehensive analysis of the use of social media by business-to-business (B2B) companies. ...The current study focuses on the number of aspects of social media such as the effect of social media, social media tools, social media use, adoption of social media use and its barriers, social media strategies, and measuring the effectiveness of use of social media. This research provides a valuable synthesis of the relevant literature on social media in B2B context by analysing, performing weight analysis and discussing the key findings from existing research on social media. The findings of this study can be used as an informative framework on social media for both, academic and practitioners.
By introducing the self-construal theory to the virtual brand community literature, this study examines how self-construal interacts with community rewards and public self-consciousness to influence ...customer participation. Based on data collected from two communities in China, we found that independent construal had a stronger positive direct effect on the intention to participate than did interdependent construal. The effect of independent construal on intention was attenuated by community reward and public self-consciousness. In comparison, the effect of interdependent construal on intention was enhanced by public self-consciousness, but this strengthening effect did not exist for community rewards. A significant association between intention and actual participation was also found.
Picking pockets on the lawn Barkhuus, Louise; Chalmers, Matthew; Tennent, Paul ...
Lecture notes in computer science,
01/2005
Conference Proceeding, Book Chapter
Recenzirano
Odprti dostop
This paper presents Treasure, an outdoor mobile multiplayer game inspired by Weiser's notion of seams, gaps and breaks in different media. Playing Treasure involves movement in and out of a wi-fi ...network, using PDAs to pick up virtual 'coins' that may be scattered outside network coverage. Coins have to be uploaded to a server to gain game points, and players can collaborate with teammates to double the points given for an upload. Players can also steal coins from opponents. As they move around, players' PDAs sample network signal strength and update coverage maps. Reporting on a study of players taking part in multiple games, we discuss how their tactics and strategies developed as their experience grew with successive games. We suggest that meaningful play arises in just this way, and that repeated play is vital when evaluating such games.
Self-attention (SA) mechanisms have been widely used in developing sequential recommendation (SR) methods, and demonstrated state-of-the-art performance. However, in this paper, we show that ...self-attentive SR methods substantially suffer from the over-smoothing issue that item embeddings within a sequence become increasingly similar across attention blocks. As widely demonstrated in the literature, this issue could lead to a loss of information in individual items, and significantly degrade models’ scalability and performance. To address the over-smoothing issue, in this paper, we view items within a sequence constituting a star graph and develop a method, denoted as MSSG, for SR. Different from existing self-attentive methods, MSSG introduces an additional internal node to specifically capture the global information within the sequence, and does not require information propagation among items. This design fundamentally addresses the over-smoothing issue and enables MSSG a linear time complexity with respect to the sequence length. We compare MSSG with eleven state-of-the-art baseline methods on six public benchmark datasets. Our experimental results demonstrate that MSSG significantly outperforms the baseline methods, with an improvement of as much as 10.10%. Our analysis shows the superior scalability of MSSG over the state-of-the-art self-attentive methods. Our complexity analysis and run-time performance comparison together show that MSSG is both theoretically and practically more efficient than self-attentive methods. Our analysis of the attention weights learned in SA-based methods indicates that on sparse recommendation data, modeling dependencies in all item pairs using the SA mechanism yields limited information gain, and thus, might not benefit the recommendation performance. Our source code and data are publicly accessible through GitHub .
The evolution of digital tools and platforms has ushered in new possibilities for researchers, scholars, and practitioners of rhetoric and composition and adjacent fields like technical ...communication. These technologies change the ways we can gather, store, and use larger datasets, prompting new discussions on what big data methods look like in the field. The chapters housed in Amanda Licastro and Benjamin Miller's edited collection Composition and Big Data investigate the promises, concerns, and areas for further conversation regarding the applications of big data methods in composition-focused research.
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