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Despite that a transformational shift has occurred in many organisations towards data-driven management, many organisations struggle to harness and translate new technology, such as “big data” into a competitive advantage. This study aims to undertake an empirical investigation into the enabling factors which lead to the practice of formulating an effective data-led strategy (EDLS). Leveraging the theoretical lenses of the resource-based view, absorptive capacity and attention-focus view, a range of various factors are hypothesised to influence EDLS.

The study takes place in South Africa and is based on primary survey data focused on the Fin-tech industry sector where the need to formulate and implement an EDLS has become urgent considering the move to technology enabled banking solutions. Partial Least Squares Structural Equation Modelling (PLS-SEM) is used to test the hypotheses.

Results highlight that several factors are related to EDLS as significant predictors, which include the data platform, technical skills, knowledge management, transformation and focus-alignment. This latter factor has the largest influence on EDLS, which suggests that the alignment of focus across multiple firm divisions both vertically and horizontally significantly enables an EDLS.

Managers need to appreciate the intricacy of the range of factors involved in enabling an EDLS. Managers are advised to grow their organisational knowledge regarding which enablers offer the best pathway towards the development of a more robust framework when putting an EDLS into practice.

The article offers new insights into better understanding the relevant antecedents which enable the successful practice of an EDLS from an African emerging market perspective.

Gambling is well-recognised as a significant public health threat. However, current responses to gambling still primarily focus on individualised responsible gambling paradigms, which neglects to consider the range of commercial and political determinants that contribute to gambling harm and how it might influence young people's gambling attitudes and consumption intentions. This includes the marketing tactics used by the gambling industry to normalise harmful gambling products as embedded in everyday life, including in sport. Young people have demonstrated an in-depth gambling brand awareness and can even recall specific strategies used in gambling advertising that might appeal to children. There have been continuous calls for action to protect children and young people from the commercial marketing of gambling products from a range of stakeholders, including young people and their parents. Young people and their parents are very supportive of increased regulations on gambling advertising, particularly during sport, and have called for sporting teams and codes to reject sponsorship deals with gambling companies. However, a heavy reliance on industry self-regulation has meant that governments across the world have decided that the costs associated with exposing children and young people to pervasive gambling marketing are outweighed by perceived benefits that gambling provides to businesses benefiting financially from gambling. Comprehensive curbs on marketing, as seen in tobacco, are required to significantly reduce young people's exposure to gambling advertising and ultimately prevent the next generation of harm.

Strategy implementation is a critical component of firm performance and middle managers play a key role in the implementation process. This study was conducted to enhance the authors’ understanding of how middle managers influence strategy implementation (SI) effectiveness by investigating the impact of leadership and work team coordination.

A field study was conducted using interviews and survey data gathered from executive managers, middle managers and work team members within a large municipal organization undergoing a major strategic change.

Middle manager transformational and instrumental leadership have a direct positive impact on work team SI effectiveness. Additionally, middle manager transformational leadership has an indirect positive effect on work team SI effectiveness through coordination.

The study offers insights into managers and practitioners seeking to improve SI effectiveness by highlighting the importance of middle manager leadership development and the coordination of interdependent tasks within work teams.

The study provides valuable insight into an important but previously unstudied relationship between middle manager leadership and SI effectiveness. The work also helps bridge the chasm between leadership research and strategy research by linking leadership behavior to SI effectiveness – a key ingredient of firm performance.

Intent detection (ID) and slot filling (SF) are two important tasks in natural language understanding. ID is to identify the main intent of a paragraph of text. The goal of SF is to extract the information that is important to the intent from the input sentence. However, most of the existing methods use sentence-level intention recognition, which has the risk of error propagation, and the relationship between intention recognition and SF is not explicitly modeled. Aiming at this problem, this paper proposes a collaborative model of ID and SF for intelligent spoken language understanding called ID-SF-Fusion.

ID-SF-Fusion uses Bidirectional Encoder Representation from Transformers (BERT) and Bidirectional Long Short-Term Memory (BiLSTM) to extract effective word embedding and context vectors containing the whole sentence information respectively. Fusion layer is used to provide intent–slot fusion information for SF task. In this way, the relationship between ID and SF task is fully explicitly modeled. This layer takes the result of ID and slot context vectors as input to obtain the fusion information which contains both ID result and slot information. Meanwhile, to further reduce error propagation, we use word-level ID for the ID-SF-Fusion model. Finally, two tasks of ID and SF are realized by joint optimization training.

We conducted experiments on two public datasets, Airline Travel Information Systems (ATIS) and Snips. The results show that the Intent ACC score and Slot F1 score of ID-SF-Fusion on ATIS and Snips are 98.0 per cent and 95.8 per cent, respectively, and the two indicators on Snips dataset are 98.6 per cent and 96.7 per cent, respectively. These models are superior to slot-gated, SF-ID NetWork, stack-Prop and other models. In addition, ablation experiments were performed to further analyze and discuss the proposed model.

This paper uses word-level intent recognition and introduces intent information into the SF process, which is a significant improvement on both data sets.

This study aims to explore a potential conference experience design strategy, namely, festivalization. It investigates the potential festivalization effects on conference attendees in two formats of business conferences: virtual and in-person.

A series of two scenario-based experimental studies were conducted. A series of one-way analysis of covariance and PROCESS procedures (Model 6) were performed for data analysis.

The inclusion of festivalization elements significantly enhances positive responses of attendees, especially for in-person conferences. This effect is further explained by a serial mediation effect, where enhanced perceived values and conference engagement play key roles in improving attendees’ conference experience.

By incorporating festivalization elements, conference organizers can create a more engaging and satisfying event experience for attendees. This can lead to greater satisfaction, positive word-of-mouth and increased registrations.

This study represents a pioneering effort in revealing the underlining mechanisms that explain how festivalization affects attendee engagement and subsequent behaviors in business event management in both face-to-face and virtual settings.

While scholarly attention has mainly focused on team-level or relational constructs for the success of team performance, understanding the inter-play between these two streams of research remains limited in digital transformation projects. Borrowing from social exchange theory, this study aims to explore the antecedents of team alignment leading to team performance with mediation effects of trust, commitment and customer–service provider relationship. The moderating role of relationship length was also examined.

Data was collected using a survey of 180 employees working on digital projects in B2B context, mostly in the Indo-Pacific region, specifically Singapore, Indonesia, Malaysia and India. Partial least squares method with multi-group analyses and bootstrapping method were used to analyze the data.

Findings show that customer control and team capability are the strongest antecedents of team alignment, and inter-play between the customer–provider relationship with team-level constructs is also significant. Relationship length has higher level of moderation impact on trust–team performance link compared to commitment–team performance relationship.

The study considers moderation impact of relationship length on trust, commitment and team performance but not other constructs. Also, gender ratio is skewed in the data set.

Digital transformation practitioners need to be aware of relational constructs (not only team-level constructs) when designing successful long-term digitalization strategies for organizations.

This study is one of the first to document the inter-play between team alignment and relational constructs (such as trust, commitment, and customer–service provider relationship), with moderation impact of relationship length leading to team performance in digital transformation projects.

In a fast-paced and hypercompetitive environment, organizational members are awash with paradoxes where they are forced to accomplish opposing goals simultaneously (“both/and”) instead of choosing one over the other (“either/or”). The literature has acknowledged paradox as a common type of contradiction in managing information and information technology (IT), but few studies have investigated how individuals can leverage paradoxical tensions. Drawing upon paradox theory, this study develops a research model that embodies a “both/and” paradigm in paradoxical tensions via analytical alignment, a paradox mindset and resilience under environmental dynamism.

This study examines the research model using hierarchical regression analysis with 308 analytics experts.

Empirical results find that the alignment of analytical technology and data-driven culture (AT-2DC) has a positive effect on a paradox mindset. Results also show that a paradox mindset has a positive influence on resilience. AT-2DC alignment also mediates the relationship between paradox mindset and resilience. In addition, AT-2DC alignment is more critical to a paradox mindset under a high level of environmental dynamism.

This study sheds light on how individuals can leverage paradoxical tensions with a “both/and” perspective and stay resilient when managing opposing demands and changes.

Limited geometric accuracy is one of the major challenges that hinder the wider application of additive manufacturing (AM). This paper aims to predict in-plane shape deviation for online inspection and compensation to prevent error accumulation and improve shape fidelity in AM.

A sequence-to-sequence model with an attention mechanism (Seq2Seq+Attention) is proposed and implemented to predict subsequent layers or the occluded toolpath deviations after the multiresolution alignment. A shape compensation plan can be performed for the large deviation predicted.

The proposed Seq2Seq+Attention model is able to provide consistent prediction accuracy. The compensation plan proposed based on the predicted deviation can significantly improve the printing fidelity for those layers detected with large deviations.

Based on the experiments conducted on the knee joint samples, the proposed method outperforms the other three machine learning methods for both subsequent layer and occluded toolpath deviation prediction.

This work fills a research gap for predicting in-plane deviation not only for subsequent layers but also for occluded paths due to the missing scanning measurements. It is also combined with the multiresolution alignment and change point detection to determine the necessity of a compensation plan with updated G-code.

Recent advancements in Artificial Intelligence (AI) and, at its core, Machine Learning (ML) offer opportunities for organizations to develop new or enhance existing capabilities. Despite the endless possibilities, organizations face operational challenges in harvesting the value of ML-based capabilities (MLbC), and current research has yet to explicate these challenges and theorize their remedies. To bridge the gap, this study explored the current practices to propose a systematic way of orchestrating MLbC development, which is an extension of ongoing digitalization of organizations.

Data were collected from Finland's Artificial Intelligence Accelerator (FAIA) and complemented by follow-up interviews with experts outside FAIA in Europe, China and the United States over four years. Data were analyzed through open coding, thematic analysis and cross-comparison to develop a comprehensive understanding of the MLbC development process.

The analysis identified the main components of MLbC development, its three phases (development, release and operation) and two major MLbC development challenges: Temporal Complexity and Context Sensitivity. The study then introduced Fostering Temporal Congruence and Cultivating Organizational Meta-learning as strategic practices addressing these challenges.

This study offers a better theoretical explanation for the MLbC development process beyond MLOps (Machine Learning Operations) and its hindrances. It also proposes a practical way to align ML-based applications with business needs while accounting for their structural limitations. Beyond the MLbC context, this study offers a strategic framework that can be adapted for different cases of digital transformation that include automation and augmentation of work.

Drawing on the knowledge-based view, this study investigates how IT–business alignment influences business model design via organizational learning and examines the moderating role of data-driven culture in the relationship between IT–business alignment and business model design via organizational learning.

Using multi-respondent survey data collected from 597 Chinese firms, mediation and moderated mediation analyses were used to examine this study's hypotheses.

The mediation test results revealed organizational learning served as a mediator between IT–business alignment and two types of business model design (i.e. novelty- and efficiency-centered). In addition, data-driven culture strengthened the indirect effects of IT–business alignment on these two types of business model design via organizational learning.

This study extends current understandings of the relationship between IT–business alignment and business model design by revealing the mediating role of organizational learning and investigating its indirect effects under various degrees of data-driven culture. As such, it contributes to the literature on the business model and IT–business alignment and provides insights for managers seeking to achieve the expected business model design.