Eugene Woon and Augustine Pang
Information vacuums (IVs) arise from organizational failure to satisfy the stakeholders’ informational demands during crises. The purpose of this paper is to expand Pang’s (2013…
Abstract
Purpose
Information vacuums (IVs) arise from organizational failure to satisfy the stakeholders’ informational demands during crises. The purpose of this paper is to expand Pang’s (2013) study of the phenomenon of IV by investigating its nature, stages, intensifying factors and resolution.
Design/methodology/approach
Print and social media data of five recent international crises with apparent IVs were analyzed.
Findings
Poor crisis communications are intensifying factors that induce media hijacks and hypes, distancing, and public confusion. A four-stage model maps the phenomenon into a flow chart describing its development. IV termination begins when organizations either respond with information or provide solutions, results, and/or compensation. Natural and strategic silence were observed and defined.
Research limitations/implications
The study lays the foundation for future examination of how media literacy, governments, and culture, both societal and organizational, induce or exacerbate the phenomenon.
Practical implications
Immediate, adequate, transparent, credible, and consistent crisis responses manage the IV and crisis, diminish the intensification of subsequent crises, and potentially reduce image and reputational damages.
Originality/value
The knowledge of the phenomenon is further developed and new theoretical models are conceptualized to provide researchers and practitioners a clearer understanding of how an IV can develop, persist, deepen, and resolve.
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Augustine Pang, Ratna Damayanti and Eugene Yong-Sheng Woon
In 2015, Malaysia’s investment vehicle, 1Malaysia Development Berhad (1MDB), came under international scrutiny after it amassed a debt of US$11 billion (10.3 billion) (Wright &…
Abstract
In 2015, Malaysia’s investment vehicle, 1Malaysia Development Berhad (1MDB), came under international scrutiny after it amassed a debt of US$11 billion (10.3 billion) (Wright & Clark, 2015), which it had difficulty repaying. More disturbingly, investigators found that US$700 million (658 million) was transferred into the personal bank account of Malaysia’s prime minister, Najib Razak, founder and chairman of 1MDB’s advisory board (Wright & Clark, 2015). Najib was also accused of embezzling state money (Reuters, 2015) and damaging the image of the country (“Najib tried to bribe me”, 2015). This chapter aims to examine the strategies used by the Malaysian prime minister to repair his image in the 1MDB scandal, the effectiveness of these strategies, and how these impacted Malaysia’s public diplomacy efforts in restoring the country’s image and reputation. Findings showed that the prime minister denied wrongdoing, and simultaneously bolstered his position and promised to turn 1MDB around. In contrast to the current explication of Benoit and Pang’s (2008) image repair strategies, Najib’s way of attacking the accusers sheds light into how image repair strategies may be operationalized in the Asian context. A new image repair strategy – diversion – is proposed to be added to the existing framework.
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Barrie O. Pettman and Richard Dobbins
This issue is a selected bibliography covering the subject of leadership.
Abstract
This issue is a selected bibliography covering the subject of leadership.
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Eugene Cheng-Xi Aw, Garry Wei-Han Tan, Keng-Boon Ooi and Nick Hajli
The present study aims to propose a framework elucidating the attributes of mobile augmented reality (AR) shopping apps (i.e., spatial presence, perceived personalization and…
Abstract
Purpose
The present study aims to propose a framework elucidating the attributes of mobile augmented reality (AR) shopping apps (i.e., spatial presence, perceived personalization and perceived intrusiveness) and how they translate to downstream consumer-related outcomes (i.e., immersion, psychological ownership and stickiness to the retailer).
Design/methodology/approach
By conducting a questionnaire-based survey, 308 responses were collected, and the data were submitted to partial least square structural equation modeling (PLS-SEM) and artificial neural network (ANN) analyses.
Findings
A few important findings were generated from the present study. First, attributes of mobile augmented reality shopping apps (i.e., spatial presence, perceived personalization and perceived intrusiveness) influence stickiness to the retailer through immersion and consumer empowerment in serial. Second, immersion positively influences psychological ownership. Third, the optimum stimulation level moderates the relationship between spatial presence and immersion. Lastly, a post-hoc exploratory finding yielded by the multigroup analysis uncovered the moderating effect of gender.
Originality/value
This study offers a novel contribution to the smart retail literature by investigating the role of mobile AR shopping apps in predicting consumers' stickiness to the retailer. A holistic framework elucidating the serial mediating effect of immersion and consumer empowerment, and the moderating roles of optimum stimulation level and gender were validated.
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Pablo Leão, Caio Coelho, Carla Campana and Marina Henriques Viotto
The present study aims to investigate an unsuccessful implementation of an active learning methodology. Active learning methods have emerged in order to improve learning processes…
Abstract
Purpose
The present study aims to investigate an unsuccessful implementation of an active learning methodology. Active learning methods have emerged in order to improve learning processes and increase students' roles in the classroom. Most studies on the subject focus on developing learning strategies based on successful implementations of such methods. Nevertheless, critical reflections on unsuccessful cases might also provide material for developing further contributions to this literature.
Design/methodology/approach
The authors conducted an intrinsic case study of an unsuccessful application of the flipped classroom method to an undergraduate basic statistics course at a Brazilian business school. The data collected comprised the course's syllabus, evaluation forms and two rounds of interviews with students and the professor.
Findings
The findings indicate that, apart from that which had been mapped by past literature, three additional aspects may limit the chances of successfully implementing a flipped classroom methodology: students' educational backgrounds, the course's structural issues and methodological and relational issues.
Originality/value
The present study contributes to the literature on active learning methodologies mainly by mapping additional aspects that should be considered in the implementation of the flipped classroom methodology. Additionally, the authors investigate an unsuccessful case of such an implementation, an investigation that is still scant within this literature.
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Mariam AlKandari and Imtiaz Ahmad
Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on climate…
Abstract
Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on climate conditions, which fluctuate over time. In this research, we propose a hybrid model that combines machine-learning methods with Theta statistical method for more accurate prediction of future solar power generation from renewable energy plants. The machine learning models include long short-term memory (LSTM), gate recurrent unit (GRU), AutoEncoder LSTM (Auto-LSTM) and a newly proposed Auto-GRU. To enhance the accuracy of the proposed Machine learning and Statistical Hybrid Model (MLSHM), we employ two diversity techniques, i.e. structural diversity and data diversity. To combine the prediction of the ensemble members in the proposed MLSHM, we exploit four combining methods: simple averaging approach, weighted averaging using linear approach and using non-linear approach, and combination through variance using inverse approach. The proposed MLSHM scheme was validated on two real-time series datasets, that sre Shagaya in Kuwait and Cocoa in the USA. The experiments show that the proposed MLSHM, using all the combination methods, achieved higher accuracy compared to the prediction of the traditional individual models. Results demonstrate that a hybrid model combining machine-learning methods with statistical method outperformed a hybrid model that only combines machine-learning models without statistical method.