Search results

Growth optimism, which describes the expected future growth of a firm, is an important but underexplored construct in strategy. This paper aims to assess the planning antecedents of such growth optimism by using a large Australian sample of small enterprises.

The authors use a secondary data set, gathered among Australian small to medium enterprises (SMEs), by the Australian Bureau of Statistics (ABS). The analysis adopts a regression approach including a mediated and a non-mediated path to explore the direct and indirect effects of strategic planning and budgetary planning and management on expected future revenues.

This paper assesses the implications of concurrent strategic planning and financial management dynamic capabilities on anticipated future revenue growth, an important predisposition dynamic capability. The authors note that this configuration of actions and predisposition aligns closely with the necessary requirements for growth. The findings suggest that firms that use strategic planning and robust budget planning and monitoring processes exhibit higher optimism about future sales growth and firms that effectively configure these planning activities with market development tend to exhibit higher growth and more growth optimism.

In terms of theoretical contributions, the paper strongly supports the formality view in the formal/informal debates associated with effectuation strategies. The authors suggest that appropriate strategic and budgetary planning and control systems act as a counterbalance to organisational confusion and managerial capriciousness, leading to improved confidence among managers and their employees regarding future resource commitments and plans.

The findings of the paper are potentially important for both managers and policy makers. For managers seeking to grow their future sales, planning is shown to be an important antecedent activity. The presence of financial and strategic planning may predispose firms to make important investment decisions that drive future growth. Also, a better understanding of the firm’s current and future strategic and financial position may be evidence of effective firm management, a situation that, in turn, drives growth.

In terms of social and policy implications, the data gathered for the survey by the ABS forms a valuable collection of information in relation to business practices. Australian firms are required by law to regularly report budget plans and outcomes. The research suggests that this data can inform policy initiatives, particularly in relation to programmes that may assist small and young firms to undertake prospective strategic and budgetary planning.

To the best of the authors’ knowledge, this is the first paper to investigate the particular configuration of strategic and financial planning and anticipated sales growth in the SME context.

The aim of this paper is to provide a narrative review of previous research on tourism demand modelling and forecasting and potential future developments.

A narrative approach is taken in this review of the current body of knowledge.

Significant methodological advancements in tourism demand modelling and forecasting over the past two decades are identified.

The distinct characteristics of the various methods applied in the field are summarised and a research agenda for future investigations is proposed.

本文旨在对先前关于旅游需求建模和预测的研究进行叙述性回顾并对未来潜在发展进行展望。

本文采用叙述性回顾方法对当前知识体系进行了评论。

本文确认了过去二十年旅游需求建模和预测方法论方面的重要进展。

本文总结了该领域应用的各种方法的独特特征, 并对未来研究提出了建议。

El objetivo de este documento es ofrecer una revisión narrativa de la investigación previa sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros.

En esta revisión del marco actual de conocimientos sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros,se adopta un enfoque narrativo.

Se identifican avances metodológicos significativos en la modelización y previsión de la demanda turística en las dos últimas décadas.

Se resumen las características propias de los diversos métodos aplicados en este campo y se propone una agenda de investigación para futuros trabajos.

The current study aimed to examine the moderating role of perceived supervisor support in the nexus between employee empowerment and organizational commitment in the Ghanaian hospitality industry.

A quantitative research design was adopted, and data were collected from 274 frontline workers from 4-star and 5-star hotels at two different waves within a 7-month interval. The data received were analyzed using descriptive and inferential statistics with the aid of Statistical Package for Social Sciences (SPSS V. 23.0) and SmartPLS (V.4.0), respectively.

As hypothesized in the study, employee empowerment was significantly related to organizational commitment. Furthermore, the results revealed that perceived supervisor support moderated the nexus between employee empowerment and affective and continuance commitment but did not moderate the nexus between employee empowerment and normative commitment.

Arguably, support from supervisors has been theoretically identified as a key construct in enhancing subordinates' commitment to an organization. However, less is known in the literature about the moderating role of perceived supervisory support in the nexus between employee empowerment and organizational commitment, especially in the Ghanaian hospitality industry.

This study presents a comprehensive investigation of turnover intention within the context of higher education, specifically focusing on private universities in Egypt, to develop a robust conceptual framework to explore this phenomenon.

The study sample comprised both male and female tenured faculty members from private universities, and data were collected through questionnaires, resulting in 396 completed responses. Statistical analysis was conducted using SPSS and partial least squares structural equation modeling (PLS-SEM) software.

The study highlights the significant impact of work-life balance (WLB) and organizational commitment on turnover intention, with job satisfaction as a mediating factor. Additionally, the research reveals that emotional intelligence (EI) does not directly influence turnover intention, but its effects are fully mediated by job satisfaction.

This research not only advances the theoretical understanding of why academics contemplate leaving their positions but also underscores the significance of this topic. Moreover, by exploring turnover intention in the private education sector of the Middle East, the study addresses a notable gap in the existing literature.

With the growing need for employees to be innovative, public-sector organizations are investing in employee training. This study aims to examine the effects of a combined Lean Six Sigma and innovation training, using action learning, on public-sector employees’ creative role identity and innovative work behavior.

The authors studied a public service agency in Singapore in which a five-day Lean Innovation Training was implemented, using a combination of Lean Six Sigma and Creative Problem-Solving tools, with a simulation on day one and subsequent team-based project coaching, spread over six months. The authors administered pre- and postintervention surveys among all the employees, and initiated group interviews and observations before, during and after the intervention.

Creative role identity and innovative work behavior had significantly improved six months after the intervention, enabled through senior management’s transformational leadership. The training induced managers to role-model innovative work behaviors while cocreating, with their employees, a renewal of their agency’s core processes. The three completed improvement projects contributed to an innovative work culture and reduced service turnaround time.

Starting with a role-playing simulation on the first day, during which leaders and followers swapped roles, the action-learning type training taught all the organizational members to use various Lean Six Sigma and Creative Problem-Solving tools. This nimble Lean Innovation Training, and subsequent team-based project coaching, exemplifies how advancing the staff’s creative role identity can have a positive impact.

The purpose of this study is to answer the following research questions. Does hostility shape the undesirable attitudinal consequences of perceived organizational politics (POP)? If so, does emotional intelligence play a role in this context? To answer these questions, the author relies on the affective events theory to present and empirically investigate a moderated mediation model in which: hostility mediates the relationships between POP and both job tension and turnover intentions; and emotional intelligence moderates these relationships.

The moderated mediation model was tested among a sample of 408 employees. The data was collected in three waves.

The results revealed that hostility mediated the relationships between POP and the two undesirable attitudes explored. In addition, one of the emotional abilities included in emotional intelligence, namely, self-emotion appraisal, moderated these relationships.

Interventions designed to increase the emotional intelligence level of employees might reduce the hostility they experience in response to POP, and consequently, its harmful implications.

Among the four emotional abilities included in emotional intelligence, only self-emotional appraisal moderates the relationship between POP and hostility. Such findings imply that in some cases, a thorough understanding about one’s emotions is more effective in regulating the hostility experienced in response to organizational politics than other emotional abilities that seem more relevant in this context.

Jubail Industrial City is one of the largest industrial centers in the Middle East, offering potential opportunities for renewable energy generation. This research paper presents a comprehensive analysis of the wind resources in Jubail Industrial City and proposes the design of a smart grid-connected wind farm for this strategic location.

The study used wind data collected at three different heights above ground level – 10, 50 and 90 m – over four years from 2017 to 2020. Key parameters, such as average wind speeds (WS), predominant wind direction, Weibull shape, scale parameters and wind power density (WPD), were analyzed. The study used Windographer, an exclusive software program designed to evaluate wind resources.

The average WS at the respective heights were 3.07, 4.29 and 4.58 m/s. The predominant wind direction was from the north-west. The Weibull shape parameter (k) at the three heights was 1.77, 2.15 and 2.01, while the scale parameter (c) was 3.36, 4.88 and 5.33 m/s. The WPD values at different heights were 17.9, 48.8 and 59.3 W/m², respectively.

The findings suggest that Jubail Industrial City possesses favorable wind resources for wind energy generation. The proposed smart grid-connected wind farm design demonstrates the feasibility of harnessing wind power in the region, contributing to sustainable energy production and economic benefits.

The paper aims to examine the effect of justice perceptions of performance appraisal (JPPA) practices (i.e. distributive, procedural, informational and interpersonal justice) on organizational citizenship behavior (OCB) and affective commitment (AC) and the effect of AC on OCB. Further, it investigates the mediating role of AC in the relationship between JPPA practices and OCB. Moreover, this study examines the moderating effect of job level on the relationship between JPPA practices and OCB.

The data were collected using a self-reported structured questionnaire. A total of 650 questionnaires were distributed among the employees of 50 information technology (IT) companies in India, and 503 samples were obtained. The conceptual framework was tested using the partial least squares structural equation modeling (PLS-SEM) method, and the moderating effect was tested using process macro.

The findings of this study reveal that the JPPA practices positively affect OCB and AC and AC affects OCB. Further, AC partially mediates this relationship between JPPA practices and OCB. Furthermore, the direct effect of JPPA practices on OCB happens to be strengthened when the job level decreases, thus confirming the moderating role of job level.

The findings of this study augment the social exchange theory (SET) by suggesting that individuals perceiving justice or fairness in performance appraisal practices are likely to have a greater AC that ultimately engages employees in OCB.

This study will be helpful for human resource practitioners in IT companies who are responsible for the fairness of performance appraisal practices and expect their employees to be emotionally attached to the organization and engaged in OCB.

The study adds to the body of knowledge of how justice in performance appraisal practices links to OCB through AC and moderates by job level in an emerging economy in Asia.

This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic response signals.

The paper conducts time-frequency analysis on signals of pavement dynamic response initially. It also uses two common noise reduction methods, namely, low-pass filtering and wavelet decomposition reconstruction, to evaluate their effectiveness in reducing noise in these signals. Furthermore, as these signals are generated in response to vehicle loading, they contain a substantial amount of data and are prone to environmental interference, potentially resulting in outliers. Hence, it becomes crucial to extract dynamic strain response features (e.g. peaks and peak intervals) in real-time and efficiently.

The study introduces an improved density-based spatial clustering of applications with Noise (DBSCAN) algorithm for identifying outliers in denoised data. The results demonstrate that low-pass filtering is highly effective in reducing noise in pavement dynamic response signals within specified frequency ranges. The improved DBSCAN algorithm effectively identifies outliers in these signals through testing. Furthermore, the peak detection process, using the enhanced findpeaks function, consistently achieves excellent performance in identifying peak values, even when complex multi-axle heavy-duty truck strain signals are present.

The authors identified a suitable frequency domain range for low-pass filtering in asphalt road dynamic response signals, revealing minimal amplitude loss and effective strain information reflection between road layers. Furthermore, the authors introduced the DBSCAN-based anomaly data detection method and enhancements to the Matlab findpeaks function, enabling the detection of anomalies in road sensor data and automated peak identification.

The purpose of this paper is to develop a blockchain-based data capture and transmission system that will collect real-time power consumption data from a household electrical appliance and transfer it securely to a local server for energy analytics such as forecasting.

The data capture system is composed of two current transformer (CT) sensors connected to two different electrical appliances. The CT sensors send the power readings to two Arduino microcontrollers which in turn connect to a Raspberry-Pi for aggregating the data. Blockchain is then enabled onto the Raspberry-Pi through a Java API so that the data are transmitted securely to a server. The server provides real-time visualization of the data as well as prediction using the multi-layer perceptron (MLP) and long short term memory (LSTM) algorithms.

The results for the blockchain analysis demonstrate that when the data readings are transmitted in smaller blocks, the security is much greater as compared with blocks of larger size. To assess the accuracy of the prediction algorithms data were collected for a 20 min interval to train the model and the algorithms were evaluated using the sliding window approach. The mean average percentage error (MAPE) was used to assess the accuracy of the algorithms and a MAPE of 1.62% and 1.99% was obtained for the LSTM and MLP algorithms, respectively.

A detailed performance analysis of the blockchain-based transmission model using time complexity, throughput and latency as well as energy forecasting has been performed.