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This study aims to apply machine learning techniques to efficiently predict leisure firms’ financial performance. Accurate financial forecasting is crucial in leisure and tourism, greatly affecting firms’ strategic decisions and competitive positioning. This study emphasizes the roles of intellectual capital to offer a nuanced understanding of how these types of capital influence firm success.

Using comprehensive firm-level data, this study examines several machine learning algorithms’ predictive capacity across a spectrum of industry sectors (general, manufacturing, service) to identify the most effective model and training dataset. These tools are used to evaluate financial metrics such as return on sales, return on assets and sales growth. A range of variables are incorporated into this process to enhance model accuracy and relevance.

Results demonstrate the support vector machine algorithm’s exceptional performance based on a training data set from the service sector in predicting leisure firms’ return on sales and sales growth. This algorithm is thus an efficacious strategic forecasting instrument. The variables significantly affecting firm performance include demand variation; organizational, product and technological innovation; synergistic innovation between multiple domains; salary levels; market strategy; and the number of employees.

By integrating advanced machine learning techniques with the strategic management of intellectual capital, this study presents a sophisticated approach to predicting leisure firms’ financial performance. Findings enrich the discourse on firm performance forecasting and offer actionable insights into strategic planning and resource allocation for practitioners in the leisure and tourism sectors.

本研究应用机器学习技术来高效预测休闲企业的财务表现。准确的财务预测对于休闲和旅游业至关重要, 极大影响企业的战略决策和竞争定位。本研究强调智力资本的作用, 以深入理解这些资本类型如何影响企业的成功。

本研究使用全面的企业层面数据, 考察多种行业领域（综合、制造、服务）中多个机器学习算法的预测能力, 识别出最有效的模型和训练数据集。这些工具用于评估财务指标, 如销售回报率、资产回报率和销售增长率。过程纳入多种变量, 以提高模型的准确性和相关性。

结果表明, 基于服务行业训练数据集的支持向量机算法在预测休闲企业的销售回报率和销售增长率方面表现出色。因此, 该算法是一种有效的战略预测工具。影响企业绩效的显著变量包括需求变化、组织、产品和技术创新、多领域之间的协同创新、工资水平、市场战略和员工数量。

通过将先进的机器学习技术与智力资本的战略管理相结合, 本研究提出了一种复杂的方法来预测休闲企业的财务表现。研究结果丰富了关于企业绩效预测的讨论, 并为休闲和旅游业的从业者提供了有关战略规划和资源分配的实用见解。

Absorptive capacity is a knowledge-processing ability that hospitality organizations should hone to create competitive advantage in a fierce business environment. This study aims to examine an integrative model explaining how hospitality organizations infuse external knowledge into competitive advantage via absorptive capacity processes and opportunity-capturing abilities.

This study used structural equation modeling, using the R Lavaan package, with 288 survey responses collected from hospitality employees.

Structural equation modeling with multiple indirect relationships presents a holistic picture of how hospitality organizations develop externally acquired knowledge into organizational outcomes through detailed absorptive capacity processes. Unit size is found to positively moderate the indirect relationship between external acquisition and competitive advantage through knowledge transformation only. Competitiveness level negatively moderates indirect relationships through assimilation and transformation.

The findings highlight the importance of hospitality organizations’ knowledge management capabilities through acquisition, assimilation, transformation and exploitation processes. These integrative mechanisms can be facilitated by intraorganizational coordinative processes through collective interpretations and applications of knowledge and effective organizational routines based on management and technical support.

This study proposes an integrative model encompassing a process perspective and the role of intraorganizational coordination in bridging potential and realized absorptive capacity.

This study aims to provide a better understanding of how gift card receivers react to the types of gift cards. This study examined the effect of gift card types (intangible experiences vs less intangible experience vs tangible goods) on a recipient’s willingness to spend more through emotions and perceived effort (Study 1) and on feeling of appreciation (Study 2).

Study 1 adopted a scenario-based 2 (tangible vs intangible) × 3 ($100 vs $200 vs $300) between-subjects design. Study 2 narrowed the scope of gift card type (intangible vs less intangible).

Receivers tended to perceive less effort in gift card selection and feel less emotion when receiving gift cards for intangible experiences than when receiving gift cards for both tangible and less intangible products. However, as face value increased, gift card receivers for intangible experiences felt more pleasure and, in turn, rated higher willingness to spend more money than face value than those with gift cards for tangible products.

Future studies can rule out alternative explanations related to brand-related effects, previous experiences and personal preferences.

Service providers should put more effort into tangibilizing the intangibles to reduce receivers’ uncertainty. Also, they can increase their profitability by stimulating gift card receivers’ willingness to spend more money through pleasure.

Answering research calls for examining consumers’ perceptions of different gift card types, this study might be the first to unveil the differential effect of gift card types associated with the tangibility of products on purchase behavior and the underlying emotional mechanism.