Purpose

This paper aims to understand the most efficient hotel system and why efficiency varies across years and between the two differing types of hotel businesses in Turkey.

Design/methodology/approach

A data envelopment analysis (DEA) analysis was used to characterise the efficiency of all-inclusive (AI) and bed and breakfast (B&B) hotel businesses with one output (total revenue) and three inputs (labour, food and capital costs). The Malmquist approach is then used to discern changes in total efficiency (TTE) and intertemporal shifts in the efficiency frontier (technological change (Tch)).

Findings

The results reveal that the AI hotel operates at 100% efficiency in the summer and year-round. The B&B hotel business operates at 89.6% with variable constant returns to scale during the summer and with 100% efficiency. The results of the Malmquist approach indicate that the total factor productivity grew in the years 2015, 2016, 2018 and 2019, while the other years were marked by inefficiency. Such increases were due to technical efficiency change (TEch) and Tch, which means that managerial and allocative efficiency (AE) were barely achieved. Slight differences were noted in the two time periods (all year and summer), suggesting that the scale of hotel businesses is prepared to operate all year round, and this calls for strategies to mitigate seasonality.

Research limitations/implications

As to avenues for future research, the limitations of this study are threefold. First, the hotel businesses are not parallel in terms of the duration of their service offerings. Future research may consider including an AI hotel business that is in operation for the whole year. Second, businesses in Turkey are sceptical about sharing their data as it is considered confidential. However, to better generalise the results and encourage hoteliers to consider the positive outcomes of such analysis, the number of observations could be increased by considering more hotel businesses in both categories. Third, a mixture of data representing businesses operating in various countries may reflect if the efficiency scores vary internationally.

Practical implications

Overall, AI hotel businesses are more attractive but less efficient than B&B. Furthermore, the external crisis impacts the efficiency of hotel businesses meaning that hotel managers could keep on exploring AI, perhaps educating their hosts not to waste or not offer huge quantities. Hotel managers may also need to enlarge their seasonal activities to ensure more efficiency.

Social implications

Despite the intentions of AI hotel businesses to increase their profitability with a lower level of service quality, this study shows that the AI hotel business is very attractive but not so efficient due to the higher propensity of guests to consume food and beverages in excess that compromises the definition of efficiency as zero waste. AI is very attractive for family groups or those seeking the pleasure of relaxation at seaside resorts and is also very popular in Turkey. On the other hand, the B&B hotel business is more efficient but less attractive.

Originality/value

The contributions of this paper are threefold. First, the authors analysed the efficiency and inefficiency of hotel businesses within nine years of operations. During this period, Turkey experienced first a tourism boom (2011–2014) followed by stagnation and subsequently a sharp decline due to political instability resulting in an (in)direct impact on tourism (2015–2019). Second, the authors compared the efficiency and inefficiency of AI and B&B hotel businesses. Third, the authors examined the effects of hotel management factors to ensure efficiency.

中文翻译：

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比较全包式和含早餐酒店业务的效率：土耳其的多时期数据包络分析

目的

本文旨在了解最高效的酒店系统，以及为何效率在土耳其不同年份和两种不同类型的酒店业务之间存在差异。

设计/方法/方法

数据包络分析 (DEA) 分析用于描述具有一项产出（总收入）和三项投入（劳动力、食品和资本成本）的全包式 (AI) 和住宿加早餐 (B&B) 酒店业务的效率。然后使用 Malmquist 方法来识别总效率 (TTE) 的变化和效率前沿的跨期变化（技术变革 (Tch)）。

发现

结果显示，人工智能酒店在夏季和全年都以 100% 的效率运营。B&B 酒店业务的运营率为 89.6%，夏季期间规模收益可变，效率为 100%。Malmquist 方法的结果表明，全要素生产率在 2015 年、2016 年、2018 年和 2019 年均有所增长，而其他年份则以低效率为特征。这种增长是由于技术效率变化（TEch）和Tch，这意味着管理和配置效率（AE）几乎没有实现。两个时间段（全年和夏季）略有不同，这表明酒店业务的规模已准备好全年运营，这需要采取缓解季节性的策略。

研究限制/影响

至于未来研究的途径，本研究的局限性是三重的。首先，酒店业务在提供服务的期限方面并不平行。未来的研究可能会考虑包括一个全年运营的人工智能酒店业务。其次，土耳其的企业对共享他们的数据持怀疑态度，因为它被认为是机密的。然而，为了更好地概括结果并鼓励酒店经营者考虑此类分析的积极成果，可以通过在这两个类别中考虑更多酒店业务来增加观察次数。第三，代表在不同国家经营的企业的混合数据可能反映效率得分在国际上是否存在差异。

实际影响

总体而言，人工智能酒店业务比 B&B 更具吸引力，但效率较低。此外，外部危机会影响酒店业务的效率，这意味着酒店经理可以继续探索人工智能，或许可以教育他们的主人不要浪费或不提供大量的产品。酒店经理可能还需要扩大他们的季节性活动以确保更高的效率。

社会影响

尽管人工智能酒店企业打算以较低的服务质量来提高盈利能力，但这项研究表明，人工智能酒店业务非常有吸引力，但效率不高，因为客人更倾向于过度消费食物和饮料，这会影响将效率定义为零浪费。人工智能对于家庭团体或在海滨度假胜地寻求放松乐趣的人非常有吸引力，在土耳其也很受欢迎。另一方面，民宿酒店业务效率更高，但吸引力较小。

原创性/价值

本文的贡献是三方面的。首先，作者分析了酒店企业在运营九年内的效率和低效率。在此期间，土耳其首先经历了旅游业的繁荣（2011-2014 年），然后是停滞不前，随后由于政治不稳定而急剧下降，从而对旅游业产生了直接影响（2015-2019 年）。其次，作者比较了 AI 和 B&B 酒店业务的效率和低效率。第三，作者检验了酒店管理因素对确保效率的影响。