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The purpose of this paper is to assess the influence of patients’ expectations from healthcare service quality on their satisfaction with nursing in public and private hospitals of Pakistan.

Data (n=456) were collected from three public sector hospitals and three private sector hospitals of Lahore, the capital of Pakistan’s most populous province. Male and female patients who have experience of both sectors were surveyed using a self-administered questionnaire developed using the original SERVQUAL approach. Data were analyzed using the statistical techniques and the Laplace criterion.

This paper attempts to explain degree of influences of five service quality constructs (empathy, responsiveness, tangibility, reliability and assurance) on Pakistani patients’ expectations from the private and public sector hospitals and thus patient satisfaction. Further, this work can offer several intuitions into the effect of five constructs of service quality on patients’ expectations of healthcare service quality and patient satisfaction with the service providers/nursing. The results reveal that the patient satisfaction is most strongly related to empathy in public sector and to responsiveness in private sector.

In light of the previous studies and the current research findings, the study anticipates no apparently significant improvement in healthcare sector of Pakistan in near future considering various factors discussed in the study. The study will also help the service providers and the policy makers in understanding the deteriorating situation of the Pakistani healthcare sector and will guide them in identifying the areas by improving which not only the healthcare service quality in the country can be improved but also the image of healthcare sector among the masses and competitiveness of the healthcare sector can be enhanced.

The value of the study rests in its critical analysis of the current status of the healthcare sector of Pakistan with a view to suggest the areas that need to be worked on by the service providers and policy makers. Also, the study tries to settle a controversy within Pakistani healthcare literature concerning the question that who is producing more satisfied patients: private hospitals or their public counterparts?

The purpose of this paper is to empirically analyze the effect of the relationship between trust in top management (TTM) and trust in immediate supervisor (TIS), who was organizational project manager in our case, on perceived organizational performance in Pakistani public and private project-based organizations (PBOs).

The survey (N=108) was done using a questionnaire that was sent to project managers in the selected PBOs in Pakistan with a request to forward it to their immediate subordinates. Later, established statistical techniques (correlation and regression analyses) and gray incidence analysis models were applied to test the hypotheses.

The results from both methods reveal that TTM was more strongly correlated to perceived organizational performance of PBOs and, in general, public sector employees are more trusted than private sector employees. The gray method revealed that in both private and public PBOs, trust in project manager is greater predictor of perceived organizational performance, while statistical analysis confirmed this only for private sector PBOs. According to statistical analysis, the public sector employees who trust their top management are more likely to have good perception of the organizational performance. Later, the study argues that because of the proven superiority of gray methods over statistics on small samples, the results obtained from gray method should be used for decision making and implications.

The study is pioneer in evaluating the association between TIS and TTM in PBOs using both statistical and gray systems methods.

The study aims to introduce two new models of project scheduling by incorporating potential quality loss cost (PQLC) in time–cost tradeoff problems by overcoming the drawbacks of the existing Kim, Khang and Hwang (KKH) model. The proposed methods are named the Revised KKH-I (RKKH-I) and Revised KKH-II (RKKH-II) models for project scheduling.

The performance of the existing KKH model has been tested using a numerical example followed by the identification of the main shortcomings of the KKH method. Later, a concrete effort has been made to address its shortcomings while improving its performance significantly. The comparative analysis of the Revised KKH models with the original model has also been presented along with sensitivity analyses.

The study recognizes that the construct on which the original KKH method was built is important; however, certain drawbacks make it unable to consider PQLC in projects, thus making its practical use questionable. The comparative analysis of the proposed methodology with the original method demonstrated that the new models (RKHH-I and II) are more comprehensive and intelligent than the existing KKH model.

The comparative analysis of the original KKH model and its improved version reveals that the revised model is far more suitable for project scheduling. The study is important for project managers who recognize project scheduling being one of the key parameters associated with project management process, crucial to control every day during the management of projects.

The purpose of this paper is to develop a new approach for equipment states prediction and provide a method for early warning of possible trouble states.

A new two-stage multi-level equipment state classification system was proposed to forecast equipment operation status. The first stage involves predicting the equipment's normal state, and the second stage involves forecasting the equipment's abnormal status. Meanwhile, the equipment state classification is done according to the manufacturing company's internal specifications to define various equipment statuses. Then, the trouble state and waiting state were predicted by grey state prediction model.

A new two-stage multi-level equipment status classification system and a new approach for equipment states prediction has been proposed in this paper.

The application on a real-world case shown that the model is very effective for predicting equipment state. The equipment's major failure risk can be reduced significantly.

The proposed approach can help improve the effective prediction of the equipment's various operation states and reduce the equipment's major failure risk and thus maintenance costs.

To test the applicability of the user-generated content (UGC) derived from social travel network sites for online reputation management, the purpose of this study is to analyze the spatial clustering of the reputable hotels (based on the TripAdvisor Best-Value indicator) and reputable outdoor seating restaurants (based on ranking indicator).

This study used data mining techniques to obtain the UGC from TripAdvisor. The Hierarchical Density-Based Spatial Clustering method based on algorithm (HDBSCAN) was used for robust cluster analysis.

The findings of this study revealed that best value (BV) hotels and reputable outdoor seating restaurants are most likely to be located in and around the central districts of the urban tourist destinations where population and economic activities are denser. BV hotels' spatiotemporal cluster analysis formed clusters of different sizes, densities and shape patterns.

This study showed that reputable hotels and restaurants (H&Rs) are concentrated within districts near historic city centers. This should be an impetus for applied research on urban investment environments.

The findings would be rational guidance for entrepreneurs and potential investors on the most attractive tourism investment environments.

There has been a lack of studies focusing on analyzing the spatial clustering of the H&Rs using UGC. Therefore, to the best of the authors’ knowledge, this study is the first to map and analyze the spatiotemporal clustering patterns of reputable hotels (TripAdvisor BV indicator) and restaurants (ranking indicator). As such, this study makes a significant methodological contribution to urban tourism research by showing pattern change in H&Rs clustering using data mining and the HDBSCAN algorithm.

为了测试社交旅游网站 (STNS) 的用户生成内容 (UGC) 对在线声誉管理 (ORM) 的适用性, 本研究分析了知名酒店的空间聚类（基于 TripAdvisor 最佳价值指标) 和信誉良好的户外座位 (ODS) 餐厅(基于排名指标)。

该研究使用数据挖掘技术从 TripAdvisor 获取 UGC。 基于（HDBSCAN）算法的分层基于密度的空间聚类方法用于鲁棒聚类分析。

调查结果显示, 最具价值 (BV) 酒店和信誉良好的 ODS 餐厅最有可能位于人口和经济活动较为密集的城市旅游目的地的中心区及其周边地区。 BV 酒店的时空聚类分析形成了不同大小、密度和形状模式的聚类。

目前的文献扔缺乏专注于分析利用 UGC 的酒店和餐厅 (H&R) 空间聚类的研究。 因此, 本研究首次绘制并分析了知名酒店（TripAdvisor BV 指标）和餐厅（排名指标）的时空聚类模式。 因此, 本研究通过利用数据挖掘和 HDBSCAN 算法显示 H&Rs 聚类的模式变化, 为城市旅游研究做出了重要的方法论贡献。

这项研究表明, 著名的 H&R 集中在历史悠久的市中心附近的地区。 这应该是对城市投资环境的应用研究的推动力。

研究结果将为企业家和潜在投资者提供最具吸引力的旅游投资环境的理性指导。

The Egyptian Premier League (EPL) holds a prominent place in Egypt’s sporting culture and serves as the stage for some of Africa’s most competitive soccer clubs. However, the actual competitive balance in this league has come under scrutiny in recent years as the two historically dominant Cairo clubs, Ahly and Zamalek, continue to retain the EPL championship. A major concern is that the competitive imbalance of the EPL may actually be hampering the league’s progress and the progress of soccer in Egypt. In order to more closely assess this situation, the purpose of this paper is to use historical EPL performance data to conduct a series of competitive balance analyses on league results from 1948 to 2014. The findings revealed that competition in the league is almost nonexistent as Ahly and Zamalek continue to enjoy a number of direct and indirect financial benefits that are unrealized by their competitors. The dominance of these clubs has compromised the elements of match uncertainty and drama that are traditionally viewed as being important to the prestige and financial achievements of leagues and teams. Discussion is therefore offered for how the EPL could go about resolving some of its organizational and competitive balance issues.

This study adopted four basic measures of competitive balance to conduct descriptive analyses on EPL data that were collected from egyptianfootball.net and the Rec Sport Soccer Statistics Foundation. These analyses began with the EPL’s inaugural season (1948-1949) and extended to the conclusion of the 2014-2015 season. During this timeframe, seven seasons were canceled due to global and political tensions and four more went unfinished. Because these seasons were excluded, the total sample size consisted of 56 seasons, each of which contained between 10 and 24 EPL teams. The data were analyzed using variations of the following competitive balance measures: the range and standard deviation of winning percentages, the ratio of the standard deviation/Noll-Scully approach, the Herfindahl-Hirschman Index, and five-club concentration ratio.

The results confirmed that the league is largely imbalanced, leading the authors to recommend systemic and structural changes that could help promote competitive balance in the league. The call for competitive balance in the EPL was bolstered by a literature review of studies that advocated for parity in professional sports leagues. In the end, the researchers recommend the EPL to improve its organizational policies and consider a revised revenue-sharing system that would allow the small-market teams to survive and thrive.

The EPL holds a prominent place in Egypt’s sporting culture and serves as the stage for some of Africa’s most competitive soccer clubs. The primary purpose of this study was to perform a series of competitive balance analyses on EPL results from 1948 to 2015 in an effort to better understand the various degrees of competitiveness in the league during this time.

The purpose of this paper is to analyse the relationship between outpatient satisfaction and the five constructs of healthcare projects’ service quality in Pakistan using Deng’s grey incidence analysis (GIA) model, absolute degree GIA model (ADGIA), a novel second synthetic degree GIA (SSDGIA) model and two approaches of decision-making under uncertainty.

The study proposes a new synthetic GIA model and demonstrates its feasibility on data (N=221) collected from both public and private sector healthcare projects of Punjab, the most populous province of Pakistan, using a self-administered questionnaire developed using the original SERVQUAL approach.

The results of decision analysis approach indicated that outpatients’ satisfaction from the private sector healthcare projects is higher as compared to the public healthcare projects’. The results from the proposed model revealed that tangibility and reliability play an important role in shaping the patient satisfaction in the public and private sectors, respectively.

The study is pioneer in evaluating a healthcare system’s service quality using grey system theory. The study proposes the SSDGIA model as a novel method to evaluate parameters comprehensively based on their mutual association (given by absolute degree of grey incidence) and inter-dependencies (given by Deng’s degree of grey incidence), and tests the new model in the given scenario. The study is novel in terms of its analysis of data and modelling. The study also proposes a comprehensive structure of the healthcare delivery system of Pakistan.

Tourism industry is a highly complex system surrounded by many uncertainties because of its innumerable connections with other supporting systems. Considering tourism, a grey system, the current study proposes optimistic–pessimistic method (OPM). This technique can aid in improving forecast accuracy of four tourism-related indicators, inbound tourism to China, outbound tourism from China, revenues collected through inbound tourism and expenses incurred on outbound tourism.

The study integrates OPM into EGM and then using the secondary data collected from the World Bank database, predicts the four tourism-related indicators. The mean absolute percentage error steered the performance of the models.

One of the main contributions of the study lies in its overall evaluation of one of the major travel and tourism countries of the world in light of four crucial indicators. The study highlights, four tourism-related indicators' recent information, contains more valuable information about the future.

OPM represents a novel application of concept of whitenization of interval grey number in grey forecasting theory.

In the current study, two grey prediction models, Even GM (1, 1) and Non-homogeneous discrete grey model (NDGM), and ARIMA models are deployed to forecast cocoa bean production of the six major cocoa-producing countries. Furthermore, relying on Relative Growth Rate (RGR) and Doubling Time (D_t), production growth is analyzed.

The secondary data were extracted from the United Nations Food and Agricultural Organization (FAO) database. Grey forecasting models are applied using the data covering 2008 to 2017 as their performance on the small sample size is well-recognized. The models' performance was estimated through MAPE, MAE and RMSE.

Results show the two grey models fell below 10% of MAPE confirming their high accuracy and forecasting performance against that of the ARIMA. Therefore, the suitability of grey models for the cocoa production forecast is established. Findings also revealed that cocoa production in Côte d'Ivoire, Cameroon, Ghana and Brazil is likely to experience a rise with a growth rate of 2.52, 2.49, 2.45 and 2.72% by 2030, respectively. However, Nigeria and Indonesia are likely to experience a decrease with a growth rate of 2.25 and 2.21%, respectively.

For a sustainable cocoa industry, stakeholders should investigate the decline in production despite the implementation of advanced agricultural mechanization in cocoa farming, which goes further to put food security at risk.

The study presents a pioneering attempt of using grey forecasting models to predict cocoa production.