Hakeem-Ur- Rehman

The rapid adoption of global mandatory Supply Chain Due Diligence (SCDD) regulations has resulted in cascading obligations for suppliers in exporting countries. However, supplier adoption of SCDD practices is still low, given these regulations. This study investigates the barriers preventing firms in exporting countries from adopting SCDD practices. The study is based on thematic analysis of 24 semi-structured interviews with supply chain practitioners from a variety of industries and countries. The SCDD barriers identified are: Financial Constraints, Low Awareness and Training, Resistance to Change, Weak Enforcement, Corruption, Power Asymmetry, and Tier 2/3 invisibility. The study is the first empirically based supplier SCDD barrier framework and extends the institutional theory by showing that coercive pressures are mediated through enforcement quality and power relations. Practical implications are offered for buyers, policymakers, industry associations, and suppliers seeking to address these barriers and enable meaningful SCDD adoption in exporting countries.

Purpose: Increasingly available online reviews of physicians by patients offer an opportunity to explore physician service quality using text mining techniques. However, such studies have so far been conducted mainly in US, UK and China. The current study aims to identify desirable attributes of physicians from online reviews in a developing country, Pakistan. Design/methodology/approach: 60,458 usable online reviews of Pakistani physicians were scraped from two popular physician rating websites of Pakistan. Topic modeling and salience analysis were employed to analyze these reviews. Findings: Results show that competence is the most important attribute of the physician for Pakistani patients, followed by attention, communication, manners and treatment efficacy in the same order. Comparison with other studies conducted in US, UK and China reveals that some physician attributes like competence and manners are valued universally, but some others vary with culture. Originality/value: The study contributes significantly to filling the knowledge gap related to patient preferences in developing countries. It also underscores the value of user-generated content available on online forums and offers practical insights to improve patients' healthcare experience.

The supply chain process of any company must be smooth and without delays. However, when the process is interfered with or choked by unfavourable factors, it leads to disruptive company operations. The purpose of this study is to manage the supply chain risks through supply chain integration and quality management practices, leading to improved operational performance. SmartPLS was used to test the hypotheses and data was collected from the 204 manager-level employees of textile organisations located in Lahore, Faisalabad, Multan and Karachi. Judgemental sampling was used for data collection. Before the formal introduction, the questionnaire was thoroughly pre-tested with company executives. The findings showed that firms can reduce supply chain risks by using integrative practices with suppliers and consumers, which can improve the operational performance of the organisation. Internal and external integration of all parties involved for improved operational performance is established on the foundation of a quality management culture. Future research could look at these connections over time and across sectors and environments.

Purpose: There is a growing trend in online shopping in developing and underdeveloped economies. Measuring web retail service quality is vital to enhance and retain online customers for higher profits. As a result, it is essential to understand e-service quality and its relationship with other e-commerce-related variables identified as customer satisfaction, word-of-mouth (WOM), loyalty and repurchase intention. The main objective of this research is to validate the psychometric properties of E-TailQ and ES-Qual scales to measure e-service quality in the context of an emerging economy.Design/methodology/approach: The study was administered by surveying online customers through a convenience sampling approach. The sample data consisted of 329 valid responses. The data were analyzed using confirmatory factor analysis (CFA) and structural equation modeling (SEM).Findings: The results suggest that both the scales are valid, reliable and adequate and confirm the psychometric properties of E-TailQ and ES-Qual scales in an emerging economy context. There is a positive and significant relationship between latent variables (E-TailQ and ES-Qual) and customer outcome variables like satisfaction, loyalty, WOM and repurchase intention. Additionally, the coefficients of the E-TailQ scale have higher values than ES-Qual scales in all dimensions. The perceived quality proved to be a precedent of satisfaction to a lesser extent than other scales' constructs, and it proved to be a superior predictor of WOM for the E-TailQ scale and loyalty for the ES-Qual scale. Overall, when measured by the ES-Qual scale, perceived quality could explain much of the sample's satisfaction, loyalty and repurchase intention compared to E-TailQ.Research limitations/implications: One of the significant limitations of this study is related to the data collection, as individual responses were collected at one particular point in time. Thus, it is impossible to verify the development of perceived service quality outcomes over a specific time. Another possible limitation is the selection of only two widely used scales, although other options are available in the literature. Finally, developing a new scale to address future challenges might be another alternative.Practical implications: The results obtained in the study will help managers assess customer satisfaction, the reputation of the business through WOM, and customer intentions for the repurchase that will lead to loyalty by emphasizing e-service quality. It will enable them to design strategies for improving business performance. The developed instrument will assist in identifying the gaps and describing how service quality can achieve higher levels of customer satisfaction.Originality/value: Several studies in the literature have used E-TailQ and ES-Qual scales to measure e-service quality in developed countries. Only a few studies addressed the validity of these widely used competing scales in an emerging economy like Pakistan. This study addresses the literature gap by addressing the reliability and validity of these scales and assesses the impact of e-commerce-related variables. Besides, the study reveals insights into the literature by addressing multiple dimensions of e-service quality and summarizes a deep understanding of each scale item that affects the perceived quality in a developing economy like Pakistan.

People with minor medical conditions in China tend to prefer healthcare consultation in the emergency department of tertiary hospitals and avoid public sector community health centers (PS-CHC). The reason behind this is the trust deficit in the quality of medical care at PS-CHC. On the other hand, Chinese m-health companies have a surprising number of registered users and daily healthcare queries. However, the lack of physical check-ups causes a lack of trust in the quality of prescribed treatments for m-health users. One such m-health company entered into a joint venture with a local government body. In this joint venture, the doctors from tertiary hospitals can provide healthcare consultation to m-health users at PS-CHC. This study investigates m-health users’ intention to use m-health services equipped with PS-CHC, an emerging public-private joint venture in China. We extended the technology acceptance model with government support and trust to predict the adoption of such technology-based public-private joint ventures in a better way. The data were collected from 432 m-health users in Hefei and analyzed using Smart-PLS. All eight direct, but only three mediating relationships were to be found significant. This study has implications for researchers, government healthcare authorities, and m-health service providers.

Hierarchical time series arise in various fields such as manufacturing and services when the products or services can be hierarchically structured. “Top‐down” and “bottom‐up” forecasting approaches are often used for forecasting such hierarchical time series. In this paper, we develop a new hybrid approach (HA) with step‐size aggregation for hierarchical time series forecasting. The new approach is a weighted average of the two classical approaches with the weights being optimally chosen for all the series at each level of the hierarchy to minimize the variance of the forecast errors. The independent selection of weights for all the series at each level of the hierarchy makes the HA inconsistent while aggregating suitably across the hierarchy. To address this issue, we introduce a step‐size aggregate factor that represents the relationship between forecasts of the two consecutive levels of the hierarchy. The key advantage of the proposed HA is that it captures the structure of the hierarchy inherently due to the combination of the hierarchical approaches instead of independent forecasts of all the series at each level of the hierarchy. We demonstrate the performance of the new approach by applying it to the monthly data of ‘Industrial’ category of M3‐Competition as well as on Pakistan energy consumption data.

Purpose: In today's global economy, developing supply chain agility (SCA) and lean practices (LP) as resource-based view and dynamic capabilities are essential for firms to sustain their competitive advantage (CA) and enhance their operational performance (OP). The purpose of this paper is to develop and empirically test a framework to investigate how CA is achieved through SCA and LP and how these, in turn, can enhance a firm's OP.Design/methodology/approach: For data collection, the authors adopted the survey method using self-administered questionnaires. Two-source survey data were collected in two rounds (separated by a two-month lag time) from supply chain managers, operational managers and general managers. The purpose of collecting data in two rounds was to reduce common-method bias. Likert scale (1–5) was used in the questionnaire. Smart PLS 3 and SPSS 23 were used for the data analysis purpose.Findings: SCA was found to directly and positively affect OP. LP also positively affected OP. In addition, CA fully mediated the relationship between SCA, LP and OP.Practical implications: This study encourages the managers of manufacturing firms to adapt LP and their supply chains (SCs) to become agile and leverage the advantages of their implementation to improve their OP and succeed in the market.Originality/value: This study is one of the first to investigate the effect of SCA and LP on OP. Furthermore, the first study examines CA's mediating impact on the relationship between SCA, LP and OP.

The emergency departments (EDs) of tertiary hospitals (THs) are significantly overcrowded, while primary healthcare centers (PHCCs) are underutilized. Therefore, the Chinese government is attempting to reduce outpatient flow from THs and establish a referral system between the EDs of THs and PHCCs. First, in this article, we propose a mobile-based outpatient healthcare service delivery framework (MOHSDF), which can help divert the flow of outpatients toward the PHCCs before they visit the EDs of THs. Second, this article examines that the MOHSDF-specific characteristics, including performance attributes (e.g., effort expectancy and performance expectancy), communicative power (e.g., interactivity), and perceived government support, are the stimuli that can help build an organism (e.g., initial trust and internal satisfaction). This organism will result in the behavioral intent to prescribe treatment using the MOHSDF. The data are collected from 292 TH physicians in Hefei, China, and analyze using Smart-PLS. All the proposed hypotheses are accepted, excluding two direct and one mediating relationship. These insignificant direct relationships are between performance attributes and internal satisfaction. Moreover, internal satisfaction does not mediate the relationship between effort expectancy and behavioral intent to prescribe treatment using the MOHSDF. This article has significant implications for researchers, government healthcare authorities, policymakers, and m-health service providers.

The literature of energy forecast is moving from the traditional statistical prediction methods to today’s more intelligent machine learning algorithms due to continuous development in these prediction methods. But, in the literature of forecasting, the superiority of the machine learning algorithms is scarce concerning their relative performance compared to traditional statistical forecasting methods. The purpose of this study is to evaluate the performance of statistical and machine learning methods for forecasting CO2 emissions from energy, transport and manufacturing sectors of Pakistan and provide sector wise CO2 mitigation strategies. We projected the CO2 emissions sector wise using selected methods till 2030 and also gave suggestions for the policymakers to make better decisions. The results show the increasing trend in CO2 emission from energy, manufacturing and transport sectors in Pakistan which causes the significant problems if the government fail to respond sector related issues appropriately.