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There are thousands of reviews constantly being posted for popular products on e-commerce websites. The number of reviews is rapidly increasing which creates information overload problem. To solve this problem, many websites introduced a feedback mechanism to vote for a review (helpful or not). The attracted votes reflect the review helpfulness. This study addresses the review helpfulness prediction problem and investigated the impact of review, reviewer and product features. Multiple helpfulness prediction models are built using multivariate adaptive regression, classification and regression tree, random forest, neural network and deep neural network approaches using two real-life Amazon product review datasets. Deep neural network-based review helpfulness prediction model has outperformed. The results demonstrate that review-type characteristics are most effective indicators as compared to reviewer and product type. In addition, hybrid combination (review, reviewer and product) of proposed features demonstrates the best performance. The influence of product type (search and experience) on review helpfulness is also examined, and reviews of search goods show strong relationship to review helpfulness. Our findings suggest that polarity of review title, sentiment and polarity of review text and cosine similarity between review text and product title effectively contribute to the helpfulness of users’ reviews. Reviewer production time and reviewer active since features are also strong predictors of review helpfulness. Our findings will enable consumers to write useful reviews that will help retailers to manage their websites intelligently by assisting online users in making purchase decisions.

Cancer is developed by rapid, uncontrolled, and abnormal cell proliferation and one of the leading causes of deaths worldwide in human beings. For the remedial measures of preventing different types of cancers, one of the research domains that have gained substantial importance in medical science is the development of new metallo-drugs and their investigations as potential anticancer drug agents by using various analytical techniques. Since metal-based complexes show weak absorption bands, electrochemical methods are considered more feasible and preferable over spectroscopic methods for easy characterization. Due to closer resemblance of electrochemical and biological processes, cyclic voltammetry among different electrochemical methods is considered the most versatile for the study of in-vitro metal-based drug–DNA interactions in terms of changes in the redox activities. Current potential data of a metal complex leads to determine binding kinetics in terms of binding constant and binding site size that involve determining the binding mode of drug with DNA, i.e., electrostatic interactions, intercalation, or minor-major groove binding. Binding parameters and modes of interactions, further, help to develop the mechanism of action of drug with the DNA. In this review, we emphasize on cyclic voltammetric DNA binding studies on some metal complexes that have been carried out in the last three decades for the investigation of their anticancer potentials.

A bulk of literature has examined the asymmetric impact of renewable energy consumption on CO 2 emissions by using the advanced econometric approach. While the asymmetric role of renewable energy production in the CO 2 equation is largely unknown, our present study quantifies the asymmetric relationship between renewable energy production, natural resources, economic progress, and CO 2 emission for Pakistan by using the NARDL approach. It is found that positive change in renewable energy production has a positive effect on CO 2 emissions, while a negative change in renewable energy production has a negative effect on CO 2 emissions in the long run. Furthermore, a positive and negative change in natural resources contributes negatively to CO 2 emissions in the long run. The results reveal that a positive change in economic progress significantly increases CO 2 emissions in the long run. Based on findings, Pakistan’s government should encourage local and international investors to increase their investment in the production of renewable energy by reducing environmental degradation.

Despite an escalated gravity of interest in exploring factors that shape university students' social entrepreneurial intentions, there are significant gaps in our understanding of this phenomenon. The current study examines the boundary effects of religiosity and spiritual intelligence to predict university students' social entrepreneurial intentions. The authors collected the data from university students in their final years in multiple waves and employed SmartPLS (v 4.0) for data analysis. Our findings indicate that religiosity can affect social entrepreneurial intentions through two paths: intrinsic motivation mediates the association between intrinsic religiosity and social entrepreneurial intentions, and extrinsic motivation mediates the relationship between extrinsic religiosity and social entrepreneurial intentions. Further, spiritual intelligence moderates the relationship between intrinsic religiosity and social entrepreneurial intentions, mediated by intrinsic motivation such that at high levels of spiritual intelligence the association is more potent and vice versa. This is the first study that examines the boundary conditions of social entrepreneurial intentions of university students by employing the lens of religiosity and spirituality. The paper presents substantial theoretical and practical implications.

PurposeThe purpose of this study is to investigate the impact of post content, post media and post scheduling strategies on online engagement on Twitter in context of micro celebrities in Pakistan.Design/methodology/approachFor this research, micro celebrities of Pakistan have been defined as the target population. Secondary data consisting of 464 tweets from walls of six micro celebrities belonging to both genders and diverse set of socio-political fields was collected. Tweedie estimation analysis was run to accept or reject the hypotheses. Mean values with standard deviations were utilized to analyze the different engagement patterns of dichotomous variables (content type, content language, mentions, hashtags, text, images, links, videos, hour of the day and day of the week) on online engagement.FindingsContent type, content language, content length, hashtags, mentions, images, links, videos, hour of the day and day of the week have been found to have a significant relationship with online engagement on Twitter.Research limitations/implicationsFirst, the study has been conducted in context of micro celebrities on Twitter. It did not include influencers on other social media networks. Second, study considered only quantitative aspects of engagement based on secondary data ignoring qualitative aspects of phenomenon due to time and methodology constraints. Third, study did not include link clicks as a measure of engagement as clicks data is not publicly available on the posts.Practical implicationsThe study contributes significantly to find out valuable “micro celebrity” strategies in Pakistan. The study suggests micro celebrities to tweet soft content in Urdu language along with relevant hashtags and mentions to get higher engagement on their tweets. Further, tweets should contain maximum number of characters. Micro celebrities should not insert images, links and videos in their tweets as these media types result in lower engagement on Twitter. Micro celebrities should tweet at low hours and weekends.Social implicationsAs this study focuses on investigation of better engagement practices for micro celebrities, it will help general public to express themselves more effectively through social media.Originality/valueFirst, this is the first study that investigates the online engagement model for micro celebrities. Second, the online engagement model designed in this study has yet not been investigated to best of our knowledge. The theoretical model combines multiple engagement factors discussed in previous studies conducted on Facebook, YouTube, Instagram and Twitter.

Extant research on workplace ostracism has investigated a victimization perspective to understand ostracism at the cost of examining the perpetrator-centric view of ostracism. The current study draws on the self-categorization theory and the social exchange theory to investigate the harmful effects of workplace romance in cultivating workplace ostracism from the perpetrator's perspective to combat concerns for victim blaming. The study further proposes that workplace ostracism triggered by workplace romance provokes knowledge sabotage. Besides, the study investigates the moderating role of parochial altruism in the underlying linkages. The study utilizes a multisource, time-lagged research design to collect data from employees working in service sector organizations in Pakistan. The study analyzes 343 responses using SmartPLS (v 4.0). The findings of this study reveal that workplace romance elicits workplace ostracism, which, in turn, fosters knowledge sabotage. In addition, the study finds that parochial altruism strengthens the associations between (a) workplace romance and workplace ostracism and (b) workplace romance and knowledge sabotage, mediated by workplace ostracism such that the associations are more potent at higher levels of parochial altruism and vice versa. This is the first study that examines workplace romance as the perpetrator-centric antecedent of workplace ostracism, and parochial altruism exaggerates outgroup ostracism and knowledge sabotage.

Reports about presence and toxicity of Cd2+ in different chemical industrial effluents prompted the researchers to explore some economical, rapid, sensitive, and accurate methods for its determination and removal from aqueous systems. In continuation of series of investigations, adsorption of Cd2+ onto the stem of Saccharum arundinaceum is proposed in the present work. Optimization of parameters affecting sorption potential of Cd2+ including pH, contact time, temperature, sorbent dose, and concentration of sorbate was carried out to determine best suited conditions for maximum removal of sorbate. To understand the nature of sorption process, linear and nonlinear forms of five sorption isotherms including Freundlich and Langmuir models were employed. Feasibility and viability of sorption process were evaluated by calculating kinetics and thermodynamics of the process, while error analysis suggested best fitted sorption model on sorption data. Thermodynamic studies demonstrated exothermic nature of reaction, while kinetic studies suggested pseudo-second order of reaction.

Service quality has been a topic of extensive inquiry for decades that has emerged now in form of self-service technology (SST) which has profound effects on the way customers interact with firms to create positive service outcomes i.e. customer satisfaction, loyalty, and behavioral Intentions. Therefore, the main objective of this study is to examine that how the technology based Services i.e. SSTs impact the customer satisfaction, loyalty, and Behavioral Intentions in service sector of Pakistan. The data have been collected from the 238 SST’s users through the online survey. In order to test the model, Structural Equation Modeling is applied by using the LISREL program. The results of this study reveal positive and significant relationship between SSTs service quality, loyalty, and behavioral Intentions directly and indirectly via customer satisfaction. These results provide insights for the service sector of the Pakistan to invest in the new technology in order to enhance the consumer experience, satisfaction, loyalty, and Intentions.

Nowadays, constructing a narrow bandgap nanocomposite photocatalyst that can degrade contamination under visible light is critical but challenging. In this report, poly (acrylic acid) microgel (PAA) based nanocomposites (Ag@CuO/PAA NC) were constructed via free radical solution polymerization by varying the concentration of silver-doped copper oxide nanoparticles (Ag@CuO NPs) from 0 to 12%. As prepared Ag@CuO and Ag@CuO/PAA were characterized by X‐ray diffraction spectroscopy, scanning electron microscopy, transmission electron microscopy, energy dispersive X-ray and X-ray photoelectron spectroscopy. The size of Ag@CuO NPs was found to be 30–50 nm. The photocatalytic activity of CuO is increased by Ag doping and C3 NPs show the best photodegradation of methylene blue (MB). Then, 4% of Ag@CuO nanoparticles were incorporated into PAA microgel, the resultant nanocomposite showed a drastic increase in photodegradation of MB. Ag@CuO/PAA NC completely degraded dye in only 30 min which was degraded up to 65% in 60 min. by Ag@CuO NPs. The successful combination of PAA with Ag@CuO boosts the photocatalytic activity because microgel provides a large surface to adsorb pollutants. Ag@CuO/PAA NC reused successfully for photodegradation of dye due to the recycling ability of microgels. This study gives a good insight into planning a significant visible‐light‐driven photocatalyst for environmental remediation.

Patients with Parkinson’s disease (PD) present with an advanced form of age-related muscle loss or sarcopenia. However, the search for a biomarker to accurately predict muscle loss in PD remains elusive. We evaluated the biomarkers of neuromuscular junction (NMJ) stability, including c-terminal agrin fragment-22 (CAF22), brain-derived neurotrophic factor (BDNF), and glial cell line-derived neurotrophic factor (GDNF) as predictors of muscle wasting and physical capacity in PD. Male, 63—78 years patients of PD, were investigated for physical capacity, handgrip strength (HGS), and circulating biomarkers at the diagnosis and follow-up during rehabilitation 6 months apart. Patients with PD presented with elevated CAF22 and reduced BDNF and GDNF levels, which were partially restored to normal levels with rehabilitation. All three biomarkers showed significant dynamic associations with HGS and indexes of sarcopenia. Logistic regression revealed that the combination of biomarkers levels into a cumulative risk score enhanced the diagnostic accuracy of sarcopenia. In brief, measurements of plasma BDNF, GDNF, and CAF22 may be helpful in timely diagnosis and/or evaluation of sarcopenia.