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The results of human studies evaluating the efficacy of plant Phytosterols on liver function were inconsistent. Therefore, the purpose of this paper is to eliminate these controversies about the Phytosterols consumption on liver serum biochemistry in adult subjects.

The literatures systematically searched throughout PubMed and Scopus databases up to June 2018; it was conducted by using related keywords. Estimates of effect sizes were expressed based on weighted mean difference (WMD) and 95% CI from the random-effects model (erSimonian and Laird method). Heterogeneity across studies was assessed by using I2 index. Eighteen studies reported the effects of Phytosterols (PS) supplementation on liver serum biochemistry.

The current meta-analysis did not show a significant effect on ALT (MD: 0.165 U/L, 95% CI: −1.25, 1.58, p = 0.820), AST (MD: −0.375 IU/Liter, 95% CI: −1.362, 0.612, p = 0.457), ALP (MD: 0.804 cm, 95% CI: −1.757, 3.366, p = 0.538), GGT (MD: 0.431 U/L, 95% CI: −1.803, 2.665, p = 0.706) and LDH (MD: 0.619 U/L, 95% CI: −4.040, 5.277, p = 0.795) following PS consumption.

The authors found that no protective or toxic effects occur after the consumption of Phytosterols on liver enzymes including ALT, AST, ALP, LDH and GGT.

Traditionally, the banking and financial sectors have pioneered adoption of new technologies and business models. One important digital banking model that has proven its efficacy in recent times, is Digital-Only Banking (DOB) where consumers interact with their banks through digital channels only. Having detailed knowledge of what actually happens at the consumer level during the adoption of new digital models and technologies is paramount to the success of these technological initiatives. The present study aims to investigate DOB adoption behavior and possible barriers using a quantitative approach at the consumer level. A conceptual model is developed by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) model, incorporating Trust (TR), Perceived Risk (PR) constructs and cultural moderators of Individualism (IDV) and Uncertainty Avoidance (UA).

For this study, an online survey instrument was created and administered in Iran. The research sample was selected through the application of purposive sampling. Data from 788 respondents were analyzed. The proposed model was tested using Partial Least Square.?.s Structural Equation Modeling (PLS-SEM).

The results show that DOB adoption is positively influenced by Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC), while PR negatively influences DOB adoption intention. Unexpectedly, the results indicate that TR has no significant impact on DOB usage intention. Additionally, this study demonstrates that with individuals having a low level of IDV, the relationship between PE and BI is stronger, and with individuals having a low level of UA, the impact of SI on BI is stronger. It also reveals that the impact of TR on BI is stronger in low individualistic cultures.

DOB providers should enhance support features of their services or provide facilities that make it simpler for users to accomplish online transactions. Here, in order to improve the UI/UX design of their apps, DOB product managers should carefully observe the technical guidelines of the operating systems of digital devices, such as the Human Interface Guidelines (HIG) for iOS and Material You for Android. Additionally, DOB providers should build partnerships with mega online retailers to provide hassle-free and easy to use payment solutions for consumers.

DOB, as a novel and business model, has been investigated in very few studies, especially regarding any which focus on its adoption. To fill this gap, this research investigates DOB adoption through a modified version of the UTAUT model. The findings of this study suggest that future research regarding DOB should consider sources of TR, types of non-adopters, and context. This study, as the first of its kind in DOB literature, also highlights the significant role played by cultural values in customer behavior regarding DOB adoption.

This qualitative study aims to fill gaps in a widely studied and relevant organizational feature: the alignment between information technologies and business strategies.

This research is a qualitative study. The authors used focus groups, content analysis and semantic networks as research approaches to identify the main factors that prevent or foster such alignment.

Results reveal a leading role of innovation, organizational culture, access to information and financial factors that could promote or inhibit alignment and competitiveness.

This research was conducted only in small and medium organizations in Mexico, which represents about 52% of the Mexican Gross Domestic Product (for Mexico as one of the leading trade partners of the USA).

Studies show that data quality (DQ) issues are extremely costly for companies. To address such issues, as a starting point, there is a need to understand what DQ is. In his context, the 1996 paper “Anchoring data quality dimensions ontological foundations” by Wand and Wang has been highly influential on the understanding of DQ. However, the present study demonstrates that some of the assumptions made in their paper can be challenged. On this basis, this study seeks to develop clearer definitions.

The assumptions behind Wand and Wang’s DQ classification are discussed, on which basis three counter-propositions are formulated. These are investigated through a representation theoretical approach involving analyses of deficient mappings between real-world and information system states. On this basis, an intrinsic DQ classification is derived. A case study is conducted to investigate the value of the developed DQ classification.

The representation theoretical analysis and the case study support the three propositions. These give rise to the development of a DQ classification that includes three primary intrinsic DQ dimensions (accuracy, completeness and conciseness), which are associated with six primary value-level DQ deficiencies (inaccuracy, incorrectness, meaninglessness, incompleteness, absence and redundancy). The case study supports the value of extending Wand and Wang’s DQ classification with the three additional data deficiencies.

By improving the conceptual clarity of DQ, this study provides future research with an improved basis for studies and discussions of DQ.

The study advances the understanding of DQ by providing additional clarity.

Numerous data quality (DQ) definitions in the form of sets of DQ dimensions are found in the literature. The great differences across such DQ classifications (DQCs) imply a lack of clarity about what DQ is. For an improved foundation for future research, this paper aims to clarify the ways in which DQCs differ and provide guidelines for dealing with this variance.

A literature review identifies DQCs in conference and journal articles, which are analyzed to reveal the types of differences across these. On this basis, guidelines for future research are developed.

The literature review found 110 unique DQCs in journals and conference articles. The analysis of these articles identified seven distinct types of differences across DQCs. This gave rise to the development of seven guidelines for future DQ research.

By identifying differences across DQCs and providing a set of guidelines, this paper may promote that future research, to a greater extent, will converge around common understandings of DQ.

Awareness of the identified types of differences across DQCs may support managers when planning and conducting DQ improvement projects.

The literature review did not identify articles, which, based on systematic searches, identify and analyze existing DQCs. Thus, this paper provides new knowledge on the variance across DQCs, as well as guidelines for addressing this.