Many companies invest considerable resources in developing Business Analytics (BA) capabilities to improve their performance. BA can affect performance in many different ways. This paper analyses how BA capabilities affect firms' agility through information quality and innovative capability. Furthermore, it studies the moderating role of environmental turbulence, both technological and in the market. The proposed model was tested using statistical data from 154 firms with two respondents (CEO and CIO) from each firm. The data were analysed using Partial Least Squares (PLS)/Structured Equation Modelling (SEM). Our results indicate that BA capabilities strongly impact a firm's agility through an increase in information quality and innovative capability. We also discuss that both market and technological turbulence moderate the influence of firms' agility on firms' performance. 1. Introduction Business analytics (BA) are overhauling the way firms are generating and using data (Ramanathan, Philpott, Duan, & Cao, 2017). They attracted increasing attention from both academics and practitioners for their high operational and strategic potential across various industries, including financial services, insurance, retail, healthcare and manufacturing (Dubey, Gunasekaran, Childe, Wamba, & Papadopoulos, 2016; Fosso Wamba, Ngai, Riggins, & Akter, 2017). BA can be defined as a holistic approach to manage, process and analyse data, not only to create actionable insights (adapted from Fosso Wamba, Akter, Edwards, Chopin, and Gnanzou, (2015)) but also to enable organisations to predict changes based on market requirements and respond to them quickly (Işık, Jones, & Sidorova, 2013). BA systems involve the use of capabilities and technologies to collect, transform, analyse and interpret data to support decision-making (Santiago Rivera & Shanks, 2015). BA are known as 'competitive differentiators (Jeble et al., 2018),' and both professional press and academic research consistently demonstrate a positive relationship between BA and or

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Purpose Market orientation (MO) (intelligence generation, intelligence dissemination and responsiveness) is known as one of the key concepts in marketing literature. Although prior research has widely focused on the meaning and application of MO, few attempts have been made to explore how market-oriented firms lead to innovation and market performance and what factors actually moderate this relationship. To fill this gap, the present study aims to explore the relationship between MO, innovation and market performance. This study also attempts to examine the intervening role of IT infrastructure, business analytics (BA) capabilities and market turbulence in the proposed model. Design/methodology/approach In this study, a questionnaire-based survey was undertaken to test the proposed hypotheses. To verify the proposed theoretical model, partial least squares (PLS)/structured equation modeling (SEM) was performed with 114 valid survey data. Findings Despite prior studies which postulated innovation performance as the final outcome of MO (Han et al., 1998; Song et al., 2015), this study focused on innovation performance as a mediating outcome which finally leads to market performance. The statistical results approve the putative relationship which means managers would be able to realize the paramount role of innovation as an integral part of achieving higher market performance. In addition, no support was found for the relationship between intelligence generation and responsiveness. This finding shows that not all obtained information can help managers in the decision-making process. Originality/value This study aims to enrich literature by developing a conceptual model to test the link between MO, innovation and market performance. The value of this study is to investigate the roles of flexible IT infrastructure, BA capabilities and market turbulence as the potential moderators in the proposed model. The results advance the understanding of the influence of BA capabilities on the link between intelligence dissemination and responsiveness. Findings also show innovation performance as remarkable and deemed valuable capability, leading to higher performance in marketing-related activities, particularly in highly turbulent markets.

Purpose-Decision support (DS), as a traditional management concept, have had a remarkable role in competitiveness or survival of organizations and nowadays, business intelligence (BI), as a brand modern impression, has various contributions in supporting decision-making process. Although, a variety of benefits are expected to arise from BI functions, researches, and models that determining the effect of BI functions on the decisional and organizational benefits are rare. The purpose of this paper is to study the relationship between BI functions, DS benefits, and organizational benefits in context of decision environment. Design/methodology/approach-This research conducts a quantitative survey-based study to represent the relationship between BI capabilities, decision support benefits, and organizational benefits in context of decision environment. On this basis, the partial least squares (PLS) technique employs a sample of 228 firms from different industries located in Middle-East countries. Findings-The findings confirm the existence of meaningful relationship between BI functions, DS benefits, and organizational benefits by supporting 15 out of 16 main hypotheses. Essentially, this research provides an insightful understanding about which capabilities of BI have strongest impact on the outcome benefits. Originality/value-The results can provide effective and useful insights for investors and business owners to utilize more appropriate BI tools and functions to reach more idealistic organizational advantages. Also it enables managers to better understand the application of BI functions in the process of achieving the specified managerial support benefits.

The objective of this study was to determine the effectiveness of treatment of nonhealing heel ulcers and gangrene and to define those variables that are associated with success. Methods: A multi-institutional review was undertaken at four university or universityaffiliated hospitals of all patients with wounds of the heel and arterial insufficiency, which was defined as absent pedal pulses and a decreased ankle/brachial index (ABI). Risk factors, hemodynamic parameters, and arteriographic findings were statistically analyzed to determine their effect on wound healing. Life-table analysis was used to assess graft patency and wound healing. Results: Ninety-one patients (57 men, 34 women) were treated for heel wounds that did not heal for 1 to 12 months (62% of nonhealing wounds, 3 months or longer). The mean preoperative ABI was 0.51, and 31% of wounds were infected. Of the patients, 55% had impaired renal function (Cr > 1.5), with 24% undergoing dialysis, 70% had diabetes, and 64% smoked cigarettes. Treatment was topical wound care for all patients and operative wound débridement in 50%. Infrainguinal bypass was performed for 81 patients, 4 had inflow procedures, 3 had superficial femoral artery percutaneous transluminal angioplasty, and 3 had primary below-knee amputation. Postoperatively, 85% of patients had in-line flow to the foot with at least a single patent vessel, 66% had a pedal pulse, and the mean ABI improved by 0.40, to 0.91. Follow-up ranged from 1 to 60 months (mean, 21 months), and 77 patients (85%) are currently alive. In 66 patients (73%), the wounds healed-all within 6 months (mean, 3 months). For 14 (16%) the wounds had not healed, and 11 patients (11%) underwent below-knee amputation. By life-table analysis, limb salvage was 86% at 3 years. During follow-up, 75 infrainguinal bypasses (91%) remained patent (3 secondarily) and 6 occluded, with primary assisted patency of 87% at 3 years. All wounds in patients with occluded grafts failed to heal. Variables found to be statistically significant in predicting healing included normal renal function (95% healed vs 55% nonhealed, P < .002), a palpable pedal pulse (85% healed vs 42%, P < .0015), a patent posterior tibial artery past the ankle (86% healed vs 57%, P < .02), and the number of patent tibial arteries after bypass to the ankle (P < .0001). Neither the ABI nor the presence of infection (defined as positive tissue cultures or the presence of osteomyelitis), diabetes, or other cardiovascular risk factors influenced the outcome. Conclusions: Complete wound healing of ischemic heel ulcers or gangrene may require up to 6 months, and short-term graft patency is of minimal benefit. Successful arterial reconstruction, especially a patent posterior tibial artery after bypass, is effective in treating most heel ulcers or gangrene. Patients with impaired renal function are at increased risk for failure of treatment, but their wounds may successfully heal and they should not be denied revascularization procedures.

Decision support and business intelligence systems have been increasingly adopted in organizations, while understanding the nature of affecting factors on such adoption decisions need receiving much academic interest. This article attempts to provide an in-depth analysis toward understanding the critical factors which affect the decision to adopt business intelligence (BI) in the context of banking and financial industry. In this regard, it examines a conceptual model that shows the impacts of different technological, organizational, and environmental factors in the decision to adopt BI by a firm. Structural equation modeling (SEM) was used for data analysis and test the relevant hypothesis. The results of this article which are derived from theoretical discussion of hypothesizes show that from nine hypothesized relationships—perceived tangible and intangible benefits, firm size, organizational readiness, strategy, industry competition and competitors absorptive capacity—affect BIS adoption in the surveyed cases.

Aim/Purpose: This research aims to present a brand-new approach for student performance prediction using the Learning Fuzzy Cognitive Map (LFCM) approach. Background: Predicting student academic performance has long been an important research topic in many academic disciplines. Different mathematical models have been employed to predict student performance. Although the available sets of common prediction approaches, such as Artificial Neural Networks (ANN) and regression, work well with large datasets, they face challenges dealing with small sample sizes, limiting their practical applications in real practices. Methodology: Six distinct categories of performance antecedents are adopted here as course characteristics, LMS characteristics, student characteristics, student engagement, student support, and institutional factors, along with measurement items within each category. Furthermore, we assessed the student’s overall performance using three items of student satisfaction score, knowledge construction level, and student GPA. We have collected longitudinal data from 30 postgraduates in four subsequent semesters and analyzed data using the Learning Fuzzy Cognitive Map (LFCM) technique. Contribution: This research proposes a brand new approach, Learning Fuzzy Cognitive Map (LFCM), to predict student performance. Using this approach, we identified the most influential determinants of student performance, such as student engagement. Besides, this research depicts a model of interrelations among the student performance determinants. Findings: The results suggest that the model reasonably predicts the incoming sequence when there is a limited sample size. The results also reveal that students’ total online time and the regularity of learning interval in LMS have the largest effect on overall performance. The student engagement category also has the highest direct effect on student’s overall performance. Recommendations for Practitioners: Academic institutions can use the results and approach developed in this paper to identify students’ performance antecedents, predict the performance, and establish action plans to resolve the shortcomings in the long term. Instructors can adjust their learning methods based on the feedback from students in the short run on the operational level. Recommendation for Researchers: Researchers can use the proposed approach in this research to deal with the problems in other domains, such as using LMS for organizational/institutional education. Besides, they can focus on specific dimensions of the proposed model, such as exploring ways to boost student engagement in the learning process. Impact on Society: Our results revealed that students are at the center of the learning process. The degree to which they are dedicated to learning is the most crucial determinant of the learning outcome. Therefore, learners should consider this finding in order the gain value from the learning process. Future Research: As a potential for future works, the proposed approach could be used in other contexts to test its applicability. Future studies could also improve the performance level of the proposed LFMC model by tuning the model’s elements.

Tube hydroforming is a process that uses internal pressure and axial feeding simultaneously to form a tube into a desired shape. The internal pressure provides the stress required to yield the material while axial feeding eases metal flow helping to produce a part without wrinkles and with even wall thickness. Pulsating pressure hydroforming applies loading path with fluctuating pressures. In this study, pulsating pressure hydroforming of T-joint part was examined experimentally. Six process parameters in pulsating pressure loading path were selected. Using Taguchi design of experiments with six parameters and two levels for each parameter, 12 experiments were conducted to study the effects of pulsating pressure parameters on the parts' defects and shape accuracy. Signal-to-noise ratio and analysis of variance were employed to determine the important process parameters affecting the final part in terms of wrinkling, bulge height and wall thickness. Three linear regressions without any interaction between the parameters were extracted for three quality responses and were evaluated through three extra experiments that show the best levels for three responses. The results show reasonable agreement between the experiments and linear regression models.