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A great deal of work has been done in recent decades examining the impact of emotional intelligence (EQ) and cultural intelligence (CQ) on extra-role job performance. However, little of this research has used both EQ and CQ constructs together in the same analytical model. In addition, the available literature in is virtually silent with regards to the relationships of these constructs in Russia, a country with a significant multicultural environment. This study examines these relationships, using a sample of 189 supervisory and non-supervisory subjects in Russia. The results suggest that motivational CQ may be seen as an antecedent of CQ and EQ, both of which significantly impact extra-role job performance. EQ and CQ factors were also found to mediate the relationship between motivational CQ and extra-role job performance. In addition, necessary condition analysis was completed adding to model insights. These results extend our knowledge to a Russian sample, and not only support the Thomas et al. (2015) approach to CQ, but also suggest significant implications for current EQ-CQ model theories.

Nursing home administrators are caught in a "perfect storm" of rapidly increasing health care costs, decreasing reimbursement, and increasing competition. This paper documents how these pressures create cascading misalignments resulting in compromises of the quality of comfort care. These problems are inevitable, given the increase in unfunded mandates, where performance evaluation is decoupled from actual performance. Ambient technologies are explored as a means of tracking actual care versus reported care. Independent quality of care tracking and documentation of ambient data, coupled with best practice research and rewards, are explored to promote quality care as a marketing advantage. Implications are discussed.

Manufacturing is playing a significant role in its re-shoring into America. Companies are grappling with ways to obtain that competitive advantage by distinguishing themselves through their intellectual capabilities, process improvements, technology, people, shop floor management and information flows. The purpose of this paper is to describe the effort at Farmingdale State College to educate our students in understanding Production Management and Master Production Schedule (MPS). We are trying to prepare students for entry into the workforce. By using a Real world ERP tool in the classroom while complimenting this learning with touring local manufacturers who use this tool and having production control experts in our classrooms. [1] The opportunity presents itself for these students to visit real world manufacturers using the same tool these students use in the classroom, the Infor Visual ERP. Each semester students go to a local manufacturer to see how the product is made and the ERP system is used to make it. Each semester a subject matter expert, SME, in manufacturing comes into the class and talks about how they use their ERP to perform their functional responsibilities. Students go into these companies and sit down with these Production Manufacturing and IT SME’s to see how they use the modules in their ERP system from estimating, Production Management, MPS to delivery and payment. From the manufacturing window to the Master Schedule Window students learn from these companies SME’s just how they perform their functions, how they use this tool. Then that is replicated this in the classroom lab assignments for students to better understand Production Management, scheduling and work order integrity. They identify the desired schedule (forecast) and populate a Master Production Schedule. They create a BOM with work orders adding operations and material. The Production Management/Control is the function of directing or regulating the movement of goods through the entire manufacturing cycle from the requisitioning of raw material to the delivery of finished products. (APICS Dictionary 13th Edition) The Master Production Schedule is often a major component of Sales and Operations Management. The purpose of the Master Schedule is to translate the Sales forecast into a Production Plan that must be executed by the organization. The Master Schedule is the demand side of the equation and must represent the customers’ needs. In this way the Master Scheduler can give manufacturing its best chance for success. Master Production Schedule (MPS): The MPS should be closely aligned with the Sales Forecast. Students enter a Sales Forecast into the system similar to what they have seen at the companies. Students see how it is the liaison between the Sales Forecast and a production work order. Its function is to translate the Sales Forecast into a viable production schedule that supports the customer requirements, while taking into account shop floor constraints. The MPS must support the Sales Forecast and customer demand. Students learn the importance of this in their lab assignments. They identify and enter shop floor resources. Students learn that companies should never chase supply they should chase demand and manage supply. Supply can mean, purchased parts, and externally produced parts, internally made items, internal machine or labor constraints. The student’s comprehension of this topic, concept and knowledge is significantly enhanced due to the tours to local manufacturers and the individuals that come into the class to discuss these functional areas and the processes they perform in their organizations.

This paper, Change Requires Change: will relate that bridging the gap between education: of what we teach and training: of what industry looks for in prepared skills for students, needs to be relevant to today’s situations. We need to re-evaluate traditional industry academic partnerships which have been relatively successful including; internships, work-study programs, curriculum advisory boards, guest lectures and capstone courses, to identify gaps and opportunities for what is needed to support our future. Do we want to continue with the status-quo or enhance education? Should we be cognizant of emerging trends? What could be the implications on changing academic-industry partnerships? How can we improve? This paper proposes several new approaches to academics and industry practitioner’s towards greater successful collaborations towards student preparation.

The relationships between Education/Training and Information Technologies at Farmingdale State College are accelerating, with the newest partnership involving the new CloudSuite Industrial Product, CSI, form Infor, USA, Inc. In this arrangement we have a dedicated Intern, Sayem Shahrier, a student at Farmingdale State College who assists with use of the CSI product in the classroom. According to this Intern, the implementation of the Infor CSI tool has been a great real life and hands-on experience. Students in the Education Alliance Program finally received a taste of how an ERP system works. Whether a company is big or small, it should be capable to manage and keep track of all their products and customers in real time. From local companies throughout Long Island all the way up to global mass markets have utilized the Infor CSI tool and have taken customer data to further maximize not only on profits but improve on operational excellence, customer intimacy, and the manufacturing process. Using this innovative tool and this partnership synergy, makes our students best prepared for industry entry and successes. This partnership brings together researchers and practitioners to support the bridging process between Education/Training and Information Technologies.

As companies continue to put data and business analytics as their top priority, universities will need to supply students with the appropriate skill sets that meet this demand and offer future opportunities to their graduates. Although business analytics is a new field, many of the required competencies stem from already established areas such as Information/Computer Technology or Information Systems. Using a sample of 225 randomly selected AACSB accredited business schools this study examined the new developments in Business Analytics undergraduate academic programs, and determined the amount of overlap between the Business Analytics and the Information/Computer Technology degree programs. Our findings reveal that approximately 36 percent of the Business Analytics programs overlap with the Information/Computer Technology programs. In addition, the top three required courses in most Business Analytics programs include a Database course, predictive analytics course, and Introduction to Business Analytics. This research provides valuable insight for schools that haven't adopted a Business Analytics degree yet or are looking to improve their existing curriculum. In addition, colleges and universities can now utilize the appropriate Information Systems courses and include them as important foundation and part of their Business Analytics programs.

This study investigates reasons behind high employee turnover of IT professionals through the value placed on intrinsic, extrinsic, leisure, and social rewards. It addresses two questions; what are the most preferred work values by IT professionals, and how do they differ from other professions. Data was collected via a survey using subjects from Amazon Mechanical Turk. A sample of 380 respondents was used. Key findings reveal that intrinsic rewards were the most important work values for IT professionals followed by leisure, extrinsic, and social rewards. Furthermore, when compared to the preferences of non-IT professionals, the levels of preferences for intrinsic rewards were statistically significantly higher for IT professionals than all other professions. This study provides valuable insight regarding key motivating factors of IT professionals and offers practical implications for practitioners relating to job characteristics that are most likely to reduce IT turnover and rethink their rewards systems.

Faculty teaching data analytics at undergraduate level are often faced with the tension created by student under-preparedness, the demands of the course, and time constraints. How do faculty close this gap? In this paper, we propose the use of flow diagramming as an accessible method for interpreting regression analyses, in ways that are time efficient and not alienating to the student. Our study shows that the use of such flow diagrams has a positive effect on student understanding without additional remedial instruction. Time saved can be directed at core learning objectives of the analytics.

Logistic regression (LoR) is a foundational supervised machine learning algorithm and yet, unlike linear regression, appears rarely taught early on, where analogy and proximity to linear regression would be an advantage. A random sample of 50 syllabi from undergraduate business statistics courses shows only two percent of the courses included LoR. Conceivable reasons for this dearth of LoR content is likely related to topic complexity, time constraints, and varying degrees of tool ease of use and support. We propose that these constraints can be countered by: [1] introducing logistic regression early, [2] informed tool selection prioritizing ease of use with comprehensive output, and [3] using/developing innovative, accessible, and easy to understand concept learning aids. This approach would leverage the proximity to linear regression and probability readily embed distributed practice for student understanding of a foundational technique.