How do academies use customer experience (CX) leadership theory? How do they employ and measure it? A content analysis and brief survey was employed to examine a University of Phoenix research center website iteration to define the customer personas of dissertation chairs, its largest customers who utilize the Center for Leadership Studies and Educational Research center for guidance to formulate research studies geared toward publication. These customers (known as affiliates) were also measured whether they believed that implicit promises made were kept, a necessity of purposeful CX strategy. The results revealed that the personas who needed the most publication support did agree that promises made were kept. The study recognized a default CX website version so that enhancements could help transport affiliate customers along the CX continuum based on what they believed was important to their professional development, more interactions such as collaborative webpages.

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How do academies use customer experience (CX) leadership theory? How do they employ and measure it? How is emotional branding related to customer experience? No matter how rigorous higher education programs become, understanding the student and faculty customer experience can have many positive effects. Staff and faculty need to understand how to create meaningful student interactions leading to loyalty that can foster networking opportunities for student success throughout the school's prospective, current, and alumni network. A content analysis and brief survey was employed to examine a University of Phoenix research center Website iteration to define the customer personas of dissertation chairs, its largest customers who utilize the Center for Leadership Studies and Educational Research (CLSER) center for guidance to formulate research studies geared towards publication. These customers (known as affiliates) also were measured whether they believed that implicit promises made were kept, a necessity of purposeful CX strategy and that signifies the degree of emotional connection.

The Journal of Leadership Studies has enjoyed sharing quality scholarship with our readership for 11 years now. Throughout its history, the journal has published high quality and peer‐reviewed manuscripts in the Feature section, the Symposium, and the Media Review. However, over the years the journal has received numerous quality manuscripts that did not quite fit in the sections mentioned above. Last year the journal’s leadership team met to discuss the addition of a new section to the journal. Thus, it is with great excitement that we introduce the new section entitled, Leadership Perspectives, and the editor of the section, Dr. Erik Bean. —Mark Ludorf

Ever since information was first operationalized by library science into consumer formats, media bias has been studied from the purview of information gatekeepers who decide what, how, and when to publish based on story importance and factors like circulation. This concept did not include individuals or entities outside of the journalism discipline. With the advent of the internet and a number of social media networks that soon followed, individuals could more effectively release information without waiting for gatekeepers, thus shaping the public’s perception regardless of the topic. Scholars offered a theoretical framework for shaping the public’s opinion and still other scholars focused on how information could be slanted or partisan. However, these seminal approaches did not operationalize the term information bias in terms of the overall partiality of major sources themselves. Information evaluation tests such as the Currency, Relevance, Authority, Accuracy, and Purpose (CRAAP) and Stop, Investigate, Find, Trace (SIFT) that have been discussed as tools to assess information for bias fall short on the very first step of what to inspect and how to sort. With a gap in the literature sorting through the types of biases can be daunting and confusing. The purpose of this paper is to propose one initial method as the first step to sort information bias regardless of its form, analog or digital, into seven prominent sources each with their own inherent but larger impartiality tied to it. The sources of all information bias to be discussed in alphabetical order are: 1) academic, 2) forprofit, 3) government, 4) hidden agenda, 5) individuals, 6) nonprofit, and 7) watchdog groups.