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1 – 10 of over 20000Kojo Kakra Twum and Andrews Agya Yalley
The use of innovative technologies by firm employees is a key factor in ensuring the competitiveness of firms. However, researchers and practitioners have been concerned about the…
Abstract
Purpose
The use of innovative technologies by firm employees is a key factor in ensuring the competitiveness of firms. However, researchers and practitioners have been concerned about the willingness of technology end users to use innovative technologies. This study, therefore, aims to determine the factors affecting the intention to use marketing analytics technology.
Design/methodology/approach
This study surveyed 213 firm employees. The quantitative data collected was analysed using partial least squares structural equation modelling.
Findings
The results reveal that performance expectancy, facilitating conditions, attitudes and perceived trust have a positive and significant effect on intentions to use marketing analytics. Effort expectancy, social influence and personal innovativeness in information technology were found not to predict intentions to use marketing analytics.
Practical implications
This study has practical implications for firms seeking to enhance the use of marketing analytics technology in developing countries.
Originality/value
This study contributes to the use of UTAUT, perceived trust, personal innovativeness and user attitude in predicting the intentions to use marketing analytics technology.
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Employee and workforce insights are the greatest competitive advantage for organizations dealing with the disruption and uncertainty driving dramatic changes in today’s workplace…
Abstract
Purpose
Employee and workforce insights are the greatest competitive advantage for organizations dealing with the disruption and uncertainty driving dramatic changes in today’s workplace. Embedded in this is the growing expectation of the human resource (HR) function to understand how workforce analytics informs the business and fuels success. This paper aims to explore how the HR function can achieve this.
Design/methodology/approach
The evolution of the “Future of HR” and how it is moving from “descriptive and diagnostic” to “prescriptive and predictive.”
Findings
According to KPMG’s 2019 Future of HR survey: 37 per cent of respondents feel “very confident” about HR’s actual ability to transform and move them forward via key capabilities such as analytics and artificial intelligence (AI). Over the next year or two, 60 per cent say they plan to invest in predictive analytics. Among those who have invested in AI to date, 88 per cent call the investment worthwhile, with analytics listed as a main priority (33 per cent). Despite data’s remarkable ability to deliver news insights and enhance decision-making, 20 per cent of HR believe analytics will be a primary HR initiative for them over the next one to two years, and only 12 per cent cite analytics as a top management concern.
Research limitations/implications
Taking a page from meeting customer needs, innovative technologies such as AI and the cloud, data analytics can give an organization the potential to gather infinitely greater amounts of information about customers.
Practical implications
Today’s workforce analytics focuses mostly on what happened and why. For instance, you might have tools for identifying areas of high turnover and diagnosing the reasons. But thanks to advancements in technology and data analytics capabilities, HR is better-positioned to be the predictive engine required for the organization’s success.
Social implications
There has never been a better time for HR to create greater strategic value, as the potential for meaningful workforce insights and analytics comes within reach. Even advancements in cloud-based systems for human capital management are coming packaged with analytics and visualization capabilities, enabling HR leaders to integrate people data with other data sources, such as customer relationship management, for a full view of the business.
Originality/value
This paper will be of value to HR leaders and practitioners who wish to use predictive analytics and emerging technology to drive performance improvement and gain the insights about their workforces.
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Tobias Klatt, Marten Schlaefke and Klaus Moeller
Over the past few years, developments in business analytics have provided strategic planners with promising instruments for dealing with turbulent environments. This study aims to…
Abstract
Purpose
Over the past few years, developments in business analytics have provided strategic planners with promising instruments for dealing with turbulent environments. This study aims to reveal whether or not the application of business analytics in strategic planning contributes to better company performance, and to formulate recommendations on how to integrate business analytics in companies' performance management systems.
Design/methodology/approach
Based on a survey conducted with 89 respondents from high‐technology firms, a group comparison between firms with strong performance and those with weak performance reveals significant differences between the two groups' strategic planning processes and application of business analytics.
Findings
The empirical survey's results show that better‐performing companies are characterized by a more sophisticated analytical planning process. Lower‐performing firms acknowledge this competitive advantage. Based on these findings, the authors develop recommendations on how to integrate business analytics in performance management contexts.
Research limitations
The empirical study's results are limited to high‐technology industries in the cultural setting of Germany.
Practical implications
The empirical results emphasize the competitive advantage gained by applying business analytics. The recommendations concerning analytical performance management should help managers to sensibly integrate the analytical toolbox in performance management contexts.
Originality/value
This paper combines insights on the best usage of business analytics from the perspective of strategic planning experts, with recommendations for the integration of business analytics into the performance management framework from an academic perspective.
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Anthony Marshall, Stefan Mueck and Rebecca Shockley
To understand how the most successful organizations use big data and analytics innovate, researchers studied 341 respondents’ usage of big data and analytics tools for innovation…
Abstract
Purpose
To understand how the most successful organizations use big data and analytics innovate, researchers studied 341 respondents’ usage of big data and analytics tools for innovation.
Design/methodology/approach
Researchers asked about innovation goals, barriers to innovation, metrics used to measure innovation outcomes, treatment and types of innovation projects and the role of big data and analytics in innovation processes.
Findings
Three distinct groups emerged: Leaders, Strivers and Strugglers. Leaders are markedly different as a group: they innovate using big data and analytics within a structured approach, and they focus in particular on collaboration.
Research limitations/implications
Respondents were from the 2014 IBM Innovation Survey. We conducted cluster analysis with 81 variables. The three cluster solution was determined deploying latent class analysis (LCA), a family of techniques based around clustering and data reduction for segmentation projects. It uses a number of underlying statistical models to capture differences between observed data or stimuli in the form of discrete (unordered) population segments; group segments; ordered factors (segments with an underlying numeric order); continuous factors; or mixtures of the above.
Practical implications
Leaders don’t just embrace analytics and actionable insights; they take them to the next level, integrating analytics and insights with innovation. Leaders follow three basic strategies that center on data, skills and tools and culture: promote excellent data quality and accessibility; make analytics and innovation a part of every role; build a quantitative innovation culture.
Originality/value
The research found that leaders leverage big data and analytics more effectively over a wider range of organizational processes and functions. They are significantly better at leveraging big data and analytics throughout the innovation process – from conceiving new ideas to creating new business models and developing new products and services.
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Previous studies focus on the direct effects of marketing analytics on entrepreneurial performance, but few explore the underlying mechanisms. Drawing on affordance theory, this…
Abstract
Purpose
Previous studies focus on the direct effects of marketing analytics on entrepreneurial performance, but few explore the underlying mechanisms. Drawing on affordance theory, this study explores pathways through new product innovation (NPI) for the effects of marketing analytics on business performance. NPI is a market-based innovation concept comprising customer- and competitor-driven NPD and incremental innovation.
Design/methodology/approach
Using survey data collected from UK-based entrepreneurial firms operating in the IT and telecoms industries, we apply confirmatory factor analysis and a sequential structural equation model to test the mediating role of NPI in the effect of marketing analytics on market performance and financial performance.
Findings
The results show that marketing analytics enhances business performance through competitor-driven but not customer-driven NPD. Although using marketing analytics to generate customer knowledge for existing product innovation may enhance market performance, this positive effect becomes negative when competitor-driven NPD is undertaken to improve existing product innovation.
Originality/value
This study makes significant contributions to the innovation and NPD literature. It delves deeper into the existing view on the positive contributions of customer engagement to business value creation, revealing the significance of competitor knowledge to enhance business performance through marketing analytics, particularly in the context of IT and telecoms entrepreneurial firms.
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B.S. Patil and M.R. Suji Raga Priya
The purpose of this study is to target utilizing Human resources (HRs) data analytics that may enhance strategic business, but little study has examined how it affects components…
Abstract
Purpose
The purpose of this study is to target utilizing Human resources (HRs) data analytics that may enhance strategic business, but little study has examined how it affects components. Data analytics, HRM and strategic business require empirical investigations and how to over come HR data analytics implementation issues.
Design/methodology/approach
A semi-systematic methodology for its evaluation allows for a more complete examination of the literature that emerges theoretical framework and a structured survey questionnaire for quantitative data collection from IT sector personnel. SPSS analyses data.
Findings
Future research is essential for organisations to exploit HR data analytics’ performance-enhancing potential. Data analytics should complement human judgment, not replace it. This paper details these transitions, the important contributions to theory and practice and future research.
Research limitations/implications
Data analytics has grown rapidly and might make HRM practices faster, more efficient and data-driven. HR data analytics may improve strategic business. HR data analytics on employee retention, engagement and organisational success is insufficient. HR data analytics may boost performance, but there is limited proof. The authors do not know how HRM data analytics influences firms and employees.
Originality/value
Data analytics offers HRM new opportunities, along with technical and ethical challenges. This study makes a significant contribution to HR data analytics, evidence-based practice and strategic business literature. In addition to estimating turnover risk, identifying engagement factors and planning interventions to increase retention and engagement, HR data analytics can also estimate the risk of employee attrition.
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Priyanka Thakral, Dheeraj Sharma and Koustab Ghosh
Organizations widely adopt knowledge management (KM) to develop and promote technologies and improve business effectiveness. Analytics can aid in KM, further augmenting company…
Abstract
Purpose
Organizations widely adopt knowledge management (KM) to develop and promote technologies and improve business effectiveness. Analytics can aid in KM, further augmenting company performance and decision-making. There has been significant research in the domain of analytics in KM in the past decade. Therefore, this paper aims to examine the current body of literature on the adoption of analytics in KM by offering prominent themes and laying out a research path for future research endeavors in the field of KM analytics.
Design/methodology/approach
A comprehensive analysis was conducted on a collection of 123 articles sourced from the Scopus database. The research has used a Latent Dirichlet Allocation methodology for topic modeling and content analysis to discover prominent themes in the literature.
Findings
The KM analytics literature is categorized into three clusters of research – KM analytics for optimizing business processes, KM analytics in the industrial context and KM analytics and social media.
Originality/value
Systematizing the literature on KM and analytics has received very minimal attention. The KM analytics view has been examined using complementary topic modeling techniques, including machine-based algorithms, to enable a more reliable, systematic, thorough and objective mapping of this developing field of research.
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For over a decade now, various stakeholders in accounting education have called for the integration of technology competencies in the accounting curriculum (Association to Advance…
Abstract
For over a decade now, various stakeholders in accounting education have called for the integration of technology competencies in the accounting curriculum (Association to Advance Collegiate Schools of Business (AACSB), 2013, 2018; Accounting Education Change Commission (AECC), 1990; American Institute of Certified Public Accountant (AICPA), 1996; Behn et al., 2012; Lawson et al., 2014; PricewaterhouseCoopers (PWC), 2013). In addition to stakeholder expectations, the inclusion of data analytics as a key area in both the business and accounting accreditation standards of the AACSB signals the urgent need for accounting programs to incorporate data analytics into their accounting curricula. This paper examines the extent of the integration of data analytics in the curricula of accounting programs with separate accounting AACSB accreditation. The paper also identifies possible barriers to integrating data analytics into the accounting curriculum. The results of this study indicate that of the 177 AACSB-accredited accounting programs, 79 (44.6%) offer data analytics courses at either the undergraduate or graduate level or as a special track. The results also indicate that 41 (23.16%) offer data analytics courses in their undergraduate curriculum, 61 (35.88%) at the graduate level, and 12 (6.80%) offer specialized tracks for accounting data analytics. Taken together, the findings indicate an encouraging trend, albeit slow, toward the integration of data analytics into the accounting curriculum.
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Sean Mackney and Robin Shields
This chapter examines the application of learning analytics techniques within higher education – learning analytics – and its application in supporting “student success.” Learning…
Abstract
This chapter examines the application of learning analytics techniques within higher education – learning analytics – and its application in supporting “student success.” Learning analytics focuses on the practice of using data about students to inform interventions aimed at improving outcomes (e.g., retention, graduation, and learning outcomes), and it is a rapidly growing area of educational practice within higher education institutions (HEIs). This growth is spurring a number of commercial developments, with many companies offering “analytics solutions” to universities across the world. We review the origins of learning analytics and identify drives for its growth. We then discuss some possible implications for this growth, which focus on the ethics of data collection, use and sharing.
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William D. Brink and M. Dale Stoel
The purpose of this study is to identify the specific skills and abilities within the broad category of data analytics that current business professionals believe are most…
Abstract
The purpose of this study is to identify the specific skills and abilities within the broad category of data analytics that current business professionals believe are most important for accounting graduates. Data analytics knowledge is clearly important, but this category is broad. Therefore, this study identifies the specific skills and abilities that are most important for accounting graduates so that faculty can create classroom materials most beneficial for the future accounting graduates. In 2013, the Association to Advance Collegiate Schools of Business developed new standards for accounting programs, including standard A7, related to information technology and analytics. The intent of the standard clearly focuses on increasing the level of technology and analytics studied within the accounting curriculum. However, the specific details and methods for achieving the intent of A7 remain an open question. This chapter uses prior research focused on business analytics education to identify potential analytic skills, tools, techniques, and management issues of concern within the accounting profession. A survey of 342 accounting professionals identifies suggested areas of analytic competencies for accounting graduates. Specifically, the authors find preferences for skills related to data interpretation and communication over any individual technical skills or statistical knowledge. These skills suggest a role for accountants as intermediaries who may need to translate analytic activities into business language. Post hoc, the authors examine the survey results for differences based on respondent characteristics. Interestingly, female respondents report lower beliefs about the importance of analytic skills. The authors also find some differences when examining different demographics within the respondents.